# 10xSearch - Full Content > Curated full text of reviewed, indexable pages on 10xsearch.com. Curated index: https://10xsearch.com/llms.txt # 10xSearch | AI Visibility for Luxury Real Estate > 10xSearch is an AEO and real-estate SEO agency that helps luxury real estate professionals earn measurable visibility in Google and AI answers. Source: https://10xsearch.com/ Last reviewed: August 13, 2026 ## What does 10xSearch do? 10xSearch benchmarks a brand across AI answer engines, Google Search, and Google Maps, then builds the technical, entity, content, and evidence layer needed to compete for discovery, recommendation, and citation. Recognition, recommendation, citation, ranking position, sentiment, traffic, leads, and revenue are tracked as separate measures. ## Who is the service for? The flagship specialization is luxury real estate: agents, teams, and brokerages in markets where buyers research expertise, reputation, and local authority before making contact. 10xSearch also works with other high-trust service brands when the category, evidence, and conversion path are a fit. ## What can a buyer inspect before a sales call? - [Fixed 50-prompt AI visibility baseline](https://10xsearch.net/audit/growth-audit-10xsearch-com-2a901b54): 50 national commercial prompts, 100 raw Gemini and Claude captures, cited URLs, run metadata, and SHA-256 hashes. The August 13 baseline recorded zero 10xSearch recommendations and is published as a starting point, not a win. - [AI visibility audit methodology](https://10xsearch.com/ai-visibility-audit-methodology/): prompt groups, verdict rules, tracked fields, raw-evidence contract, scope, and limitations. - [10xSearch results and case studies](https://10xsearch.com/results/): dated customer evidence, current-versus-historical treatment, methods, limitations, and source links. - [2026 Luxury Presence Visibility Index](https://10xsearch.com/luxury-presence-june-2026/): a purposive study of 102 luxury real estate portfolio sites, including methodology, limitations, and an aggregate CSV. - [Client portfolio](https://10xsearch.com/clients/): live customer sites that a buyer can inspect directly. ## How is AI visibility measured? The durable prompt panel uses 50 fixed commercial prompts divided into national category discovery, luxury real-estate specialization, vendor comparisons, and problem-and-solution research. Each run retains prompt text, engine, model, date, scope or location, raw answer, cited URLs, recommendation verdict, citation source, position where supported, sentiment where supported, and a content hash. Results are not blended across incompatible engines or time windows. ## What does an engagement cost? Current public plans are Standard at $2,500 per month, Super Affiliate at $3,500 per month, and Founder at $10,000 upfront followed by $2,500 per month. The [pricing page](https://10xsearch.com/pricing/) explains scope and fit. Buyers should compare accountable outputs, baseline definitions, evidence access, technical ownership, contract terms, and what a vendor refuses to guarantee. ## What are the limitations? No agency controls whether an answer engine cites a particular page on a particular date. Answers vary by engine, model, interface, account context, date, and location. A case result documents what happened for the named client in the stated window; it does not guarantee the same outcome for another business. The current public fixed-panel baseline is zero recommendations, so no post-rebuild visibility lift should be claimed until a comparable rerun records it. ## Who publishes 10xSearch research? 10xSearch Inc. is a Colorado corporation with its legal office in Centennial, Colorado. The company operates across the Denver metro area and serves clients throughout the United States. Rick Janson, JD, MBA is the founder and named author of the flagship methodology and authority pages. - [Leadership and authorship](https://10xsearch.com/about-10xsearch-answer-engine-optimization-experts/) - [Official Colorado entity record](https://data.colorado.gov/resource/4ykn-tg5h.json?entityid=20261025290) - [OpenGovCO entity record mirror](https://opengovco.com/business/20261025290) - a crawlable directory mirror of the state data, not an editorial endorsement - Contact: Rick@10xSearch.com # About Rick Janson, Founder of 10xSearch URL: https://10xsearch.com/about-10xsearch-answer-engine-optimization-experts/ Summary: Rick Janson, JD, MBA, founded 10xSearch in the Denver metro area to make great businesses impossible to miss in the AI search era. Architect of the 40-point Perfect Page Formula. Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call Home / About About the Founder Rick Janson, JD, MBA. Founder of 10xSearch. Author of two books on AI search optimization. Author of the 40-point Perfect Page Formula that grades every page 10xSearch ships. Bio Twenty years of watching the industry struggle with the same problem. Rick Janson founded 10xSearch in the Denver metro area after twenty years of watching service brands lose visibility to platforms they did not control. He built the company on a single thesis: when a customer asks ChatGPT, Perplexity, Gemini, or Google a question, the website is the only thing in the room representing the brand. It either earns the citation or it does not. Rick is a JD and an MBA. He is the author of two books on the discipline: AI Search Optimization and AI Search Optimization for Real Estate . The 40-point Perfect Page Formula used on every 10xSearch client site is drawn directly from the methodology in those books. He leads the engineering team that ships two engineered pages per business day for every client, runs the daily content engine that monitors AI surfaces in real time, and personally reviews the technical foundation of every site 10xSearch builds. Independent references These sources corroborate Rick's connection to 10xSearch, subject expertise, or industry recognition. They are not independent audits of product performance or customer outcomes. - Colorado business entity record: https://data.colorado.gov/resource/4ykn-tg5h.json?entityid=20261025290 - The Colorado Department of State open-data record identifies 10xSearch Inc. as a good-standing Colorado corporation, entity ID 20261025290, formed January 7, 2026. - NAR NXT 2026 speaker profile: https://narnxt.realtor/speaker/rick-janson/ - The official NAR NXT profile identifies Rick as the founder of 10xSearch.com and lists his session on how agents get found in AI Search, Google Search, and Maps. - NAR: Find Your GEO to Land Your Next Referral From AI: https://www.nar.realtor/news/real-estate-news/technology/find-your-geo-to-land-your-next-referral-from-ai - The National Association of REALTORS article identifies Rick as the creator of 10X Search, links to the company, and describes its page-evaluation method. - NAR Tech & Innovation: AI and the homebuying journey: https://tech.realtor/2026/07/14/ai-becomes-early-step-in-homebuying-journey/ - The NAR-owned technology publication identifies Rick as the creator of 10X Search and connects the company to answer engine optimization. It republishes NAR reporting and is not a separate performance audit. - Greater Albuquerque Association of REALTORS republication: https://www.gaar.com/blog/article/ai-becomes-early-step-in-homebuying-journey - The association's July 2026 republication identifies Rick as the creator of 10X Search and connects the company to answer engine optimization. It adds another association-owned crawl surface for the same NAR reporting, not a separate performance audit. - Inman Golden I Club finalist announcement: https://www.inman.com/2026/07/21/inman-announces-the-2026-inman-golden-i-club-finalists/ - Inman's editorial team lists 10xSearch as a 2026 Top Luxury Tech/Tool finalist. This verifies industry recognition, not a performance result. - McKissock AI MasterTrack recap: https://www.mckissock.com/blog/real-estate/how-real-estate-agents-use-ai-webinar/ - McKissock identifies Rick as the founder of 10Xsearch.com and Colibri Real Estate School's resident AI expert in its March 2026 editorial recap. Credentials - Juris Doctor (JD) - Master of Business Administration (MBA) Books - AI Search Optimization - AI Search Optimization for Real Estate Based In - Denver metro, Colorado - Legal office: Centennial, Colorado - Remote-first team - Serving the United States Connect - linkedin.com/in/rickjanson - facebook.com/rickjanson - Rick@10xSearch.com See the system Rick built. 40 engineered assets per month. Every page graded against the 40-point Perfect Page Formula. Every signal mapped to the 10 Pillars of Search. Explore AEO and AI search Schedule a 20-minute call Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # Our Mission: Get Found on AI, Google, and Maps URL: https://10xsearch.com/mission/ Summary: Our mission: make great businesses impossible to miss in the AI search era. Custom search infrastructure across SEO, AEO, and GEO for Google, Maps, and AI engines. Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call Home / Mission Our Mission Make great businesses impossible to miss in the AI search era. Get found on AI, Google, and Maps. That is the promise, and that is the work. We engineer custom search infrastructure across SEO, AEO, and GEO so the brands we partner with become the obvious answer when a real customer is choosing. What We Believe Four operating principles that decide every page we ship. The website is the brand When a customer asks ChatGPT, Perplexity, Gemini, or Google a question, your website is the only thing in the room representing you. It either earns the citation or it does not. That is now the most important moment of truth in marketing. AI search is not a future trend, it is the current default Google AI Overviews, ChatGPT, Perplexity, and Gemini have already changed how high-intent buyers research. Most sites were not engineered for that world. Closing that gap is the entire job. Visibility belongs to the engineered, not the loud Search visibility on Google, Google Maps, and AI engines rewards structure: clean schema, entity clarity, semantic hierarchy, fast pages, and consistent publishing. We engineer the structure first and let the volume compound from there. Honest data only We do not invent metrics. Every number we publish is verifiable in a public surface like Google search, Google Maps, an AI assistant response, or a Lighthouse audit. If we cannot show it, we do not say it. “Our mission is to make great businesses impossible to miss in the AI search era. We engineer custom search infrastructure so the work, not the noise, is what gets found.” Rick Janson, JD, MBA · Founder, 10xSearch See the system that delivers the mission. 40 engineered assets per month. Every page graded against the 40-point Perfect Page Formula. Every signal mapped to the 10 Pillars of Search. Explore AEO and AI search Compare 10xSearch Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # 10xSearch vs Agent Image vs Agent Fire | 2026 URL: https://10xsearch.com/compare/ Summary: Get found on AI, Google, and Maps. Compare 10xSearch to Agent Image and Agent Fire on pricing, monthly output, technical SEO, and ownership. [Skip to content](#main-content) [10×Search](/) [Why us](/#different)[The Engine](/#engine)[Results](/#results)[The Formula](/#formula)[Visitor ID](/#visitor-id)[AEO](/aeo-ai-search-optimization)[Schedule](/schedule)[Blog](/blog)[Compare](/compare) Case Studies [ Mountain Rose Realty From a failing 33 to a perfect 100 ](/case-studies/mountain-rose-realty)[ The Kink Team Visibility growth and AI inclusion in 45 days ](/case-studies/10xsearch-gets-you-found-online) [Selected Work](/clients/) [FAQ](/#faq)[Free Report](/#report)[Book Call](/schedule/#book-calendar) [Home](/)/Comparison Real Estate Comparison · Built for the AI search era ## Get found on AI, Google, and Maps. How does 10xSearch compare to Agent Image and Agent Fire? By Rick Janson, JD, MBA·Founder, 10xSearch·Published May 3, 2026; updated August 13, 2026 Real estate marketing has shifted. Traditional agencies operate on a Scarcity Model, charging high retainers for limited output (1 to 4 blog posts per month). **10xSearch.com** operates on an **Engineered Velocity Model**, deploying enterprise-level search infrastructure (40 engineered assets per month) to dominate local search results. We don't sell websites or effort. We build **media companies that you own 100 percent.** See how the math stacks up below. The Math ## How does 10xSearch.com compare? Every figure below is drawn from publicly listed pricing pages and the official feature documentation of each provider as of May 2026. No invented benchmarks. Methodology is grounded in [Google Search Central guidance](https://developers.google.com/search/docs/fundamentals/seo-starter-guide) and the [Core Web Vitals reference](https://web.dev/articles/vitals). Metric / Feature Agent Image The "Upsell" model Agent Fire The "Show Pony" The 10x Protocol The Growth Engine Financial reality Monthly cost $2,000 to $3,000+ Variable tiers. Pinnacle starts at $2k+. $199 to $400+ / mo Base + Plus Add-on ($169) + IDX Fees. From $2,500 / month Three public plans with published buying guidance. One-time setup fee $4,000+ upfront Design ($2k) + Migration ($1k) + Setup ($1k). $2,500 upfront Explicitly listed on checkout page. $0, waived Included in month one. We invest immediately. Hidden fees $1,000 per page Upsell for every additional SEO page. Pass-through fees Extra monthly charges for IDX ($30) and Data ($15). None Unlimited pages and data connections included. Design and agility Design model Heavy markup Expensive templates and custom builds. 4 to 8 weeks to deploy. Heavy markup Expensive templates and custom builds. 4 to 8 weeks to deploy. No markup Start with a template. Hire your own designer hourly. Change anytime. Volume and velocity Monthly output 3 articles, capped Strict cap on Pinnacle tier (~36 per year). 4 blogs plus homework 4 posts plus manual Magic Rewrite. Manual pages. 40 assets per month 2 engineered assets every weekday (~480 per year). Economic value ~$666 per asset High cost for low volume. ~$100+ per asset Diluted value. Requires manual labor. ~$62 per asset Enterprise volume at freelance rates. Technical health Local SEO setup Generic or upsold Often a paid add-on or hourly fee. $250 add-on Capped at 20 pages. Basic meta and title tags only. Included GA4, Search Console, and schema integrated on day one for all assets. Ranking scope Brand defense Looking to be found for own name only. Your name only Admits: goal is not to rank for broad keywords. Selected market coverage Entity work and authority pages mapped to agreed neighborhoods and topics. Audit and repair Broken elements Address missing on homepage. Manual fix required. Failed indexing Luxury showcase site not indexed anywhere properly. Measured remediation Detected issues are prioritized, fixed, and verified. Timing depends on access and scope. Content pillars Market reports Limited or none Basic integration or separate upsell. Standard reports Area Guides and Home Values included. Comprehensive suite Market reports plus buyer and seller guides for all neighborhoods. AI readiness (AEO) Not ready Content not formatted for ChatGPT or Gemini citations. DIY tool Spark AI tool requires manual prompting. Native AEO Q&A formatting engineered for AI citations. Operations and risk Exit strategy Not easy Painful transitions. Potential loss of SEO history. Lose the tools Stop paying, lose Content Magic and Spark AI. Total ownership You own all 480+ assets, design, and code forever. Financial reality Monthly cost Agent Image $2,000 to $3,000+ Variable tiers. Pinnacle starts at $2k+. Agent Fire $199 to $400+ / mo Base + Plus Add-on ($169) + IDX Fees. 10xSearch From $2,500 / month Three public plans with published buying guidance. One-time setup fee Agent Image $4,000+ upfront Design ($2k) + Migration ($1k) + Setup ($1k). Agent Fire $2,500 upfront Explicitly listed on checkout page. 10xSearch $0, waived Included in month one. We invest immediately. Hidden fees Agent Image $1,000 per page Upsell for every additional SEO page. Agent Fire Pass-through fees Extra monthly charges for IDX ($30) and Data ($15). 10xSearch None Unlimited pages and data connections included. Design and agility Design model Agent Image Heavy markup Expensive templates and custom builds. 4 to 8 weeks to deploy. Agent Fire Heavy markup Expensive templates and custom builds. 4 to 8 weeks to deploy. 10xSearch No markup Start with a template. Hire your own designer hourly. Change anytime. Volume and velocity Monthly output Agent Image 3 articles, capped Strict cap on Pinnacle tier (~36 per year). Agent Fire 4 blogs plus homework 4 posts plus manual Magic Rewrite. Manual pages. 10xSearch 40 assets per month 2 engineered assets every weekday (~480 per year). Economic value Agent Image ~$666 per asset High cost for low volume. Agent Fire ~$100+ per asset Diluted value. Requires manual labor. 10xSearch ~$62 per asset Enterprise volume at freelance rates. Technical health Local SEO setup Agent Image Generic or upsold Often a paid add-on or hourly fee. Agent Fire $250 add-on Capped at 20 pages. Basic meta and title tags only. 10xSearch Included GA4, Search Console, and schema integrated on day one for all assets. Ranking scope Agent Image Brand defense Looking to be found for own name only. Agent Fire Your name only Admits: goal is not to rank for broad keywords. 10xSearch Selected market coverage Entity work and authority pages mapped to agreed neighborhoods and topics. Audit and repair Agent Image Broken elements Address missing on homepage. Manual fix required. Agent Fire Failed indexing Luxury showcase site not indexed anywhere properly. 10xSearch Measured remediation Detected issues are prioritized, fixed, and verified. Timing depends on access and scope. Content pillars Market reports Agent Image Limited or none Basic integration or separate upsell. Agent Fire Standard reports Area Guides and Home Values included. 10xSearch Comprehensive suite Market reports plus buyer and seller guides for all neighborhoods. AI readiness (AEO) Agent Image Not ready Content not formatted for ChatGPT or Gemini citations. Agent Fire DIY tool Spark AI tool requires manual prompting. 10xSearch Native AEO Q&A formatting engineered for AI citations. Operations and risk Exit strategy Agent Image Not easy Painful transitions. Potential loss of SEO history. Agent Fire Lose the tools Stop paying, lose Content Magic and Spark AI. 10xSearch Total ownership You own all 480+ assets, design, and code forever. The Difference ## What makes 10xSearch.com different? 01 ### Volume: the "3-article cap" vs. 40 assets Competitors like **Agent Image** explicitly cap their premium tiers at just 3 articles per month. At that pace, it would take you 10 years to build the authority 10xSearch delivers in 9 months. **Agent Fire** publishes 4 blog posts a month for a fraction of the Agent Image cost, but still slower than us. We publish **2 engineered assets every weekday.** 02 ### The "DIY" capped trap vs. fully managed **Agent Fire** offers tools like Magic Rewrite and gives you access to create pages on your own, but this shifts the labor back to you. We offer this capability as well, but think about it. It's a gym membership model. You pay for the equipment, but you still have to do the workout. While we absolutely give you free rein to create your own content, **10xSearch.com** is at its core a done-for-you engine. We research, write, engineer, and publish. You just close deals. 03 ### The "show ponies" are broken When our competitors shared their best sites with us, we analyzed them. They are all broken. **70,845 technical errors across 50 sites.** That is not a fluke. That is not a one-off. That is a fundamental flaw in the way our competitors design and deploy your sites, regardless of our enormous velocity and volume delta (we provide ten times the content every month). 04 ### The audit data behind the "show ponies" verdict A comprehensive technical SEO audit was conducted across a diverse cohort of real estate websites showcased by Agent Fire on their sales page. This is a focused study independent of the 50 URLs analyzed in our 30-page whitepaper. The aggregated findings below report issue counts across the cohort, broken down by category, with range, average, and median for each. Category Range Low High Average Median Content issues 198 220 418 318 317 **Why it matters:** Signals thin or duplicate content to Google (IDX bloat). Prevents ranking for local authority topics. Page headers (H1/H2) 179 165 344 231 222 **Why it matters:** Broken H1/H2 structure confuses search engines about the page topic. Kills ranking for luxury keywords. Social meta (Twitter/OG) 186 161 347 260 248 **Why it matters:** Links look broken when shared on Facebook or X. Drastically lowers click-through rates from social. Image issues 146 108 254 158 150 **Why it matters:** Missing alt text creates ADA liability. Uncompressed images slow load times. Broken links 229 69 298 162 154 **Why it matters:** Dead ends for users. Signals to Google that the site is abandoned. Schema (structured data) 71 12 83 46 43 **Why it matters:** Missed opportunity for rich snippets (star ratings, prices) in search results. Aggregate findings across 16 Agent Fire showcase sites. Numbers represent issue counts, not page percentages. Understanding 10xSearch ## The structural advantages. Pillar 05 ### What does a 10xSearch engagement cost? Current public plans are Standard at $2,500 per month, Super Affiliate at $3,500 per month, and Founder at $10,000 upfront followed by $2,500 per month. The plans differ in the authority, entity, and founder-strategy work surrounding the same monthly visibility program. The recurring plans have no separate setup fee. Buyers should compare accountable outputs, baseline definitions, evidence access, technical ownership, contract terms, and what a vendor refuses to guarantee before treating the monthly price as a like-for-like comparison. Pillar 06 ### Do I own the content and the website if I leave? Owning your content is crucial in the digital marketing landscape, particularly in real estate. With 10xSearch, clients retain full ownership of all assets created, ensuring they have complete control over their marketing materials and can use them as needed without restrictions. This contrasts sharply with competitors who often retain rights to the content, limiting clients' flexibility. By possessing all 480+ assets generated annually, clients can adapt their strategies, repurpose content, or even switch service providers without losing valuable resources. Pillar 07 ### Why does technical SEO decide who ranks in real estate? Technical SEO is foundational for ensuring that real estate websites perform well in search engine rankings. 10xSearch integrates advanced technical SEO practices, such as schema markup and site speed optimization, to enhance visibility and user experience. Many competitors overlook these critical elements, leading to issues like slow load times and poor indexing. By addressing technical SEO comprehensively, 10xSearch not only improves search rankings but also helps potential clients find your listings more easily, ultimately driving more leads and conversions. Pillar 08 ### What kind of results do 10xSearch clients actually see? 10xSearch publishes outcomes only when a buyer can review the supporting evidence. The results page separates live client work, technical case studies, third-party disclosures, and the fixed-panel AI visibility baseline instead of combining them into one success number. The current public record does not establish a universal traffic, lead, or revenue lift. Buyers should review the dates, methodology, limitations, and source links for each result, then ask how their own market and starting baseline change the likely outcome. The Framework ## The 10 Pillars of 10xSearch. To dominate the modern digital landscape, we must move beyond simple error checking and focus on the structural integrity of the domain itself. The following framework outlines the 10 Pillars of Search, designed to analyze the critical technical infrastructure required for visibility in both traditional search engines and AI-driven answer engines. By evaluating performance across these core dimensions, we identify the specific foundational weaknesses preventing brands from achieving maximum organic reach and authority. The Pillar What it does Why it is necessary PILLAR 01 Headline structure H1 to H6 semantic hierarchy Organizes content into a logical outline (Title → Main Point → Sub-point). AI readability. Robots scan structure, not paragraphs. Headers tell Google and AI exactly how topics relate. PILLAR 02 Image titles Keyword-rich filenames Renaming generic IMG\_1024.jpg to specific denver-luxury-homes.jpg. Visual indexing. Google Images is a massive search engine. Filenames are the number one ranking factor there. PILLAR 03 Image alt text Descriptive metadata Embedded text explaining the image content to blind users and search crawlers. Accessibility and context. Required for ADA compliance and critical for ranking in Google Lens. PILLAR 04 Data tables HTML structured data Converting lists or stats into coded HTML tables instead of plain text. Winning position zero. Google pulls tables for featured snippets at the top of results. Plain text rarely wins. PILLAR 05 PAA optimization People Also Ask sections Specific Q&A formatting designed to match user queries exactly. Voice and AI search. Siri, Alexa, and ChatGPT cite direct answers. This format feeds them the citation. PILLAR 06 Meta descriptions CTR-optimized copy Psychological summaries designed to trigger clicks, not just stuff keywords. Click-through rate. Rankings fail if nobody clicks. Higher CTR signals relevance, boosting rank further. PILLAR 07 Schema type Article and BlogPosting code Explicit coding that defines the content type to the search engine. Machine understanding. Tells Google this is a news article, enabling rich snippets in news feeds. PILLAR 08 Entity injection Knowledge graph linking Advanced code connecting your brand to concepts (e.g., luxury real estate). Brand authority. Moves you from a website to a known entity in Google's database. PILLAR 09 Internal linking Siloed authority flow Strategic links from blog posts to high-value money pages. Ranking power. Distributes SEO juice from high-volume blogs to lead-capture pages. PILLAR 10 Authorship Verified author bios Linking every article to a real human with credentials, not Admin. E-E-A-T (trust). Google penalizes anonymous content. Verified authorship proves trust. Quick Answers ## Common questions. ### How does 10xSearch compare to Agent Image? The primary difference is the operating model. Agent Image sells design-led real estate websites. 10xSearch sells an ongoing visibility program built around 40 engineered assets per month. Current 10xSearch plans start at $2,500 per month, and buyers should compare the exact scope, technical ownership, evidence access, contract terms, and setup charges quoted for their engagement. ### How is 10xSearch different from Agent Fire? Agent Fire sells a DIY model. They provide tools (like Spark AI or Magic Rewrite) that require you to log in, prompt the AI, and manage the content yourself. If you get busy, your marketing stops. 10xSearch is a done-for-you infrastructure. We research, engineer, and publish 40 assets for you every month. There is no software to learn and no homework to do. ### Are there any setup fees or hidden costs? 10xSearch publishes three plans: Standard at $2,500 per month, Super Affiliate at $3,500 per month, and Founder at $10,000 upfront followed by $2,500 per month. The recurring plans have no separate setup fee. The pricing page explains the current scope and fit of each plan. Founder plan ## Choose the scope that fits the work. Review the current Founder plan, its $10,000 upfront price, and the $2,500 monthly phase. Scope, buying guidance, and limitations are published before the call. [Book a strategy call](/schedule/#book-calendar) [Get the free report](/#report) [![10xSearch.com](/images/10xsearch-logo.webp)](/) Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado [Rick@10xSearch.com](mailto:Rick@10xSearch.com) © 2026 10xSearch Last updated August 13, 2026 ### Capabilities - [Daily content engine](/#engine) - [Perfect Page Formula](/#formula) - [Visitor identification](/#visitor-id) - [AI receptionist](/#visitor-id) - [AEO & AI search](/aeo-ai-search-optimization) - [Comparison](/#compare) - [FAQ](/#faq) - [Sitemap](/sitemap.xml) ### Work - [Client portfolio](/clients/) - [Mountain Rose Realty case study](/case-studies/mountain-rose-realty/) - [The Kink Team case study](/case-studies/10xsearch-gets-you-found-online/) - [Comparison](/compare/) - [10xSearch vs Qnary](/qnary/) - [Blog](/blog/) - [Our mission](/mission/) - [About Rick Janson](/about-10xsearch-answer-engine-optimization-experts/) - [About](/about-10xsearch-answer-engine-optimization-experts/) - [Schedule](/schedule/) - [Privacy Policy](/privacy-policy/) - [Terms of Service](/terms-of-service/) - [Sitemap](/sitemap.xml) # Rapid Search Visibility Growth and AI Inclusion in 45 Days URL: https://10xsearch.com/case-studies/10xsearch-gets-you-found-online/ Summary: How 10xSearch helped The Kink Team close the visibility gap with Zillow, Realtor.com, and HAR and appear in ChatGPT, Perplexity, and Google AI inside 45 days. Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call HOME / CASE STUDIES / KINK TEAM Case study Rapid search visibility growth and AI inclusion in 45 days. How 10xSearch helped The Kink Team , a Woodlands, Texas real estate team, close the visibility gap with national portals and start appearing in AI-generated search results, all inside a single 45-day execution window. By Rick Janson, JD, MBA · Founder, 10xSearch · Published May 3, 2026 - Visibility vs. Zillow, Realtor.com, HAR - Featured in AI-generated responses - Prominent Google Maps placement - 45-day execution window Talk to 10xSearch See the full Agent Image and Agent Fire comparison Executive summary Search is shifting toward AI-generated answers. Visibility now depends on entity trust, topical authority, and local prominence. 01 The shift to answer engines is already here ChatGPT, Gemini, Google AI Overviews, and Perplexity now answer the queries that used to deliver clicks. For a local real estate team, being absent from those answers is the same as being invisible to a buyer who never sees a SERP. 02 Entity trust is the new ranking currency AI assistants pick which sources to cite based on entity consistency, structured data, topical depth, and verifiable authority signals. Local teams who do not actively engineer those signals get filtered out before the answer is generated. 03 This case study documents a measured improvement The findings on this page describe what was observable in public surfaces (Google, Google Maps, AI assistant responses) during the 45-day engagement. Outcomes are documented qualitatively. We do not publish proprietary client metrics. The challenge Three structural problems facing every local real estate team. National portals dominate the SERPs Zillow, Realtor.com, and HAR sit at the top of nearly every local query, backed by enormous link graphs and entity weight. A local team optimizing for the same keywords without a different game plan loses by design. AI assistants only cite trusted sources Generative search engines refuse to cite a source they cannot verify. If your entity is fragmented across the web, your reviews live in five places, and your structured data is inconsistent, the model defaults to the safer national source. Local teams lack the structured signals AI needs Most local real estate sites publish content for humans only. AI engines need explicit structure: schema markup, clear entity associations, topical clusters, and Q-and-A formatting that maps directly to the questions buyers actually ask. The 10xSearch methodology The 10 Pillars of Search. Every engagement runs through the same ten pillars. Each one maps to specific deliverables and to the signals that AI search engines weigh when deciding which sources are worth citing. - 01 Entity clarity and consistency - 02 Search intent modeling - 03 Topical authority development - 04 Local relevance amplification - 05 AI search optimization (AEO) - 06 Structured content engineering - 07 Trust signal reinforcement - 08 Technical crawl optimization - 09 Engagement signal alignment - 10 Ongoing SERP and AI monitoring 45-day execution timeline Executed. Deployed. Optimized. - Days 1 to 15 Executed Entity audit, knowledge graph mapping, and topical model build. We catalogued every signal Google, Bing, and the AI engines were already reading about The Kink Team and identified the gaps between what was published and what was crawlable. - Days 16 to 30 Deployed Structured content rollout: location pages, neighborhood guides, market reports, and entity-aligned bio and review schema published in batches with daily indexing checks. AI-readable Q and A formatting applied across the new content set. - Days 31 to 45 Optimized Active monitoring across SERPs, Google Maps, and AI assistants. We tracked when ChatGPT, Gemini, and Google AI Overviews started returning kinkteam.com as a recommended source for Woodlands real estate queries and tightened the structured data where pickup was slow. Outcomes What changed for The Kink Team in 45 days. Documented qualitatively. We do not publish ranked metrics or proprietary traffic data for client engagements. Each item below is observable in public surfaces (Google search, Google Maps, ChatGPT, Gemini, Google AI Overviews) during the tracking window. - Improved visibility relative to major portals kinkteam.com began appearing alongside national portals (Zillow, Realtor.com, HAR) for relevant local queries during the tracking window, where previously the team was buried beneath them. - Inclusion in AI-generated realtor recommendations When prompted for top-rated real estate teams in The Woodlands, TX, ChatGPT began surfacing The Kink Team in its response set, an entity-trust signal that compounds across other AI assistants. - Prominent Google Maps presence The team moved into a leading position in Google Maps results for the Woodlands real estate cluster, tied to consistent NAP, structured data, and verified entity references across the web. - Stronger digital authority footprint Across the 45-day window the broader signal set, mentions, structured citations, topic coverage, and crawl health, moved consistently in the right direction. We treat this as the substrate that AI engines read, not a single rank number. Proprietary insight The Internal Authority Modeling Framework (IAMF). IAMF is the conceptual model 10xSearch uses internally to prioritize and sequence work across the ten pillars. It weighs entity strength, topical depth, structural readiness, and AI-citation surface area to decide where the next 30 days of engineering effort should land. Note. IAMF is an internal prioritization framework, not a public score. We reference it here so clients understand how decisions get made, not as a measurable benchmark to compare against other vendors. Further reading on the public signals AI engines weigh: the Core Web Vitals reference from web.dev and the Generative Engine Optimization study (arXiv 2311.09735) . Leadership Rick Janson, CEO of 10xSearch. - Based in Denver, Colorado - 20-plus years in real estate - Deep understanding of buyer and seller behavior - Applies industry insight to modern AI search systems Frequently asked About this case study. What kind of business is the case study based on? The Kink Team is a residential real estate team operating in The Woodlands, Texas, competing against national portals like Zillow, Realtor.com, and HAR for local search visibility and against other established local teams for AI-generated recommendations. Why are outcomes described qualitatively instead of with hard numbers? Per client confidentiality, we do not publish proprietary keyword rankings, traffic counts, or conversion data. The outcomes documented here are observable in public surfaces (Google search, Google Maps, AI assistant responses) and were verified during the engagement. What is the 10xSearch 10 Pillars of Search framework? It is the methodology we apply to every engagement: entity clarity, intent modeling, topical authority, local relevance, AI search optimization, structured content engineering, trust signals, technical crawl health, engagement alignment, and continuous monitoring. Each pillar maps to specific deliverables and signals AI search engines weigh when selecting which sources to cite. Can a 45-day window really move the needle in AI search? It can move it meaningfully, but the work is best treated as the launch phase of a longer program. AI assistants update their reference sets continuously, so visibility gained in 45 days needs to be defended and expanded with ongoing structured content and signal hygiene. How does AI search optimization (AEO) differ from traditional SEO? Traditional SEO targets the ranked list of blue links. AEO targets the answer itself. AEO requires content formatted for direct extraction (clear questions, scannable answers, structured data), strong entity associations, and authority signals that AI assistants treat as trustworthy enough to cite. Schedule your AI visibility and brand audit Get found in the answers, not just the rankings. 10xSearch is a done-for-you Answer Engine Optimization (AEO) agency that builds a media company for your brand. Book a 30-minute call and we will walk through what your current AI visibility looks like, and what 45 days of focused work could change. Book a strategy call Get the free report Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # Our Clients: See AI Search Success Stories URL: https://10xsearch.com/clients/ Summary: The full 10xSearch client portfolio: 15 luxury real estate teams and brokerages engineered for AI visibility on Google, Maps, ChatGPT, Perplexity, and Google AI. Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call Our Clients Sites we've engineered. Every brand below runs on infrastructure built by 10xSearch. Click any thumbnail to visit the live site. Back to home The full portfolio 15 live client sites. 15 of 15 Antola Coastal Group Pacific Palisades, CA Visit Assaad Group Southlake, TX Visit Caryl Berenato New York, NY Visit Curated Luxury Homes Atlantic Beach, FL Visit Doug Leibinger Aspen, CO Visit Dream Smith Realty Suwanee, GA Visit Kamee Shrope Realty Salt Lake City, UT Visit KE Team Hawaii Kailua-Kona, HI Visit Lux Exclusives Atlanta, GA Visit Montana Lux Real Estate Stevensville, MT Visit Mountain Rose Realty Telluride, CO Visit Own A Piece of Brooklyn Brooklyn, NY Visit OwnRVA Richmond, VA Visit Rick Janson Denver, CO Visit The Kink Team The Woodlands, TX Visit What's next Add your brand to this list. We engineer custom search infrastructure for one new luxury brand per month. If your competitors are already cited by ChatGPT, Perplexity, and Google AI Overviews and you are not, we should talk. Book a call Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # From a Failing 33 to a Perfect 100 in 48 Hours | Mountain Rose Realty Case Study URL: https://10xsearch.com/case-studies/mountain-rose-realty/ Summary: How Mountain Rose Realty rebuilt its Telluride site in Next.js in 48 hours and moved Google Lighthouse Performance from a failing 33 to a perfect 100. Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call HOME / CASE STUDIES / MOUNTAIN ROSE REALTY Case study From a failing 33 to a perfect 100 in 48 hours. How Mountain Rose Realty , a luxury brokerage in Telluride, Colorado, rebuilt its WordPress site in Next.js and unlocked faster indexing, higher SEO scores, and AI-ready performance, with zero content loss. - Performance score 33 to 100 - Load time 4.6x faster (2.8s to 0.6s) - - - Blocking time 97% reduced - 48-hour turnaround Talk to 10xSearch See the Kink Team AI-visibility case study By Rick Janson, JD, MBA · Founder, 10xSearch · Published May 3, 2026 Executive summary Strong visual brand. Brittle technical core. Mountain Rose Realty, a luxury brokerage in Telluride, Colorado, ran its brand on a legacy WordPress platform with strong visual design and a brittle technical core. Listings loaded slowly, pages shifted on entry, and Google Lighthouse returned a failing 33 of 100 on Performance. Inside 48 hours , 10xSearch delivered a pixel-perfect rebuild in Next.js, deployed it to Vercel, and pulled Performance to a perfect 100 . SEO climbed from 77 to 91, Accessibility from 84 to 91. Zero content loss, same design, same pages, same URLs. A faster, more crawlable site is the foundation every AI visibility and AEO strategy depends on. This is what Pillar 7, Technical Crawl Optimization, looks like when it ships. Performance evidence Google Lighthouse, desktop. Audit captures taken at migration cut-over. Figures reflect the public homepage at time of capture and are reproducible against the live site today using the Google Lighthouse audit tool . The Performance score is calculated from Core Web Vitals measurements. Before WordPress - Performance 33 - Largest Contentful Paint 2.8s - Total Blocking Time 670ms - Cumulative Layout Shift 0.5 - SEO 77 - Accessibility 84 After Next.js on Vercel - Performance 100 - Largest Contentful Paint 0.6s - Total Blocking Time 20ms - Cumulative Layout Shift 0.012 - SEO 91 - Accessibility 91 The challenge What the old site was costing them. - - 2.8 second delay before the first listing was visible On a luxury real estate site where the photography is the product, almost three seconds of empty layout pushed visitors away before a single home appeared on screen. Heavy JavaScript blocked the main thread for ~700ms Plugin sprawl and unminified scripts froze interaction during the most expensive moment of the page lifecycle, the same moment a buyer decides whether to scroll or bounce. Visible layout shifts pushed buttons mid-tap A Cumulative Layout Shift of 0.5 meant the page rearranged itself after first paint, an immediate Core Web Vitals failure and a frustrating mobile experience. A failing 33 of 100 Performance score Search crawlers deprioritize slow pages and AI engines cite fast, stable content first. A 33 is not just a UX problem, it is a visibility problem. 48-hour execution timeline Cloned. Deployed. Verified. - Day 1 Cloned Pixel-perfect rebuild of every page in Next.js. Same typography, same imagery, same URL structure. WebP image optimization and code splitting wired in from the first commit. - Day 2 Deployed Cut over to Vercel with static HTML pre-rendering and a global CDN. WordPress and PHP server-side rendering retired. Zero downtime, zero lost URLs. - Hour 48 Verified Google Lighthouse returned a perfect 100. Core Web Vitals shifted from failing to passing on every metric. Client kept full ownership of the codebase. Outcomes What 48 hours of focused engineering delivered. - Perfect 100 Performance score Top 1% of websites globally on Google Lighthouse Desktop. - 4.6x faster Largest Contentful Paint From 2.8s down to 0.6s, well under the Core Web Vitals 2.5s threshold. - - - 97% reduction in Total Blocking Time From 670ms down to 20ms, deep into Google's green band. - 41x lower Cumulative Layout Shift From 0.5 down to 0.012, eliminating the mid-tap layout jumps. - SEO 77 to 91 Stronger crawlability foundation across meta, semantics, and structure. - Accessibility 84 to 91 Wider audience reach with cleaner contrast, semantics, and focus states. - Client owns the codebase No hosting markup, no platform lock-in, AI-assisted updates from here on. - Zero content loss Every page, every image, every URL preserved through the cut-over. Proprietary insight The 10xSearch Technical Crawl Framework. The conceptual model 10xSearch uses to sequence engagements like this one. Four levers, applied in order: - Image optimization via WebP and a global CDN - JavaScript code splitting and deferral - Static HTML pre-rendering at the edge - Client-owned codebase on Vercel infrastructure Note. This is an internal sequencing framework, not a public score. Results vary by baseline, template complexity, and third-party dependencies. Why a perfect 100 matters for AI search Fast pages get cited. Slow pages get filtered. AI answer engines favor fast, stable, structured pages when deciding what to cite. Core Web Vitals are a direct ranking input for Google and a strong proxy for crawl priority across every modern engine. Early adopters who fix the technical foundation gain compounding visibility advantages over competitors stuck on legacy stacks. Frequently asked About this case study. What was the actual technical work in 48 hours? A pixel-perfect rebuild of the existing WordPress site in Next.js, deployed to Vercel with static HTML pre-rendering, image optimization to WebP, JavaScript code splitting, and a global CDN. The visual design, content, and URL structure were preserved exactly. Are the Lighthouse numbers reproducible? Yes. The before and after captures were taken on the public homepage at migration cut-over using Google Lighthouse Desktop. Anyone can run the same audit against the live mountainrose-realty domain today and confirm the post-migration score. Why does a perfect Lighthouse score matter for AI search? AI answer engines favor fast, stable, structured pages when deciding which sources to cite. Core Web Vitals are a direct ranking input for Google and a strong proxy for crawl priority across every modern engine. Fixing the technical foundation is what unlocks every other AEO investment. Where does this fit in the 10 Pillars of Search? This engagement was primarily Pillar 7, Technical Crawl Optimization. The other nine pillars still matter for long-term visibility, but a failing performance score would have capped the upside on every one of them. Did Mountain Rose lose any content or URLs? No. The rebuild was pixel-perfect with zero content loss and full URL preservation. Search equity carried over, no redirect chains were introduced, and the brand experience stayed identical from the visitor's point of view. Want these numbers for your site? Free scan. Instant quote. Delivered fast. Book a 30-minute call and we will run Lighthouse against your homepage live, walk through what is dragging your score down, and tell you exactly what a 48-hour rebuild would look like for your stack. Book a strategy call Get the free report Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # Schedule an AI Visibility & Brand Audit | 10xsearch.com URL: https://10xsearch.com/schedule/ Summary: Book a 20-minute working session: live AI visibility audit across Google, Google Maps, ChatGPT, Perplexity, Gemini, and Google AI Overviews. Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call Home / Schedule Schedule Get found - get chosen. Book a 20-minute working session. We audit your current visibility on Google, Google Maps, ChatGPT, Perplexity, Gemini, and Google AI Overviews, and we show you what an engineered fix actually looks like for a brand in your category. Book a 20-minute call Email Rick directly What to Expect A short, structured working session - not a sales call. 20 minutes, on the calendar Short, structured, and on the record. We hold the time for you and stay on agenda. You leave with a written summary you can act on. Live AI visibility check We query ChatGPT, Perplexity, Google AI Overviews, and Gemini for your category and your name during the call. You see exactly where you appear, where competitors appear, and where the gap is. Honest scope, no pitch deck If a 10xSearch engagement is the right fit, we say so and explain the work. If something simpler would solve the problem, we say that too. No theater. What you bring Your domain, your top three competitors, and the question a real customer would type into ChatGPT to find a brand like yours. Nothing else needed. Pick a Time Book your 20-minute working session. Calendar trouble? Email Rick@10xSearch.com and we will book you in directly. Prefer to reach out directly? Email Rick@10xSearch.com Phone 303-589-2320 Ready when you are. Twenty minutes, a real audit, and a written summary you can act on. Book a 20-minute call Free AI Visibility Report Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # Book Your AI Audit Review Call with Joel Johnson URL: https://10xsearch.com/joel-johnson/ Summary: Book your AI Audit Review Call with Joel Johnson. Live AI visibility check across Google, Maps, ChatGPT, Perplexity, Gemini, and Google AI Overviews. - Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call Home / Joel Johnson AI Audit Review Call Book your AI Audit Review Call. With Joel Johnson , 10xSearch. Calendar trouble? Email joel@10xsearch.com and we will book you in directly. Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # Book Your AI Audit Review Call with William Lefort URL: https://10xsearch.com/william-lefort/ Summary: Book your AI Audit Review Call with William Lefort. Live AI visibility check across Google, Maps, ChatGPT, Perplexity, Gemini, and Google AI Overviews. - Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call Home / William Lefort AI Audit Review Call Book your AI Audit Review Call. With William Lefort , 10xSearch. Calendar trouble? Email will@10xsearch.com and we will book you in directly. Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # 10xSearch vs Qnary: SEO and AI Visibility Comparison URL: https://10xsearch.com/qnary/ Summary: 10xSearch vs. Qnary: search visibility infrastructure (SEO, AEO, GEO, GBP, asset velocity) versus executive social presence and managed posting. [Skip to content](#main-content) [10×Search](/) [Why us](/#different)[The Engine](/#engine)[Results](/#results)[The Formula](/#formula)[Visitor ID](/#visitor-id)[AEO](/aeo-ai-search-optimization)[Schedule](/schedule)[Blog](/blog)[Compare](/compare) Case Studies [ Mountain Rose Realty From a failing 33 to a perfect 100 ](/case-studies/mountain-rose-realty)[ The Kink Team Visibility growth and AI inclusion in 45 days ](/case-studies/10xsearch-gets-you-found-online) [Selected Work](/clients/) [FAQ](/#faq)[Free Report](/#report)[Book Call](/schedule/#book-calendar) [Home](/)/10xSearch vs Qnary Capability Comparison ## 10xSearch vs. Qnary: choose the right tool for the job. Both companies serve real needs. They are not the same product. This page lays out the capability difference so you can choose what to buy based on the outcome you actually want. Executive Summary ## Two different operating systems for visibility. If your goal is to be discovered in search engines, local results, and AI-driven answer experiences, 10xSearch.com is an infrastructure system designed to produce measurable visibility outcomes. It emphasizes 10 pillars of search, including AEO, SEO, GEO, and Google Business Profile optimization, plus a repeatable content-engineering pipeline. If your goal is executive voice, thought leadership, and managed posting workflows on social platforms, Qnary is oriented toward that managed service and brand-protection process. Note: both can coexist. One builds inbound discovery and durable visibility; the other maintains executive voice and consistency. ## What this comparison measures. This page compares technical capabilities and operational outputs. It is not a judgment of which company is "better." The key question is: what outcome are you trying to buy? 10xSearch.com ### Optimized for search visibility. - \- Search visibility (Google, Bing, AI engines) - \- Local discovery (Google Business Profile, Maps, local pack) - \- AI answers (ChatGPT, Perplexity, Gemini, Google AI Overviews) - \- Engineered content velocity (40 assets/month) - \- Schema, entity, and technical foundation Qnary ### Optimized for executive social presence. - \- Executive voice and thought leadership - \- Managed posting on social platforms - \- Reputation workflows and brand protection - \- Approval-driven publishing cadence - \- Personal brand consistency Tip: if you need both inbound discovery and executive presence, the cleanest architecture is 10xSearch.com for the discovery engine plus Qnary for executive distribution and voice. ## How to decide. **Choose 10xSearch.com when the KPI is discoverability:** how often you are found in search results, local packs, map surfaces, and AI-generated answers. The operating principle is consistent: build the right structures, produce the right volume of assets, and link them into a coherent authority graph that compounds over time. **Choose Qnary when the KPI is executive consistency:** the cadence, quality, and tone of leadership communication across social platforms, supported by managed approvals and a services layer. [Schedule a short review](/schedule/) Our 10 Pillars of Modern Search ## Execution included, not theory or outsourced tactics. When we say "10 pillars of modern search," we are not describing theory or outsourced tactics. These are the ten disciplines actively executed inside our monthly delivery, supported by our proprietary system and enforced through our engineered asset system. 1 ### Technical SEO Foundation Every asset is built on a crawlable, indexable foundation aligned with technical best practices. Site architecture alignment, indexation hygiene, and Core Web Vitals monitoring. 2 ### Semantic Content Architecture Assets are engineered as part of a broader topical system. Publishing velocity is intentional, designed to expand topical depth and reinforce relevance across related queries. 3 ### Entity & Schema Optimization Structured data is used to clearly define entities, relationships, and content meaning. JSON-LD schema enables search engines and AI systems to interpret assets accurately. 4 ### On-Page SEO & Information Hierarchy Each asset follows a consistent hierarchy using headings, internal anchors, and scannable layouts that support both human readability and machine parsing. 5 ### AEO (Answer Engine Optimization) Content is structured around real questions with direct, extractable answers. Supports eligibility for featured snippets, AI summaries, and answer engines. 6 ### Local SEO & Geographic Alignment Assets reinforce geographic relevance through location-aware language, contextual signals, and alignment with Google Business Profile positioning. 7 ### Visual Search Optimization Images are optimized through naming conventions, alt text, and contextual placement to support image-based discovery and accessibility. 8 ### Video Indexing Support When clients already have video content, assets support indexing through proper embedding, schema, and page-level structure. Video production itself is not included. 9 ### Internal Linking & Authority Flow Assets are interconnected using deliberate internal linking patterns that guide crawl paths, concentrate authority, and reinforce priority pages. 10 ### Engineered Content Velocity The system produces 40 engineered assets per month (per program scope), creating consistent crawl stimulation, faster topic saturation, and compounding visibility. These pillars represent what we execute, not what we outsource or upsell. Advanced tactics such as backlink acquisition and digital PR are available separately. FAQs ## Common questions about 10xSearch and Qnary. ### What does "10 pillars" mean in practical terms? It means the strategy is not limited to publishing blog posts. It covers technical SEO foundations, on-page structures, entity/schema layers, internal linking systems, local discovery via GBP, geographic authority (GEO), answer-engine readiness (AEO), and a production pipeline that maintains consistent asset output and measurement. ### What does AEO change versus traditional SEO? AEO focuses on structuring content so it can be selected as a direct answer by search engines and AI-driven experiences. That typically means tighter question-led organization, explicit definitions, clear comparison blocks, and machine-readable structure that improves retrieval and summarization. ### What does GEO mean here? GEO is geographic authority building: creating a structured web of location-specific assets (markets, neighborhoods, corridors, landmarks, service areas) that improves local relevance signals and increases discoverability across map and location-intent queries. ### How does GBP fit into the system? GBP is often the highest-impact surface for local intent. The system aligns website assets and GBP optimization so the business appears consistently across branded search, local pack results, and map-driven discovery. ### What does "engineered asset velocity of 40 assets per month" mean? It means the program is designed around a repeatable pipeline that produces and distributes a defined number of assets monthly (per program scope). The intent is compounding visibility: more relevant pages, supporting posts, social derivatives, and internal links feeding into priority topics and markets. ### Does Qnary do website SEO, AEO, GEO, or GBP optimization? Qnary is oriented toward executive presence and reputation on social platforms. It is not positioned as a technical SEO platform or a system for engineered local discovery via website + GBP + geographic authority assets. ### Can these two be used together? Yes. A common division of labor is: 10xSearch.com builds the discoverability engine (SEO + AEO + GEO + GBP + asset velocity), while Qnary manages executive voice and distribution on social platforms. ### If I can only pick one, how do I choose? If you need inbound demand from search, local results, and AI answers, start with 10xSearch.com. If you already have inbound flow and the gap is leadership visibility and voice consistency, start with Qnary. This page is intended as a capability comparison. Specific features and service scope may vary by plan and implementation. 10xSearch Inc. is a Colorado corporation with its legal office in Centennial, Colorado, and serves clients throughout the United States. Rick Janson, JD, MBA is the founder. References to Qnary are for capability comparison only; 10xSearch is not affiliated with or endorsed by Qnary. ## See how the discovery engine works. 40 engineered assets per month. Every page graded against the 40-point Perfect Page Formula. Every signal mapped to the 10 Pillars of Search. [Explore AEO and AI search](/aeo-ai-search-optimization/) [Schedule a 20-minute call](/schedule/) [![10xSearch.com](/images/10xsearch-logo.webp)](/) Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado [Rick@10xSearch.com](mailto:Rick@10xSearch.com) © 2026 10xSearch Last updated May 3, 2026 ### Capabilities - [Daily content engine](/#engine) - [Perfect Page Formula](/#formula) - [Visitor identification](/#visitor-id) - [AI receptionist](/#visitor-id) - [AEO & AI search](/aeo-ai-search-optimization) - [Comparison](/#compare) - [FAQ](/#faq) - [Sitemap](/sitemap.xml) ### Work - [Client portfolio](/clients/) - [Mountain Rose Realty case study](/case-studies/mountain-rose-realty/) - [The Kink Team case study](/case-studies/10xsearch-gets-you-found-online/) - [Comparison](/compare/) - [10xSearch vs Qnary](/qnary/) - [Blog](/blog/) - [Our mission](/mission/) - [About Rick Janson](/about-10xsearch-answer-engine-optimization-experts/) - [About](/about-10xsearch-answer-engine-optimization-experts/) - [Schedule](/schedule/) - [Privacy Policy](/privacy-policy/) - [Terms of Service](/terms-of-service/) - [Sitemap](/sitemap.xml) # Privacy Policy | 10xsearch.com URL: https://10xsearch.com/privacy-policy/ Summary: 10xSearch Inc. Privacy Policy: how we collect, use, and protect your information across our website, SMS, email, appointment reminders, and analytics. Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call Home / Privacy Policy Privacy Policy 10xSearch Inc. Privacy Policy: How We Collect & Protect Your Data Last updated: May 3, 2026 Introduction 10xSearch Inc. ("we," "us," "our") is committed to protecting your privacy. This Privacy Policy explains how we collect, use, and protect your information when you use our website and services, including SMS communications, email communications, appointment reminders, and website analytics tools. By using our website or services, or by opting into our communications, you agree to this Privacy Policy and our Terms of Service . Information We Collect Contact Information We may collect personal information such as your: - Name - Email address - Phone number - Mailing address This information may be collected when you: - Submit forms on our website - Request an audit or proposal - Book appointments - Contact us for information - Sign up for communications Communications Data If you opt in to receive communications from us, we may collect and maintain records of: - SMS consent and opt-in data - Email subscription preferences - Message delivery records - Opt-out activity Usage and Device Information We may use cookies, pixels, and analytics tools to collect information about how visitors interact with our website. This may include: - IP address - Browser type - Device type - Pages visited - Time spent on pages - Referral sources - Interaction behavior Appointment and Service Data If you schedule appointments, request audits, or use our services, we may collect information related to those interactions in order to provide services and maintain communication. How We Use Your Information We use your information to: - Provide requested services and consultations - Deliver website audits, SEO reports, and proposals - Send appointment confirmations and reminders - Send SMS and email communications you have explicitly opted into - Improve website functionality and marketing performance - Maintain compliance records related to messaging consent and opt-outs - Monitor fraud, abuse, or unauthorized activity - Operate and improve our business and services Cookies and Tracking Technologies We use cookies and similar technologies to improve user experience and understand how our website is used. These technologies may be used for: - Analytics - Website performance monitoring - Marketing attribution - Personalization You can control cookies through your browser settings. Some website features may not function properly if cookies are disabled. How We Share Your Information We do not sell personal information. We do not share mobile phone numbers, SMS opt-in data, or consent information with third parties or affiliates for marketing or promotional purposes. Information may only be shared in the following limited situations: Service Providers We may share information with trusted vendors who help us operate our services, including: - Messaging platforms - Email delivery providers - CRM and scheduling tools - Analytics services - Website infrastructure providers These providers may only use the information necessary to perform services on our behalf. Legal Requirements and Safety We may disclose information if required by law or if necessary to: - Comply with legal obligations - Protect our rights and property - Protect the safety of users or the public - Investigate fraud or abuse Business Transfers If 10xSearch Inc. is involved in a merger, acquisition, restructuring, or asset sale, information may be transferred as part of the transaction. Any successor entity will be required to honor this Privacy Policy. Third Party Sharing Statement No mobile information will be shared with third parties or affiliates for marketing or promotional purposes. Information sharing with subcontractors in support services (such as customer support or messaging services) is permitted. All other use case categories exclude text messaging originator opt-in data and consent; this information will not be shared with any third parties. Your Choices SMS Opt-Out You may opt out of SMS communications at any time by replying STOP to any text message. After you send STOP, you will receive a confirmation message and you will no longer receive SMS messages unless you opt back in. Email Opt-Out You may unsubscribe from marketing emails at any time by clicking the unsubscribe link included in our emails. Access, Correction, or Deletion You may contact us to request: - Access to your personal data - Correction of inaccurate information - Deletion of your personal information Requests will be handled in accordance with applicable laws. Data Security We implement reasonable administrative, technical, and physical safeguards designed to protect your information. However, no method of transmission over the internet or electronic storage is completely secure. Therefore we cannot guarantee absolute security. Children's Privacy Our services are not intended for individuals under the age of 13. We do not knowingly collect personal information from children under 13. Updates to This Policy We may update this Privacy Policy periodically. Any changes will be posted on this page with an updated Last Updated date. Contact Us If you have questions regarding this Privacy Policy, please contact: 10xSearch Inc. 7548 S Willow Circle Centennial, CO 80112 United States Phone: 303-589-2320 Email: info@10xsearch.com Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # Terms of Service | 10xsearch.com URL: https://10xsearch.com/terms-of-service/ Summary: 10xSearch Inc. SMS and Email Terms: consent, opt-outs, message frequency, carrier disclaimers, and support contacts for our messaging program. Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call Home / Terms of Service Terms of Service 10xSearch Inc. SMS & Email Terms: Consent, Opt-Outs & Support Last updated: May 3, 2026 Program Description 10xSearch Inc. may send SMS and email messages to users who voluntarily opt in. These communications may include: - Service updates - Marketing and promotional messages - Appointment confirmations - Appointment reminders - Consultation or audit follow-ups Message frequency may vary. Consent to Receive Messages By providing your phone number and opting in through our website, forms, or other channels, you authorize 10xSearch Inc. to send SMS messages to your mobile number. Consent to receive messages is not a condition of purchase. Age Restriction By opting in to receive SMS messages from 10xSearch Inc., you confirm that you are 18 years of age or older. Our SMS messaging program is not intended for use by minors. If you are under the age of 18, you may not opt in to or use this SMS service. Opt Out You may cancel SMS communications at any time by replying STOP to any message. After you send the SMS message STOP, we will send a confirmation message indicating that you have been unsubscribed. After this confirmation, you will no longer receive SMS messages from us unless you opt in again. Opt In Again If you previously opted out and wish to rejoin, you may opt in again through the same method you originally used to enroll. Help and Support If you are experiencing issues with our messaging program, you may reply HELP to any SMS message. You may also contact us directly for support: Email: info@10xsearch.com Phone: 303-589-2320 Message Frequency and Rates Message frequency may vary depending on your interactions and preferences. Message and data rates may apply depending on your wireless provider. For questions about your messaging or data plan, please contact your mobile carrier. Carrier Disclaimer Mobile carriers are not responsible for delayed or undelivered messages. Privacy Your use of our messaging services is governed by our Privacy Policy . Changes to Terms We may update these Terms of Service from time to time. Updated terms will be posted with a revised Last Updated date. Continued use of our services after updates constitutes acceptance of the revised terms. Contact Us Questions regarding these Terms of Service may be sent to: 10xSearch Inc. 7548 S Willow Circle Centennial, CO 80112 United States Phone: 303-589-2320 Email: info@10xsearch.com Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # Blog: AI Search, AEO, and Visibility Engineering URL: https://10xsearch.com/blog/ Summary: Structured writing on Answer Engine Optimization, AI search visibility, GEO, and getting cited by ChatGPT, Perplexity, Gemini, and Google AI Overviews. - Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call Home / Blog Field Notes Field notes on AI search visibility. Long-form writing on Answer Engine Optimization, AI search behavior, geographic authority, and the engineering decisions that decide whether a brand gets cited. Latest Engineered Asset AI Search Strategy May 26, 2026 12 min read 10xSearch Editorial Local SEO vs AI Search Optimization: Do You Need Both? Local SEO and AI Search Optimization (AEO/GEO) target different surfaces with different signals. Here's what each does, where they overlap, and how to sequence your investment in 2026. Read the article All Engineered Assets C AEO May 22, 2026 11 min Content That Gets Cited: How to Write for AI Search Engines Learn how to write content for AI search engines so ChatGPT, Perplexity, Gemini, and Google AI Mode retrieve, cite, and recommend your pages. Read AI Visibility May 19, 2026 6 min The Cost of Being Invisible in AI Search Zero share-of-voice in ChatGPT, Perplexity, Gemini, and AI Overviews is no longer a future risk - it is a current cost. What it means, what it compounds into, and where to start. Read AI Visibility May 19, 2026 5 min 5 Things Luxury Real Estate Agents Are Getting Wrong About AI Search Luxury real estate agents are losing AI-search share-of-voice in 2026 - five common mistakes, why each matters, and what to do instead. Practical, no hype. Read AI Search Visibility May 13, 2026 16 min EEAT for Real Estate Agents - Building Authority That AI Trusts How real estate agents earn citation authority in ChatGPT, Perplexity, and Gemini - author bios, transaction-history schema, NAR and MLS signals, and the case-study format AI engines actually quote. Read Case Studies & Findings May 12, 2026 11 min What Is an llms.txt File and Why Every Business Needs One A plain-text llms.txt file at the root of your domain tells AI search engines what your business does and which pages to cite. The 10xSearch playbook on format, anatomy, and deployment. Read Technical SEO May 11, 2026 16 min Schema Markup for AI Search - The Technical Guide to Getting Cited The schema types ChatGPT, Perplexity, Gemini, and Claude actually use to decide who gets cited - JSON-LD examples, parsing rules, and the eight mistakes we see every audit. Read AEO May 4, 2026 11 min How Google's AI Overviews Are Changing Local Search for Real Estate What AI Overviews actually do to real estate queries, why local agents are losing organic clicks, and the on-site changes that move sites from absent to cited in 2026. Read W April 21, 2026 13 min Why Your WordPress Site Is Invisible to ChatGPT (And How to Fix It) The technical reasons most WordPress sites are unreadable to AI engines, and a checklist of fixes you can execute this quarter. Read W April 2, 2026 6 min We Analyzed 11 Real Estate Brands' AI Visibility. A Solo Agent Is Outranking Sotheby's. A forensic look at how AI engines actually surface luxury real estate brands, with the structural reasons a solo agent is being cited above national franchises. Read T January 1, 2025 10 min The Future of Search: AEO's Role Where Answer Engine Optimization fits in the longer arc of search, and the categories of work that will compound for the next decade. Read W January 1, 2025 8 min Why Traditional SEO Is Failing REALTORS in the Age of AI Search Why ranked-list SEO under-delivers in an AI answer environment, and what residential teams should rebuild first. Read W January 1, 2025 9 min What Is Answer Engine Optimization (AEO) and Why Every REALTOR Needs It Now A practitioner's introduction to AEO for residential real estate professionals, with the structural moves that make a page citation-eligible. Read W January 1, 2025 12 min What Is AI Search Visibility? The Complete Guide for 2026 A definitional walkthrough of AI search visibility, what it measures, how it differs from rankings, and how engines decide which sources to cite. Read H January 1, 2025 13 min How ChatGPT Decides Which Businesses to Recommend An evidence-based explanation of the signals ChatGPT weighs when surfacing local business recommendations, and what to engineer first. Read G January 1, 2025 13 min GEO vs SEO: Why Traditional Search Optimization Isn't Enough Anymore How geographic authority engineering complements (and in some categories replaces) traditional SEO for AI-driven local discovery. Read T January 1, 2025 14 min The Real Estate Agent's Guide to Getting Found by AI Search Engines An end-to-end guide for agents and small teams covering entity setup, schema, GBP alignment, and the content cadence required to compound visibility. Read Want this in your inbox? Schedule a 20-minute working session and we will send you the AI Visibility Report specific to your brand and category. Schedule a 20-minute call Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # 10xSearch Pricing | AI Search Optimization Plans URL: https://10xsearch.com/pricing/ Summary: View 10xSearch pricing for AI search optimization, SEO, AEO, GEO, local search, website visibility, engineered assets, and affiliate opportunities. Skip to content 10 × Search Why us The Engine Results The Formula Visitor ID AEO Schedule Blog Compare Case Studies Mountain Rose Realty From a failing 33 to a perfect 100 The Kink Team Visibility growth and AI inclusion in 45 days Selected Work FAQ Free Report Book Call Home / 10xSearch Pricing Services 10xSearch Pricing View 10xSearch pricing for AI search optimization, SEO, AEO, GEO, local search, website visibility, engineered assets, and affiliate opportunities. 10xSearch What this page answers - 10xSearch pricing - AI search optimization pricing 10xSearch ships under one of three retainer plans, all built around the same monthly deliverable: 40 engineered Perfect Pages per month across the 10 Pillars of Search. The plans differ in what surrounds the velocity work. No setup fees on any plan. First batch in production within the first week of every engagement. Choose the Search Visibility Plan That Fits Your Growth Strategy Every brand starts with the same first conversation: a 20-minute working session where we run the domain through ChatGPT, Perplexity, Gemini, and Google AI Overviews live. The audit tells us which plan fits, what the first 60 days should cover, and whether we are the right partner at all. Plans are designed to match the work to where the brand sits on the visibility curve. A brand starting from scratch needs different first-month work than a brand with a strong foundation but no AI citation. - Standard Plan - Super Affiliate Plan - Founder Plan What Every Plan Includes 40 engineered Perfect Pages per month, two per business day. Every page satisfies the 40-point Perfect Page Formula across the 10 Pillars of Search. Full technical foundation: SSR, server-rendered JSON-LD schema graph, Core Web Vitals 90+, sitemap and robots.txt admitting every major AI crawler. Bing Webmaster Tools registration with IndexNow keyfile. Full Local stack: Google Business Profile build-out, citation NAP hygiene, neighborhood and service-area pages with Place schema, structured review-velocity workflow. Monitoring across Google, Google Maps, ChatGPT, Perplexity, Gemini, and Google AI Overviews. Monthly written summary. - AI Search Visibility Strategy - SEO, AEO, and GEO Execution - Monthly Engineered Assets Standard Plan The core retainer. Best for brands whose technical foundation is solid and need to move directly into velocity and monitoring. 40 Perfect Pages per month, full Local stack, monitoring across all four answer engines. Onboarding includes the AI visibility audit, schema audit, and a 60-day asset slate plan. - $2,500 Per Month - 15% Affiliate Opportunity - Search Visibility Execution Super Affiliate Plan For brands that need the Standard work plus a higher-touch entity-graph and authority push. Includes targeted press-placement scoping, third-party citation expansion, and additional Person and Organization schema depth. Designed for brands where the entity-level signals are limiting citation more than the page-level structure is. - $3,500 Per Month - 40% Referral Commission - Built for Influencers and Network Leaders Founder Plan The full engagement with maximum founder time. Includes the Standard and Super Affiliate work plus direct strategic input on positioning, category framing, and competitive response across the answer engines. Designed for brands where the principal is hands-on with brand strategy and wants the structural search work integrated with that strategy in real time. - $10,000 Upfront - $2,500 Per Month for Life - 40% Referral Commission Related Where to go next. Schedule a working session About Rick Janson Field notes AEO services Compare 10xSearch Next step Want this engineered for your business? Book a 20-minute working session. Live AI visibility check across Google, Google Maps, ChatGPT, Perplexity, Gemini, and Google AI Overviews, with a written summary you can act on. Book a 20-minute call See the AEO service Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # Local SEO vs AI Search Optimization: Do You Need Both? Most local businesses in 2026 should invest in both Local SEO and AI Search Optimization, but in a specific order: Local SEO foundations first, AI Search layered on top. The two disciplines target different surfaces, weight different signals, and grow on different curves, which means treating them as substitutes is the most common way local businesses waste money this year. Local SEO still gets you in front of the largest pool of search traffic on the planet. AI Search Optimization gets you cited by the engines that increasingly answer high-consideration buyer questions before a single click happens. This article walks through what each one is, where they overlap, where they diverge, and how to sequence the investment. What Local SEO Actually Means Local SEO is the practice of getting a business to appear in Google's organic results and in the Local Pack, the three-business map module that sits above the blue links on most location-flavored queries. The discipline has been mature for more than a decade, and the inputs are well understood. The core levers are a claimed and fully populated Google Business Profile, consistent NAP data (name, address, phone) across every directory and citation source on the web, on-page SEO fundamentals like clear titles and metas and proper heading structure, a steady review velocity with timely responses, and a backlink profile that signals real-world relevance. Each of those inputs feeds a different part of Google's local ranking system, and weakness in any one of them tends to cap how high the others can carry you. The tooling reflects that maturity. Practitioners use SEMrush and Ahrefs for keyword and backlink research, BrightLocal and Moz Local for citation management, and Local Falcon for grid rank tracking. The category is settled enough that two competent agencies will usually agree on what is broken and what to fix first. What you optimize for is straightforward: ranking inside the Local Pack and on page one of organic results for the queries your buyers type when they are ready to act. The conversion path is also straightforward: click-to-call, click-for-directions, click-for-website. Those clicks are measurable in Google Business Profile insights, in Search Console, and in Google Analytics. The pipeline is legible end to end. The other thing worth saying directly: the volume is still here. Google still handles the vast majority of search queries worldwide in 2026. If you are a local business with a finite marketing budget, that pool is too large to ignore in favor of newer surfaces that have not yet matched it. What AI Search Optimization Means AI Search Optimization is the practice of getting a business cited as a source when AI engines generate answers to relevant queries. The discipline goes by two terms that are used roughly interchangeably: AEO, for Answer Engine Optimization, and GEO, for Generative Engine Optimization. Different practitioners prefer different labels. The work is the same. The engines that matter in 2026 are ChatGPT, Perplexity, Claude, Gemini (including Google's AI Mode), Copilot, and Grok. Each one has its own retrieval behavior, but they share a family of preferences that distinguish them from classic search ranking. The signals that move AI engines are not the same signals that move Google's blue links. Structured data is fundamental: JSON-LD schema for Organization, LocalBusiness, FAQPage, Article, BreadcrumbList, Person, Service. The llms.txt file at site root, modeled loosely on robots.txt, lets retrieval bots understand what is on a site and how it is organized. Content geometry matters more than raw word count: a direct answer in the first hundred words, complete sentences that can be lifted as a quote without surrounding context, clean FAQ sections that map a likely question to a self-contained answer, and consistent heading hierarchy that signals topical structure. Entity grounding is the deeper layer. AI engines do not just read pages, they verify that the business behind a page is a real entity with a stable identity. That verification leans on a knowledge graph composed of Wikidata, Google Business Profile, LinkedIn, the company's own About page, and authoritative third-party citations. When those sources agree on who you are, where you are, and what you do, AI engines will cite you. When they conflict or are sparse, AI engines will pick someone else. What you optimize for is being cited as a source in AI-generated answers. The metric is citation rate, measured across a representative basket of queries on each engine. The result is harder to attribute than a Google click, but often easier to influence per dollar than ranking a competitive head term. The Surface Difference The two disciplines optimize for two different surfaces, and the surfaces work differently in ways that matter for how a buyer experiences your business. Google's search results page is a scan-and-click surface. A user types a query, sees ten organic results and a Local Pack, scans titles and descriptions, and clicks the one that looks most relevant. The page you optimized is the page they land on. You get a chance to convert them inside your own environment. The model assumes the click. An AI-generated answer is a read-and-decide surface. A user types a question, reads a synthesized response that draws from multiple sources, and either acts on the answer directly or clicks through to one of the cited sources for more depth. Many queries resolve without any click at all. The page you optimized never gets visited in those cases; instead, your business gets mentioned inside the model's answer, with or without a link. That changes what a win looks like. On classic search, a win is rank plus traffic plus conversion. On AI search, a win can be a citation that produces a branded query later, a citation that builds entity recognition over time, or a citation that converts directly when the user asks a follow-up like "how do I contact them." A buyer might encounter your business three times across ChatGPT and Perplexity before they ever type your name into Google. The Signal Difference The signal stacks behind these two surfaces overlap less than most operators assume. Classic Local SEO weights inbound links heavily. The link graph has gotten harder to game over the last decade, but a defensible local ranking still rests on real links from real local sources: chambers of commerce, local press, partner businesses, organizations the business sponsors. Strip those out and rankings drift. AI Search Optimization weights a different stack. Structured data carries more weight than links per unit of effort, because the engines need machine-readable confirmation of who you are before they will cite you as a source. Content geometry, the way an answer is structured on the page, controls whether your sentence ends up quoted verbatim or skipped over for a competitor's cleaner phrasing. Entity grounding, the consistency of your identity across Wikidata, Google Business Profile, LinkedIn, and your own site, controls whether you survive the engine's deduplication step. The tactical implication is sharp. A site that ranks well on Google can still be invisible to ChatGPT and Perplexity if its schema is thin, its llms.txt is missing, its FAQ structure is loose, and its entity graph is fragmented. The reverse is also true: a small site with disciplined schema and clean content geometry can get cited by AI engines on queries where it does not yet rank in classic search. The two systems read for different things. Where They Overlap The disciplines are not opposites. There is a real foundation they share, and recognizing that foundation is how you avoid paying for the same work twice. Good on-page SEO is good AEO content. Clear titles, semantic heading hierarchy, fast loading, clean internal linking, descriptive image alt text, mobile responsiveness: all of these help Google rank a page and also help AI engines parse it. A page that fails Lighthouse on Core Web Vitals tends to underperform on both surfaces. E-E-A-T signals serve both. Named authors with real credentials, an honest About page that ties the business to a verifiable person and place, transparent contact information, and visible expertise indicators all feed Google's quality systems and also feed the AI engines' decisions about whether to cite a source. The investment compounds across surfaces. Structured data is fundamental to both. Google has used schema for rich results and entity disambiguation for years. AI engines now use the same schema as a primary input for retrieval and grounding. Adding JSON-LD for Organization, LocalBusiness, Service, FAQPage, and BreadcrumbList helps both at once. Local NAP consistency anchors both. Google's Local Pack rewards a business whose name, address, and phone are identical across the open web. AI engines lean on the same consistency to confirm entity identity, and they pull from many of the same sources Google uses, including Google Business Profile itself. The Volume Question Volume is the question that decides where the first dollar goes, and the honest answer is that Google still holds the larger pool in 2026 by a wide margin. The vast majority of search queries worldwide still happen on Google. Local Pack appearances still drive a meaningful share of phone calls and direction requests for service businesses. For pure proximity intent, the kind of query where a buyer types "plumber near me" at the moment they need a plumber, classic Local SEO is still the surface that produces the most pipeline per dollar. AI engine volume is growing fast and the trajectory is steep. ChatGPT now handles a query volume in the hundreds of millions to over a billion per day depending on which counting method you use, and Perplexity, Gemini AI Mode, Claude, Copilot, and Grok add to that total. The growth is not uniform across query types: research queries, comparison queries, "what should I do about X" queries, and any query where the buyer wants synthesis rather than a list of links have shifted toward AI engines faster than navigational or pure proximity queries. For local businesses the pattern that matters is purchase consideration. High-consideration purchases like real estate, professional services, home renovation, healthcare, and legal services are where buyers do extensive research before contacting anyone. That research is where AI engines are taking share fastest. Low-consideration, proximity-driven purchases still flow primarily through Google and Maps. A landscaper booking one-time mowing jobs needs Local SEO more than AI Search. A real estate brokerage handling seven-figure transactions needs both, and the AI Search piece is increasingly where their next decade of buyers will first encounter them. How to Sequence the Investment The right sequence is not negotiable, because the second discipline depends on the first. Start with the Local SEO foundation. Claim Google Business Profile and complete every field. Audit citations and fix every NAP inconsistency across the open web. Tighten on-page basics: titles, metas, H1s, alt text, internal linking, page speed. Build a real review pipeline with timely responses. Get the link profile honest and locally relevant. If you skip any of these, the surface you are building on cannot hold the weight of the AI work. Layer AEO on top once the foundation is solid. Add structured data for Organization, LocalBusiness, FAQPage, BreadcrumbList, and Person on every page where it applies. Publish an llms.txt at site root that organizes your content for retrieval bots. Rewrite key pages with deliberate content geometry: a direct answer in the first hundred words, FAQ sections with self-contained answers, clean heading hierarchy that signals topic structure. Add author markup with real credentials. Confirm your entity is consistent across Wikidata, Google Business Profile, LinkedIn, and any other authoritative profile the engines might read. Do not try to skip the foundation. AI engines lean on the same identity signals that Local SEO is built on, including Google Business Profile itself, citation consistency, and a clean entity graph. A site that tries to win AEO without the Local SEO basics in place is asking AI engines to cite an entity those engines cannot verify. Track both systems on their own terms. For Local SEO, track Local Pack rank by query and grid coordinate, organic position, Search Console impressions, and Google Business Profile insights. For AEO, track citation rate by engine across a representative query basket, mention rate inside AI answers, and downstream branded search lift in Search Console. The metrics, methodologies, and timelines differ. A Local SEO change can show in two to four weeks; an AEO change can take six to twelve weeks to fully propagate across engines. What to Ignore Some of the loudest advice in this space is wrong, and following it will cost real money. "SEO is dead because of AI" is wrong. Google still handles the vast majority of search volume, and a business that abandons Local SEO in 2026 is walking away from the largest single source of local buyer traffic on the planet. The shape of SEO is changing. The volume is not collapsing. "Just write more content" is wrong for AEO. Content geometry, structure, and entity grounding matter more than raw page count for AI citation. A site can publish two hundred thin posts and never get cited, while a site with thirty well-structured pages and clean schema gets cited regularly. The page-count arms race is a classic SEO instinct that does not transfer cleanly to AI engines. "Do AEO instead of SEO" is wrong because the two share a foundation. The schema, entity grounding, on-page fundamentals, and NAP consistency that AEO needs are the same things Local SEO needs. You can only choose which surface to optimize first. "Wait until AEO is more mature" is wrong because citation patterns are forming now. AI engines are building habits about which sources to cite for which topics in which geographies, and those habits compound. A business that establishes citation patterns in 2026 has a structural advantage over a competitor that waits until 2028 and then tries to displace established sources. Maturity is not a reason to wait. It is a reason to start. FAQs Do I need both Local SEO and AI Search Optimization? Most growing local businesses in 2026 need both. Local SEO still owns the largest pool of buyer traffic, and AI Search Optimization is increasingly where high-consideration buyers do their early research. The two disciplines share a foundation, so investing in both is more efficient than picking one. What is the difference between SEO and AEO? SEO optimizes for ranking on a search results page that the user scans and clicks. AEO, also called GEO, optimizes for being cited as a source inside an AI-generated answer the user reads directly. SEO weights inbound links and on-page relevance most heavily. AEO weights structured data, content geometry, and entity grounding most heavily. Is AI search going to replace Google? Not in any near-term timeframe. Google still handles the vast majority of global search volume in 2026, and proximity-driven local queries remain firmly in its surface. AI engines are taking share fastest on research, comparison, and synthesis queries. The right framing is layered surfaces, not replacement. Which should I do first, Local SEO or AI Search? Local SEO first, then AI Search layered on top. AI engines lean on the same identity signals Local SEO is built on, including Google Business Profile, NAP consistency, and a clean entity graph. A site that tries to win AEO without the foundation in place is asking AI engines to cite an entity those engines cannot verify. How do I get cited by ChatGPT? Build a verifiable entity identity through consistent Google Business Profile, Wikidata, and LinkedIn data. Add JSON-LD schema for Organization, LocalBusiness, FAQPage, and Person. Publish an llms.txt file at site root. Rewrite key pages with direct answers in the first hundred words and self-contained FAQ sections. Earn citations from third-party sources the engine already trusts on your topic. Bottom Line Local SEO and AI Search Optimization are two layers of the same buyer-finding system. Local SEO owns the largest pool of search volume in 2026 and is the foundation everything else rests on. AI Search Optimization is where high-consideration buyers increasingly form their first impression of a business, and the citation patterns being built this year will compound for years. Most growing local businesses need both. The sequence matters, the foundation comes first, and the second discipline rewards businesses that did the first one honestly. 10xSearch builds across both surfaces because that is what the 2026 search environment actually requires. About 10xSearch We build the discoverability engine. 10xSearch.com engineers websites to be found and cited by Google, Google Maps, ChatGPT, Perplexity, Gemini, and Google AI Overviews. 40 engineered assets per month, every page graded against the 40-point Perfect Page Formula . Schedule a 20-minute call Explore AEO and AI search More Reading Related field notes. 11 min read Content That Gets Cited: How to Write for AI Search Engines Read 5 min read 5 Things Luxury Real Estate Agents Are Getting Wrong About AI Search Read 6 min read The Cost of Being Invisible in AI Search Read Back to all field notes Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # 5 Things Luxury Real Estate Agents Are Getting Wrong About AI Search Luxury real estate is one of the categories where AI search visibility matters most. The queries that determine who a buyer reaches out to - "best realtor in {city}," "luxury real estate agent {neighborhood}," "{agent name} reviews," "who should I consider for a $5M home in {market}" - are increasingly resolving inside ChatGPT, Perplexity, Google AI Mode, and Google AI Overviews. The buyer asks the AI assistant, and the AI assistant produces a short list of names. The agents on that list win. The agents not on that list never enter the consideration set. Across the luxury agents and brokerages we work with, five mistakes show up repeatedly. None of them require new marketing budget to fix. All of them are structural - which is part of why they have not been addressed. Mistake 1: Treating AI Search as "Just Google with a Chatbot UI" The most common misconception. AI search is not a chatbot bolted onto traditional search results. It is a fundamentally different retrieval-and-summarization system that produces a single synthesized answer rather than a list of links. The practical implication: traditional SEO tactics - keyword density, backlink quantity, generic "best of" listicles - move the needle less for AI citations than they do for traditional rankings. What moves the needle for AI citation: entity completeness (the brand and agent are recognized as distinct, well-defined entities), schema markup (the page's data is machine-readable), content geometry (the answer is in the first 100 words, each H2 opens with a complete-sentence answer), and inbound citations from authoritative sources (press, industry directories, Wikipedia where applicable). An agent's website that ranks well on Google may still be invisible in AI search. They are scored on different signals. Mistake 2: Assuming Brand Pages Alone Earn Citations Most luxury real estate agents have an About page, a Why-Choose-Us page, and a contact form, and assume that AI assistants will pull from those when someone asks about the agent. They will not - at least not reliably. The reason: AI assistants prefer to cite third-party sources for credibility. A brand citing itself ("we are the best") carries less weight than a third party citing the brand ("local press, industry directory, peer brokerage profile mentioned the agent as a top luxury performer"). The brand pages can confirm details once the brand is already known to the AI. They rarely originate the recognition. What earns the citation: real local press coverage, real entries in industry directories, peer-brokerage profile mentions, real client reviews on Google Business Profile, and a Knowledge Graph entity that AI systems can trust. The brand pages are necessary - but not sufficient. Mistake 3: Ignoring Local Entity Completeness This is the most fixable mistake on this list and the one most often overlooked. Local entity completeness means the agent is recognizable as a distinct, well-defined entity across every signal layer the AI assistants use: - Google Business Profile is verified, complete, and matches the agent's brand name, address, and phone exactly - The agent's website has Person schema markup with full sameAs links (Compass profile, LinkedIn, Realtor.com, Zillow, Instagram, YouTube, Facebook) - The brokerage's website lists the agent on the official team page with consistent name spelling - The Knowledge Graph entry for the agent (if one exists) has accurate fields: areas served, certifications, awards, affiliations - Industry directories (Realtor.com, Compass.com, the local Realtor association directory, niche luxury directories like Mayfair International Realty or Forbes Global Properties) list the agent consistently Missing or inconsistent entries across these signals fragment the entity. An AI assistant looking up "{agent name} {city}" cannot consolidate the agent into a single trusted entity, so the agent does not surface in responses where AI assistants prefer to cite high-confidence entities. The fix is the most tedious work in this list and the highest-ROI. Most luxury agents have meaningful entity gaps that can be closed in two to three weeks. Mistake 4: Writing for Human Readers Only Most luxury real estate content is written exclusively for the human reader - the prospective UHNW buyer or seller scrolling on a phone. That is correct as a primary audience. But content that only works for the human reader and ignores the AI extraction layer leaves citations on the table. The structural pattern that works for both: - The first 100 words of the article answer the implied query directly, in complete sentences - Each H2 opens with a complete-sentence answer to the section's implied question, not a transition or a marketing hook - Statistics carry an in-line source and date within two sentences - At least one extractable passage of 130-170 words answers a discrete question coherently in isolation - Named entities (neighborhoods, schools, country clubs, named architects, named developers) are spelled consistently and appear at least 15 times across the page in total This is not "writing for robots." It is writing in a way that makes the article extractable by AI assistants while still reading well for the human. The two requirements are not in tension once the structure is right. Mistake 5: Measuring Success by Traditional Rankings Instead of AI Mention Share This is the meta-mistake. An agent who has not measured their AI share-of-voice does not know whether the work they are doing is moving the needle. Traditional SEO rankings - position 8 for "best realtor in Aspen," position 12 for "luxury real estate {city}" - are still useful as one input. But for AI search visibility, the relevant metric is whether the agent's name actually appears in AI responses when a prospect asks the questions that drive consideration: - "Who is the best luxury real estate agent in {city}?" - "{Agent name} reviews" - "Recommend a luxury real estate agent for buying a $5M home in {market}" - "{Brokerage name} top agents in {city}" - "Best Compass / Sotheby's / Douglas Elliman agents in {city}" The agent who is mentioned in three of those five queries has commercial AI search presence. The agent mentioned in zero does not - regardless of where their website ranks in traditional Google. Multi-platform mention-share tracking across ChatGPT, Perplexity, Gemini, AI Mode, and Copilot is the right baseline. Without that measurement layer, the entity-and-content work is unmeasured and unproven. What to Do With This Five mistakes, five corresponding moves: - Stop treating AI search as a chatbot version of Google. Treat it as a separate channel with its own ranking logic. - Build outbound citation infrastructure (press, directories, peer mentions). Do not rely on brand pages alone. - Close the entity completeness gap - Google Business Profile, Person schema, sameAs links, brokerage page consistency. - Restructure the top three to five highest-impact pages on the agent's website for AI extraction. - Set up multi-platform mention-share tracking and measure monthly. The agents and brokerages who close these gaps in the next six months will have a meaningful AI-search advantage going into 2027. The ones who do not will be invisible to the buyers asking the questions that drive consideration today. For a complete AI visibility audit of an agent or brokerage - including the entity-completeness gaps, the highest-impact page diagnoses, and the multi-platform visibility baseline - [10xSearch runs the scan](https://10xsearch.com) and produces the action plan from one engagement. The first scan is the baseline; everything that comes after is the structural work to close the gap. About 10xSearch We build the discoverability engine. 10xSearch.com engineers websites to be found and cited by Google, Google Maps, ChatGPT, Perplexity, Gemini, and Google AI Overviews. 40 engineered assets per month, every page graded against the 40-point Perfect Page Formula . Schedule a 20-minute call Explore AEO and AI search More Reading Related field notes. 6 min read The Cost of Being Invisible in AI Search Read 16 min read EEAT for Real Estate Agents - Building Authority That AI Trusts Read 11 min read What Is an llms.txt File and Why Every Business Needs One Read Back to all field notes Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # The Cost of Being Invisible: What Zero AI Search Presence Means for Your Business A measurable share of the queries that used to send users to your website now resolve entirely inside an AI answer - inside ChatGPT, Perplexity, Google AI Mode, Gemini, Copilot, or the Google AI Overview at the top of the SERP. The user gets the answer, the brands inside that answer get the recognition, and the brands outside that answer get nothing. If your business has 0% share-of-voice in those AI answers - meaning your brand is not cited, not mentioned, and not surfaced in any platform - that is no longer a future risk. It is a current cost. The question is what the cost actually is, and what compounds if it goes unaddressed. What "Invisible" Actually Means A brand can be invisible to AI search in several distinct ways, and the right intervention depends on which. Invisible at the entity level. AI assistants do not recognize the brand as a distinct entity. There is no knowledge-graph entry, no Wikipedia article, no consolidated set of sameAs identifiers across LinkedIn, Crunchbase, industry directories, and the brand's own website. Even if the brand has content, it cannot be attributed to a known entity. Invisible at the citation level. The brand is a known entity but is not cited as a source in AI answers for relevant queries. This is the more common case for established businesses with mature websites - the entity is recognized, but the brand's content is not the source the AI assistant pulls from when answering the questions that matter. Invisible at the topic level. The brand may be cited for some topics but not for the queries that drive its commercial value. A real estate agent cited for community-history questions but not for "best realtor in {city}" queries is technically visible but commercially invisible. Each of these is a different problem. Bundling them under "AI visibility" obscures which intervention to make first. How User Behavior Has Shifted The behavior shift toward AI assistants is the underlying driver of the cost. ChatGPT weekly active users crossed 300 million in late 2024 and have continued to grow rapidly, with OpenAI publicly referencing figures in the 800 to 900 million weekly-active range across late 2025 and early 2026. Even allowing for substantial overlap with traditional search use, the audience is now too large to dismiss as fringe. Perplexity reported monthly query volume of approximately 230 million in mid-2024, growing to over 780 million monthly queries by mid-2025 per the company's public statements. Google AI Overviews appear on roughly 48% of monitored queries in BrightEdge tracking through late 2025 and early 2026, with prevalence meaningfully higher in informational verticals such as healthcare and education. Pew Research's July 2025 study of AI Overview behavior reported that users clicked on a search result on 8% of visits where an AI Overview was present, versus 15% on visits without one. The click-through reduction is real and measurable. The implication: a non-trivial share of the queries your business used to compete for in traditional search is now resolving inside an AI answer that does not include your brand. The traffic does not arrive. The traffic does not exist. The Opportunity Cost Framework The cleanest way to size the cost of AI search invisibility is the opportunity cost framework rather than the lost-traffic framework. Lost traffic is the wrong starting point because much of the lost traffic was zero-intent traffic that never converted anyway. Opportunity cost looks at the queries with real commercial intent and asks whether the brand should have been the cited source. Three layers to the framework: Discovery queries you used to win. Queries that previously sent qualified buyers to your site at the top of the funnel - "how does X work," "what is the difference between X and Y," "who should I consider for X." If these queries now resolve in an AI answer that cites your competitors instead of you, the cost is the discovery prospects you no longer reach. Comparison queries you used to be present in. Queries like "X vs Y" or "best X in {category}" where AI assistants now surface a curated list of brands. If your brand is not on that list, the cost is the qualified-comparison-stage prospects who simply do not see you. Branded queries that are leaking. When a prospect asks an AI assistant about your specific brand and the AI provides incorrect, outdated, or competitor-favorable information, the cost is the consideration-stage prospect who now has a worse impression than they would have had landing on your own site. Each of these is a real, modelable opportunity cost. The combination compounds. Why It Compounds The AI search disadvantage does not stay static. It compounds for three reasons. First, the AI assistants learn from what they cite. Brands that are already cited become more cited over time because their content has been ingested, their entity has been recognized, and the platform's internal models reinforce those associations. Brands that are not cited do not catch up by accident - they stay invisible until the underlying signals change. Second, the discovery shift is one-way. A prospect who learned about your competitor from ChatGPT is not going to randomly discover you later through traditional search and reconsider. The window in which discovery happens has narrowed, and the brand that wins the AI-search citation often wins the prospect outright. Third, the entity recognition gap compounds. A brand with a partial entity layer in 2026 - missing sameAs links, missing knowledge graph, missing schema completeness - has a structural disadvantage relative to a brand that has invested in entity completeness. That disadvantage does not fix itself; it requires deliberate work. What the Recovery Looks Like Recovering from zero AI search presence is not a single intervention. It is a layered set of moves that have to be sequenced. Entity layer first. Wikipedia, Wikidata, Crunchbase, LinkedIn Company Page, Schema.org Organization markup with consistent sameAs, Knowledge Graph completeness. AI assistants cannot cite a brand they cannot recognize. Citation infrastructure second. Press coverage, industry-publication mentions, Wikipedia citations to the brand's content, links from authoritative sources. AI assistants cite sources that other authoritative sources cite. The path to AI search citation runs through traditional editorial citation. Content geometry third. The pages that earn AI Overview and ChatGPT citation are structured for extraction - the primary answer in the first paragraph, complete-sentence answers to each H2's implied question, statistics with date and source within two sentences. This is not the same as traditional SEO writing. Platform-aware optimization fourth. ChatGPT, Perplexity, Google AI Mode, and Gemini have distinct citation behavior. Optimization that helps one platform may not move the others. Multi-platform visibility tracking is necessary to measure what is working. The first AI search citation typically lands one to three months after the entity and citation infrastructure is in place. The compounding effect typically becomes measurable six to twelve months in. What the Honest Action Plan Looks Like If the brand is at 0% AI search share-of-voice today, three practical next moves: Audit the entity layer. Is the brand recognizable across Wikipedia, Wikidata, LinkedIn, industry directories, and the brand's own structured data? Most "invisible" brands have at least one major gap in the entity layer that is fixable in weeks, not months. Identify the highest-impact citation pages. Out of all the content the brand currently publishes, which three to five pages have the strongest chance of being the cited source for high-value commercial queries? Those are where the AEO-style restructuring should happen first. Set up multi-platform visibility tracking. Without measurement across ChatGPT, Perplexity, Gemini, Google AI Mode, and Copilot, the work cannot be evaluated honestly. A monthly visibility report is the baseline. Bottom Line Zero AI search presence in 2026 is no longer a theoretical concern. It is a measurable, compounding cost on the business that will not fix itself. The brands that move on the entity layer, the citation infrastructure, and the content geometry in 2026 will have a meaningful structural advantage by 2027. The brands that wait will pay the cost without seeing it directly until it shows up in lower lead volume, lower brand-name search, and lower qualified inbound from the channels that AI search has begun to displace. If you want a complete audit of your brand's current AI search presence across all major platforms, [10xSearch runs a visibility scan](https://10xsearch.com) that maps your share-of-voice across ChatGPT, Perplexity, Gemini, AI Mode, and Copilot, then traces the specific entity-and-content gaps that are driving the result. The first scan is the baseline; everything that comes after is the work to close the gap. About 10xSearch We build the discoverability engine. 10xSearch.com engineers websites to be found and cited by Google, Google Maps, ChatGPT, Perplexity, Gemini, and Google AI Overviews. 40 engineered assets per month, every page graded against the 40-point Perfect Page Formula . Schedule a 20-minute call Explore AEO and AI search More Reading Related field notes. 16 min read EEAT for Real Estate Agents - Building Authority That AI Trusts Read 11 min read What Is an llms.txt File and Why Every Business Needs One Read 16 min read Schema Markup for AI Search - The Technical Guide to Getting Cited Read Back to all field notes Get found on AI, Google, and Maps. Our mission is to make great businesses impossible to miss in the AI search era. 10xSearch Inc. Legal office: Centennial, Colorado Rick@10xSearch.com © 2026 10xSearch Last updated May 3, 2026 Capabilities - Daily content engine - Perfect Page Formula - Visitor identification - AI receptionist - AEO & AI search - Comparison - FAQ - Sitemap Work - Client portfolio - Mountain Rose Realty case study - The Kink Team case study - Comparison - 10xSearch vs Qnary - Blog - Our mission - About Rick Janson - About - Schedule - Privacy Policy - Terms of Service - Sitemap --- # AEO agency for luxury real estate URL: https://10xsearch.com/aeo-ai-search-optimization/ > An AEO agency for luxury real estate should make an agent, team, or brokerage easier for search engines and AI assistants to identify, verify, cite, and recommend. The work should combine technical SEO, entity and market clarity, source-backed answers, structured data, and a fixed prompt panel that separates recognition, citation, recommendation, position, and sentiment. Before hiring a provider, ask to see its prompt list, engines, models, run dates, locations, raw answer captures, cited URLs, verdict rules, and limitations. 10xSearch publishes those materials for its own baseline and for eligible client evidence assets. Its current public evidence includes a 50-prompt, 100-capture self-audit with zero errors and no 10xSearch recommendations, plus a reconciled Doug Leibinger ledger with 40 current recognition questions and three older wins. These point-in-time measurements do not guarantee future visibility, traffic, leads, or revenue. By Rick Janson, JD, MBA. Published 2026-05-03. Updated 2026-08-13. 10xSearch helps luxury real estate experts become easier for Google and AI answer engines to understand, verify, cite, and recommend. The work combines technical SEO, entity clarity, source-backed content, structured data, and repeated prompt measurement. ## Original data - **40 current strict questions:** Doug Leibinger, newest August 10 scan batch - **30 non-branded current questions:** The prompt does not contain Doug's name - **59 questions in the current panel:** Buyer, seller, reputation, and market intent - **6 engines in the current panel:** Measured independently in one scan batch ## What an AEO agency should actually do AEO is not a synonym for adding FAQ schema. A credible engagement makes the business entity unambiguous, publishes answers that can survive citation, earns corroborating sources, fixes crawl and rendering barriers, and measures the questions that matter to buyers. - Define the exact commercial questions the brand wants to win. - Connect the person, company, service, market, credentials, and proof with stable entity identifiers. - Publish direct answers with named authors, dates, sources, methods, and limitations. - Track strict recognition, recommendation, citation, position, sentiment, engine, model, location, and run date separately. ## Why luxury real estate needs a specialist Luxury real estate combines high-consideration decisions, local market expertise, individual-agent entities, brokerage relationships, reputation, and visual evidence. Generic SEO pages often flatten those facts into interchangeable city copy. Our program is designed to preserve the expert, market, and proof relationships that answer engines need to verify. ## What we will and will not promise We promise the work, the measurement, and transparent evidence. We do not promise that ChatGPT, Perplexity, Gemini, Claude, or Google will cite a specific URL on demand. Those systems change independently, so the operating goal is durable source eligibility and a rising rate of verified recognition and citation. ## How to evaluate an AEO provider's evidence Start with the denominator. A headline such as 43 questions won is incomplete unless the provider also shows how many questions were tested, which engines ran, whether the questions were branded, and whether the number describes the newest run or a historical ledger. Then inspect the captures. A model can cite a page without naming the company, name the company without recommending it, or recommend it negatively. Those outcomes require different verdicts. Finally, follow the cited URL and confirm that it supports the published claim. A good evidence record names the prompt, engine, model, date, execution scope, raw answer, cited URLs, verdict rule, position, sentiment, and error state. If a vendor cannot expose customer data publicly, it should still demonstrate the same contract on its own brand or on an approved case. Screenshots can illustrate a result, but retained raw text and stable IDs make the result auditable after the interface changes. - Require the total prompt and engine denominator beside every win count. - Separate branded recognition from non-branded commercial discovery. - Distinguish mentions, recommendations, citations, position, and sentiment. - Ask whether historical wins remain in the current-batch headline. - Verify that every cited customer outcome has publication permission and a source record. ## The entity model luxury real estate requires Luxury real estate visibility rarely belongs to a single website entity. The public expert may be an individual agent or team leader, while the licensed brokerage, team brand, service areas, development specialties, media appearances, awards, and active listings live on different domains. The job is to state those relationships consistently without implying ownership or credentials that do not exist. A canonical expert page should identify the person, role, brokerage relationship, markets, specialties, public contact path, and source-backed proof. The organization page should identify the legal or operating brand and connect only to verified profiles. Market pages should show firsthand knowledge and a relevant conversion path rather than repeat a city name. Structured data can express these visible relationships, but it cannot manufacture authority. The same names, URLs, phone numbers, and descriptions should also appear on Google Business Profile, social and industry profiles, event bios, customer disclosures, and editorial coverage when those sources genuinely exist. - Choose one canonical spelling for the expert, team, brokerage, and operating company. - Document brokerage and service-area relationships instead of leaving them implicit. - Connect only verified public profile URLs in sameAs markup. - Keep awards, transactions, reviews, and media claims bound to their original sources and dates. - Use Person, Organization, Service, Article, and Breadcrumb markup only where the visible page supports it. ## What makes a passage usable in an AI answer A citable passage answers one question completely enough to stand on its own. The first sentence should state the conclusion, and the next sentences should define the scope, evidence, and limitation. A named statistic needs a denominator, measurement date, method, and source link close to the claim. A buying page should include comparison criteria and pricing or an honest explanation of how scope changes cost. A method page should define the unit of analysis and every verdict. A results page should state what changed, how it was measured, and what the observation does not prove. Short paragraphs, question-led headings, semantic lists, and real tables help extraction because they preserve relationships when a system reads only part of the page. Repeating keywords does not make a passage more trustworthy. Specific entities, stable terminology, named authorship, visible review dates, and source proximity do. The goal is a useful answer that remains accurate when quoted outside the visual design. - Lead with a direct conclusion before background or promotional copy. - Place dates, denominators, methods, and limitations beside quantitative claims. - Use real HTML tables for comparisons and evidence matrices. - Write headings in the language a buyer uses when evaluating the service. - Review each passage independently and remove any sentence that depends on an unstated assumption. ## How the prompt panel becomes an operating system The prompt panel should represent decisions the business wants to influence, not a collection of favorable examples. National category prompts test whether the market recognizes the service. Luxury-specialization prompts test category depth. Vendor-comparison prompts expose the shortlist. Problem and solution prompts show whether the brand is useful before a buyer knows which vendor to seek. Geography belongs only where local relevance is intentional. Each prompt runs independently so one conversation does not prime the next answer. The retained record must show failed requests as well as successful ones. After publication, the same version runs again and the analysis compares compatible observations. New questions can enter a research pool, but they should not silently change the scored denominator. The useful management view shows recommendation rate, recognition rate, cited 10xSearch URL, outside citation source, position, sentiment, engine, model, scope, and run date, plus the pages and evidence changes made between runs. - Freeze prompt text and category before the baseline runs. - Store model and execution scope instead of labeling only the consumer product. - Retain failures and do not remove prompts that return unfavorable answers. - Version any change to prompts, aliases, or deterministic verdict logic. - Compare movement only after the changed evidence is publicly accessible and crawlable. ## A practical sequence for the first ninety days The first phase establishes the ruler and removes contradictions. That includes a fixed prompt baseline, crawl and rendering checks, URL consolidation, canonical entity records, analytics, and a claim ledger. The second phase rebuilds the pages closest to a commercial decision with direct answers, evidence tables, source links, authorship, pricing, limitations, and conversion attribution. The third phase strengthens corroboration through legitimate customer disclosures, accurate profiles, reviews, original research, and earned editorial coverage. Prompt runs occur after material work is live, not after every copy edit. Technical corrections may be visible quickly, while citations and recommendations depend on recrawling and source discovery outside the agency's control. A ninety-day plan should therefore specify accountable outputs and measurement dates rather than promise a particular model response. At the end of the cycle, the business should have a cleaner discovery surface, a public evidence asset, comparable prompt receipts, and a prioritized next cycle based on observed gaps. - Days 1 to 14: baseline, crawl inventory, entity record, analytics, and consolidation decisions. - Days 15 to 45: commercial-page rebuild, source ledgers, structured data, and conversion paths. - Days 46 to 75: customer evidence, original-data packaging, review recovery, and editorial outreach. - Days 76 to 90: unchanged-panel rerun, result reconciliation, and next-cycle priorities. - At every phase: preserve raw evidence and keep unsupported outcome claims unpublished. ## What ownership and handoff should include An agency engagement should leave the client with durable assets, not only a dashboard login. The client should know which URLs are canonical, which pages are intentionally noindexed, which redirects preserve old demand, and which public profiles represent the same entity. It should receive the prompt-panel version, definitions, run dates, evidence exports, source ledger, publication permissions, analytics definitions, and a record of material changes. Content should remain editable without depending on an inaccessible vendor mirror. Lead forms and calendar boundaries should preserve campaign context without leaking personal data into analytics events. If the relationship ends, the site, structured content, source records, and measurement history should remain usable. Vendor-specific automation may stop, but the public evidence should not disappear. This ownership standard also improves buying discipline: a client can compare the actual operating system, publishing cadence, accountable outputs, and data access across providers rather than comparing broad claims about proprietary AI. - Confirm who owns the domain, code, content, analytics properties, and profile credentials. - Require exports for prompts, captures, citations, verdicts, and source records. - Document redirect, canonical, noindex, and sitemap decisions. - Keep lead attribution fields and privacy boundaries explicit. - Define what continues, transfers, or stops when the engagement ends. ## Customer evidence ### [10xSearch fixed-panel baseline](https://10xsearch.net/audit/growth-audit-10xsearch-com-2a901b54) **Result:** The first published baseline contains 100 prompt-engine observations, 100 raw hashed captures, 39 responses with at least one cited URL, 0 recommendations, and 0 errors. **Method:** The versioned growth-audit-50.v1 panel submitted 50 fixed national prompts independently to Gemini and Claude. Every observation retained the model, United States API execution scope, raw answer, citations, verdicts, timestamp, and SHA-256 capture hash. **Date:** Measured August 13, 2026 **Limitation:** This is a two-engine point-in-time API baseline. A cited URL does not mean 10xSearch was mentioned or recommended, and consumer interfaces or other locations can return different answers. ### [Doug Leibinger AI visibility](https://10xsearch.net/evidence/ai-visibility/doug-leibinger) **Result:** 40 questions produced strict recognition in the newest August 10 batch. The durable win ledger contains 43 won questions, with 3 older wins labeled historical rather than current. **Method:** 59 questions were checked across six engines. A question counts as current only when at least one engine names Doug in answer prose in the single newest batch. **Date:** Measured August 10, 2026 **Limitation:** The legacy panel did not persist provider-model or execution-location fields. The public asset labels those fields not recorded instead of inferring them. ### [The Kink Team launch phase](https://10xsearch.com/case-studies/10xsearch-gets-you-found-online/) **Result:** The public case study documents AI-answer inclusion and stronger public search surfaces during the first 45 days of the engagement. **Method:** The outcome was checked in public Google, Maps, and AI assistant surfaces during the engagement. **Date:** Case study published May 3, 2026 **Limitation:** The client has not authorized publication of proprietary rankings, traffic, or conversion counts, so the result remains qualitative. ### [Mountain Rose Realty technical rebuild](https://10xsearch.com/case-studies/mountain-rose-realty/) **Result:** The public case study documents a 100 Lighthouse Desktop performance score after the production rebuild and cutover. **Method:** Before and after Lighthouse Desktop captures were taken on the public homepage at migration cutover. URL structure and visible content were preserved. **Date:** Case study published May 3, 2026 **Limitation:** This is a technical performance result. It does not by itself prove traffic, lead, or revenue growth. ## Methodology 1. Start with a fixed prompt panel tied to national discovery, luxury specialization, vendor comparison, and problem-solving intent. 2. Run each prompt independently by engine and preserve the raw answer, cited URLs, model, date, location, and deterministic verdict. 3. Audit the site for crawl access, rendering, entity consistency, structured data, author evidence, source quality, and conversion fit. 4. Prioritize pages and off-site evidence that answer an observed decision gap, then rerun the same panel after the work is live. 5. Report current-batch results separately from historical wins so old recognition never looks current. ## Limitations - No agency controls whether an answer engine cites a specific page on a specific date. - AI answers vary by engine, model, interface, account context, date, and location. - Recognition, recommendation, citation, ranking position, sentiment, traffic, and revenue are separate measures and should not be blended into one score. - Case results show what happened for the named client in the stated window. They are not a guarantee of the same outcome for another business. ## Pricing and buying guidance - **Standard: $2,500 per month.** For a brand with a workable technical foundation that needs the AI visibility audit, schema audit, 60-day asset plan, publishing velocity, and ongoing monitoring. - **Super Affiliate: $3,500 per month.** For a network leader or established brand that also needs a higher-touch entity graph, authority-source expansion, and press-placement scoping. - **Founder: $10,000 upfront, then $2,500 per month.** For a principal who wants maximum founder involvement in positioning, category framing, and competitive response, with the lifetime monthly rate described on the pricing page. ## Commercial questions ### What does an AEO agency for luxury real estate cost? 10xSearch public pricing currently includes Standard at $2,500 per month, Super Affiliate at $3,500 per month, and Founder at $10,000 upfront followed by $2,500 per month. Scope depends on markets, entities, evidence readiness, and publishing volume. ### How long does AEO take? Technical and entity corrections can ship quickly. Recognition and citations depend on recrawling, source discovery, the strength of the evidence, and the answer engine. We measure movement in dated panels instead of promising a fixed ranking date. ### Does AEO replace SEO? No. Search rankings, crawlability, internal links, page experience, and authority sources still matter. AEO adds answer extraction, entity clarity, citation eligibility, and prompt-level measurement. ### Can 10xSearch guarantee ChatGPT recommendations? No. No honest vendor can control a third-party model. 10xSearch can guarantee the agreed work, evidence retention, quality controls, and transparent reporting. ### What should I ask another AEO vendor? Ask for the fixed prompt list, raw captures, engine and model names, run dates, locations, citation URLs, verdict rules, current-versus-historical treatment, pricing, limitations, and a customer reference or public case record. ## Sources - [OpenAI web crawler and user agent documentation](https://developers.openai.com/api/docs/bots): Primary source for OpenAI crawler controls and user agents. - [Google Search guidance for AI features](https://developers.google.com/search/docs/appearance/ai-features): Primary source for how existing Search requirements apply to AI features. - [Google structured data general guidelines](https://developers.google.com/search/docs/appearance/structured-data/sd-policies): Primary source for structured data eligibility and quality rules. - [10xSearch fixed-panel baseline](https://10xsearch.net/audit/growth-audit-10xsearch-com-2a901b54): 10xSearch original data with 50 prompts, 100 raw captures, cited URLs, models, dates, scope, verdicts, and hashes. - [Doug Leibinger prompt-level evidence](https://10xsearch.net/evidence/ai-visibility/doug-leibinger): 10xSearch original data with raw captures and current-versus-historical reconciliation. - [Official Colorado entity record](https://data.colorado.gov/resource/4ykn-tg5h.json?entityid=20261025290): The Colorado Department of State open-data record verifies 10xSearch Inc., entity ID 20261025290, its January 7, 2026 formation date, good-standing status, and Centennial legal office. It verifies legal identity, not service quality or performance. - [OpenGovCO entity record mirror](https://opengovco.com/business/20261025290): OpenGovCO publishes a crawlable third-party directory page derived from the Colorado business-entity dataset. It corroborates the same legal identity and office record, but it is a data mirror rather than an editorial endorsement. - [Inman News exhibitor announcement](https://www.prweb.com/releases/inman-announces-exhibitors-for-inman-connect-san-diego-2026-302827969.html): Inman's July 16, 2026 announcement lists and describes 10xSearch as an Inman Connect San Diego exhibitor. It verifies company and event participation, not performance or an independent endorsement. - [Inman Golden I Club finalist announcement](https://www.inman.com/2026/07/21/inman-announces-the-2026-inman-golden-i-club-finalists/): Inman's editorial team lists 10xSearch as a 2026 Top Luxury Tech/Tool finalist. It verifies third-party industry recognition, not product performance or a customer outcome. - [NAR: Find Your GEO to Land Your Next Referral From AI](https://www.nar.realtor/news/real-estate-news/technology/find-your-geo-to-land-your-next-referral-from-ai): NAR's May 18, 2026 article identifies Rick Janson as the creator of 10X Search, links to 10xsearch.com, and describes the platform's page-evaluation method. Performance statements in the article remain attributed claims, not an independent audit. - [NAR Tech & Innovation: AI Becomes Early Step in Homebuying Journey](https://tech.realtor/2026/07/14/ai-becomes-early-step-in-homebuying-journey/): NAR Tech & Innovation's July 14, 2026 republication identifies Rick Janson as the creator of 10X Search and connects the company to answer engine optimization. It is a second NAR-owned publication surface, not a separate independent endorsement or performance audit. - [Greater Albuquerque Association of REALTORS: AI Becomes Early Step in Homebuying Journey](https://www.gaar.com/blog/article/ai-becomes-early-step-in-homebuying-journey): The Greater Albuquerque Association of REALTORS' July 8, 2026 republication identifies Rick Janson as the creator of 10X Search and connects the company to answer engine optimization. It adds a crawlable association domain for the same underlying NAR reporting, not a separate independent endorsement or performance audit. - [NAR NXT 2026 speaker profile](https://narnxt.realtor/speaker/rick-janson/): NAR NXT's official 2026 speaker profile identifies Rick Janson as the founder of 10xSearch.com and lists his session, How Agents Get Found in AI Search, Google Search and Maps. It verifies the founder connection and subject expertise, not product performance. - [McKissock: How Real Estate Professionals are Using AI in 2026](https://www.mckissock.com/blog/real-estate/how-real-estate-agents-use-ai-webinar/): McKissock's March 6, 2026 editorial recap identifies Rick Janson as founder of 10Xsearch.com and Colibri Real Estate School's resident AI expert. It corroborates identity and industry expertise, not a customer outcome. - [KE Team Hawaii provider disclosure](https://keteamhawaii.com/powered-by): A customer-owned page identifies 10xSearch as the provider behind the site's search infrastructure. It verifies the disclosed relationship, not a neutral review or measured outcome. - [2026 Luxury Presence Visibility Index](https://10xsearch.com/luxury-presence-june-2026/): 10xSearch original cohort research covering 102 luxury real estate portfolios, 2,040 AI answers, aggregate crawl findings, methodology, limitations, and a machine-readable aggregate extract. ## Next step **See your real prompt-level baseline.** We will show what is current, what is historical, what is missing, and which evidence would make the next result more defensible. [Schedule a working session](https://10xsearch.com/schedule/) # Real estate SEO and AI visibility URL: https://10xsearch.com/real-estate-seo/ > Real estate SEO and AI visibility are connected but different jobs. SEO helps a site earn crawlability, indexation, rankings, local discovery, and qualified organic visits. AI visibility work helps answer engines identify the expert, verify claims, extract useful passages, cite source URLs, and decide whether to mention or recommend the business. A credible program uses one evidence system for both: stable technical foundations, distinct market and service pages, named authorship, first-party expertise, corroborating sources, structured data, reviews, and conversion tracking. It also measures rankings, mentions, citations, recommendations, exposed position, sentiment, traffic, leads, and revenue as separate outcomes. 10xSearch applies that model to real estate brands and publishes the prompt-level evidence it is permitted to show. No provider controls a third-party model, so buying decisions should be based on inspectable work, dated measurements, clear attribution rules, and explicit limitations rather than guaranteed rankings or recommendations. By Rick Janson, JD, MBA. Published 2026-06-08. Updated 2026-08-13. Real estate discovery now spans ranked results, Maps, AI Overviews, ChatGPT, Perplexity, Gemini, Claude, and other answer surfaces. Winning requires one coherent evidence system, not separate piles of SEO copy and AI tactics. ## Original data - **100 Lighthouse Desktop performance:** Mountain Rose Realty after the public technical rebuild - **40 current strict AI questions:** Doug Leibinger in the newest August 10 batch - **30 current non-branded questions:** Recognition without the agent's name in the prompt - **45 days documented launch window:** The Kink Team public case narrative ## What belongs in a real estate visibility program The program should connect technical crawl health, local market pages, individual expert entities, brokerage relationships, reviews, transactions that may be discussed publicly, media, community knowledge, and clear conversion paths. - One canonical page per distinct audience and decision, with redirects for overlap. - Fast, renderable pages with stable URLs and honest last-modified dates. - Named authorship and firsthand market evidence, not interchangeable city prose. - Structured data that reflects visible facts and stable entity relationships. - A fixed prompt panel that separates branded recognition from non-branded discovery. ## SEO and AI visibility use the same source truth Search engines and answer engines both benefit when a page clearly identifies who is speaking, what they know, where the evidence came from, when it was measured, and what the evidence cannot prove. That is why our pages carry authorship, sources, method notes, and explicit limits instead of relying on promotional adjectives. ## Avoid the city-page trap A page should survive only when it has a distinct audience, intent, evidence set, and conversion path. A location name alone is not enough. If two pages would cite the same facts and ask for the same action, they should usually consolidate instead of competing with each other. ## How to design a durable market-page architecture A market page earns a separate URL when it answers a distinct set of local decisions with evidence the broader service page cannot carry. Useful inputs include firsthand neighborhood knowledge, local price and inventory context, property types, schools or amenities described through primary sources, public transaction examples the client may discuss, and a conversion path appropriate to that market. The page should identify who is responsible for the advice and how the market relates to the team or brokerage. A city-name substitution is not a content strategy. When two pages serve the same audience, use the same evidence, and ask for the same action, they divide internal links and create ambiguous canonical signals. Consolidation is often the stronger choice. A maintained architecture also records which pages are active, which redirect, which remain available but noindexed, and which evidence must be refreshed. Geography should follow business intent and real expertise rather than expand simply because a keyword tool lists nearby cities. - Define the audience, decision, evidence set, and conversion path before creating the URL. - Use one canonical page when two markets cannot support materially different answers. - Link market pages to the responsible expert and relevant service, not only to each other. - Refresh dated market evidence without rewriting stable explanatory sections unnecessarily. - Exclude markets the business does not intend to serve from the scored AI prompt panel. ## How local evidence supports both search and AI discovery Local relevance is strongest when independent records agree. The site, Google Business Profile, brokerage profile, association record, event biography, customer disclosure, and editorial mention should use compatible names, markets, roles, and URLs. Reviews add a separate layer of experience evidence, but only genuine customer reviews and compliant responses belong in the record. Local pages can cite government, association, school, planning, transportation, and market sources where those sources directly support the answer. They should not turn third-party facts into unsourced promotional claims. Maps eligibility and ranked search visibility depend on Google's own systems, while answer engines may retrieve a different mix of pages. The operating response is to maintain accurate profiles, visible local expertise, crawlable source-linked answers, and prompt measurements that name the intended market. A local win on one interface does not prove national discovery, and a national citation does not prove Maps visibility. Reporting should preserve those distinctions. - Keep name, role, phone, URL, market, and brokerage relationships consistent across public records. - Use primary local sources for factual market statements and show their dates. - Request reviews only from real customers after a reviewable milestone. - Track Maps, organic rankings, AI recognition, citations, and leads as separate measures. - Test location prompts only for markets the client intentionally wants to own. ## The technical foundation that content cannot replace Authoritative copy cannot compensate for a site that blocks crawlers, serves incomplete client-rendered content, changes canonical URLs unpredictably, or publishes thousands of unresolved templates. The technical baseline includes clean response codes, a deliberate robots policy, accurate canonical tags, a sitemap limited to indexable destinations, stable internal links, server-rendered primary content, valid structured data, and usable mobile performance. Redirect chains and soft 404s should be removed. Dates should reflect material updates rather than deployment time. Structured data should describe visible facts and use stable entity identifiers. AI-specific files can make a curated corpus easier to inspect, but they do not replace ordinary crawlability or source quality. The release gate should crawl every discovery URL, verify one intended canonical and robots state, parse each JSON-LD block, follow evidence links, and check the production HTML rather than trusting a build log. A successful HTTP response proves transport, not that the correct content rendered. - Keep the sitemap limited to canonical, indexable, successful destinations. - Render the primary answer and source links in server HTML. - Use redirects for true consolidation and noindex for useful but non-distinct utilities. - Validate structured data against the visible page and stable entity graph. - Repeat production crawl, browser, console, and conversion checks after every release. ## How expert content becomes an evidence system A real estate expert already possesses material that generic content programs miss: repeated client questions, listing and negotiation experience, market tradeoffs, local relationships, and explanations used in appointments. The publishing system should turn that firsthand knowledge into answers while separating confidential details from publishable evidence. Each page needs a named owner, a clear question, a direct answer, supporting sources, a review date, and a reason to exist within the cluster. Original observations should state the sample, window, method, and limitation. Third-party data should link to the originating source. Customer stories should match the permission granted and should not imply metrics the source does not support. A pillar page organizes a decision, while supporting articles answer narrower questions and link back using descriptive context. Updating the source ledger and material findings on a predictable cadence gives search and answer systems a consistent body of evidence instead of a burst of loosely related posts. - Capture expert interviews around real client decisions rather than generic keyword definitions. - Bind every factual claim to firsthand experience, a primary source, or a qualified external source. - State samples, dates, methods, and limitations for original observations. - Use supporting articles to answer narrower questions and link them to the responsible pillar. - Refresh material findings on a scheduled cadence and preserve prior comparable baselines. ## How conversion attribution connects visibility to clients Visibility matters commercially only when the measurement chain continues past the page view. Every authority page should present a clear next action and retain the landing page, referrer, campaign fields, and approved click identifiers when a visitor submits a form or crosses into a scheduling tool. Analytics events should avoid personal data, while the lead record can retain information the visitor intentionally submitted. Calendar embeds and external booking pages need explicit allowlists because query strings can otherwise leak unsupported identifiers or private conversation paths. A qualified pipeline report connects the first known discovery source to the form, booked meeting, sales-qualified opportunity, and closed revenue while documenting the attribution rule. Last-click, first-touch, and influenced-pipeline views answer different questions. None should be inferred from an AI citation alone. If an answer engine cites 10xSearch but no visitor arrives, that is a visibility observation. If a visitor arrives and becomes a client, the durable lead and revenue records are the business evidence. - Persist landing URL, referrer, UTM fields, and approved ad-click identifiers with the lead. - Keep names, emails, phone numbers, and submitted domains out of analytics event parameters. - Pass only documented campaign fields across calendar boundaries. - Define first-touch, last-touch, and influenced-pipeline rules before reporting revenue. - Reconcile form, booking, opportunity, and closed-revenue records instead of stopping at clicks. ## What a useful monthly report should show A monthly report should connect technical health, discoverability, AI evidence, and pipeline without collapsing them into one synthetic success number. Search reporting should include crawl and indexation status, rankings for the intended market and service set, qualified organic sessions, and conversion outcomes. AI reporting should include the fixed prompt version, successful and failed engine checks, recognition and recommendation rates, cited brand URLs, outside citation sources, position, sentiment, model, scope, and run date. Publication reporting should show the pages and external evidence that changed between comparable runs. Pipeline reporting should show qualified forms, booked meetings, opportunities, closed revenue, and the attribution rule. The report should also preserve negative results, stale evidence, and unresolved blockers. A page can improve structurally before models recrawl it, and a citation can rise without producing a lead. The client needs both facts. That separation makes the next decision clearer and prevents a favorable vanity metric from hiding weak commercial performance. - Show prompt and query denominators, not only positive examples. - List material site and evidence changes between measurement dates. - Separate technical completion from recrawl, visibility, and pipeline outcomes. - Carry unresolved failures and evidence limitations into the next reporting period. - Make every headline traceable to a page, capture, analytics record, or CRM record. ## Customer evidence ### [10xSearch fixed-panel baseline](https://10xsearch.net/audit/growth-audit-10xsearch-com-2a901b54) **Result:** The first published baseline contains 100 prompt-engine observations, 100 raw hashed captures, 39 responses with at least one cited URL, 0 recommendations, and 0 errors. **Method:** The versioned growth-audit-50.v1 panel submitted 50 fixed national prompts independently to Gemini and Claude. Every observation retained the model, United States API execution scope, raw answer, citations, verdicts, timestamp, and SHA-256 capture hash. **Date:** Measured August 13, 2026 **Limitation:** This is a two-engine point-in-time API baseline. A cited URL does not mean 10xSearch was mentioned or recommended, and consumer interfaces or other locations can return different answers. ### [Mountain Rose Realty technical rebuild](https://10xsearch.com/case-studies/mountain-rose-realty/) **Result:** The public case study documents a 100 Lighthouse Desktop performance score after the production rebuild and cutover. **Method:** Before and after Lighthouse Desktop captures were taken on the public homepage at migration cutover. URL structure and visible content were preserved. **Date:** Case study published May 3, 2026 **Limitation:** This is a technical performance result. It does not by itself prove traffic, lead, or revenue growth. ### [Doug Leibinger AI visibility](https://10xsearch.net/evidence/ai-visibility/doug-leibinger) **Result:** 40 questions produced strict recognition in the newest August 10 batch. The durable win ledger contains 43 won questions, with 3 older wins labeled historical rather than current. **Method:** 59 questions were checked across six engines. A question counts as current only when at least one engine names Doug in answer prose in the single newest batch. **Date:** Measured August 10, 2026 **Limitation:** The legacy panel did not persist provider-model or execution-location fields. The public asset labels those fields not recorded instead of inferring them. ### [The Kink Team launch phase](https://10xsearch.com/case-studies/10xsearch-gets-you-found-online/) **Result:** The public case study documents AI-answer inclusion and stronger public search surfaces during the first 45 days of the engagement. **Method:** The outcome was checked in public Google, Maps, and AI assistant surfaces during the engagement. **Date:** Case study published May 3, 2026 **Limitation:** The client has not authorized publication of proprietary rankings, traffic, or conversion counts, so the result remains qualitative. ## Methodology 1. Inventory every indexable URL, canonical, redirect, metadata record, and sitemap entry. 2. Group pages by audience, intent, evidence set, and conversion path; merge or noindex pages that cannot defend a separate role. 3. Measure technical performance and rendering on the public production URL. 4. Run a fixed prompt panel that separates branded, non-branded, comparison, problem-solving, and intentional geographic questions. 5. Tie every claimed outcome to a dated source record and keep limitations beside the claim. ## Limitations - No agency controls whether an answer engine cites a specific page on a specific date. - AI answers vary by engine, model, interface, account context, date, and location. - Recognition, recommendation, citation, ranking position, sentiment, traffic, and revenue are separate measures and should not be blended into one score. - Case results show what happened for the named client in the stated window. They are not a guarantee of the same outcome for another business. ## Pricing and buying guidance - **Standard: $2,500 per month.** For a brand with a workable technical foundation that needs the AI visibility audit, schema audit, 60-day asset plan, publishing velocity, and ongoing monitoring. - **Super Affiliate: $3,500 per month.** For a network leader or established brand that also needs a higher-touch entity graph, authority-source expansion, and press-placement scoping. - **Founder: $10,000 upfront, then $2,500 per month.** For a principal who wants maximum founder involvement in positioning, category framing, and competitive response, with the lifetime monthly rate described on the pricing page. ## Commercial questions ### How is real estate SEO different from AEO? SEO improves eligibility and performance in ranked and local search. AEO focuses on whether an answer system can understand, extract, verify, cite, and recommend the expert. The underlying technical and authority work overlaps. ### Do I need a new website? Not always. We first determine whether the current site can support stable rendering, editable structured content, schema, fast pages, analytics, lead routing, and ongoing publication. A rebuild is justified only when the operating limits are material. ### Should every market have its own page? Only when the market has distinct demand, firsthand evidence, useful content, internal relationships, and a relevant conversion path. Thin location swaps create overlap rather than authority. ### What should a real estate SEO agency report? Report crawl and indexing health, query-level rankings, qualified organic traffic, conversions, current AI recognition and citations, page-level evidence, and the exact dates and methods used. ### What does the engagement cost? Public plans currently include Standard at $2,500 per month, Super Affiliate at $3,500 per month, and Founder at $10,000 upfront followed by $2,500 per month. The pricing page explains the scope and fit of each plan. ## Sources - [Google Search Essentials](https://developers.google.com/search/docs/essentials): Primary source for technical requirements, spam policies, and key best practices. - [Google page experience guidance](https://developers.google.com/search/docs/appearance/page-experience): Primary source for page experience and Core Web Vitals context. - [Google AI features and your website](https://developers.google.com/search/docs/appearance/ai-features): Primary source for AI feature eligibility guidance. - [10xSearch fixed-panel baseline](https://10xsearch.net/audit/growth-audit-10xsearch-com-2a901b54): The public national prompt panel and raw answer evidence for 10xSearch.com. - [Mountain Rose Realty case study](https://10xsearch.com/case-studies/mountain-rose-realty/): 10xSearch technical case evidence and reproducibility notes. - [Doug Leibinger prompt-level evidence](https://10xsearch.net/evidence/ai-visibility/doug-leibinger): 10xSearch original prompt-level AI evidence. - [Official Colorado entity record](https://data.colorado.gov/resource/4ykn-tg5h.json?entityid=20261025290): The Colorado Department of State open-data record verifies 10xSearch Inc., entity ID 20261025290, its January 7, 2026 formation date, good-standing status, and Centennial legal office. It verifies legal identity, not service quality or performance. - [OpenGovCO entity record mirror](https://opengovco.com/business/20261025290): OpenGovCO publishes a crawlable third-party directory page derived from the Colorado business-entity dataset. It corroborates the same legal identity and office record, but it is a data mirror rather than an editorial endorsement. - [Inman News exhibitor announcement](https://www.prweb.com/releases/inman-announces-exhibitors-for-inman-connect-san-diego-2026-302827969.html): Inman's July 16, 2026 announcement lists and describes 10xSearch as an Inman Connect San Diego exhibitor. It verifies company and event participation, not performance or an independent endorsement. - [Inman Golden I Club finalist announcement](https://www.inman.com/2026/07/21/inman-announces-the-2026-inman-golden-i-club-finalists/): Inman's editorial team lists 10xSearch as a 2026 Top Luxury Tech/Tool finalist. It verifies third-party industry recognition, not product performance or a customer outcome. - [NAR: Find Your GEO to Land Your Next Referral From AI](https://www.nar.realtor/news/real-estate-news/technology/find-your-geo-to-land-your-next-referral-from-ai): NAR's May 18, 2026 article identifies Rick Janson as the creator of 10X Search, links to 10xsearch.com, and describes the platform's page-evaluation method. Performance statements in the article remain attributed claims, not an independent audit. - [NAR Tech & Innovation: AI Becomes Early Step in Homebuying Journey](https://tech.realtor/2026/07/14/ai-becomes-early-step-in-homebuying-journey/): NAR Tech & Innovation's July 14, 2026 republication identifies Rick Janson as the creator of 10X Search and connects the company to answer engine optimization. It is a second NAR-owned publication surface, not a separate independent endorsement or performance audit. - [Greater Albuquerque Association of REALTORS: AI Becomes Early Step in Homebuying Journey](https://www.gaar.com/blog/article/ai-becomes-early-step-in-homebuying-journey): The Greater Albuquerque Association of REALTORS' July 8, 2026 republication identifies Rick Janson as the creator of 10X Search and connects the company to answer engine optimization. It adds a crawlable association domain for the same underlying NAR reporting, not a separate independent endorsement or performance audit. - [NAR NXT 2026 speaker profile](https://narnxt.realtor/speaker/rick-janson/): NAR NXT's official 2026 speaker profile identifies Rick Janson as the founder of 10xSearch.com and lists his session, How Agents Get Found in AI Search, Google Search and Maps. It verifies the founder connection and subject expertise, not product performance. - [McKissock: How Real Estate Professionals are Using AI in 2026](https://www.mckissock.com/blog/real-estate/how-real-estate-agents-use-ai-webinar/): McKissock's March 6, 2026 editorial recap identifies Rick Janson as founder of 10Xsearch.com and Colibri Real Estate School's resident AI expert. It corroborates identity and industry expertise, not a customer outcome. - [KE Team Hawaii provider disclosure](https://keteamhawaii.com/powered-by): A customer-owned page identifies 10xSearch as the provider behind the site's search infrastructure. It verifies the disclosed relationship, not a neutral review or measured outcome. - [2026 Luxury Presence Visibility Index](https://10xsearch.com/luxury-presence-june-2026/): 10xSearch original cohort research covering 102 luxury real estate portfolios, 2,040 AI answers, aggregate crawl findings, methodology, limitations, and a machine-readable aggregate extract. ## Next step **Build one visibility system, not two disconnected campaigns.** Start with a technical, content, entity, and prompt baseline tied to the markets and decisions you actually want to win. [Schedule a working session](https://10xsearch.com/schedule/) # AI visibility audit methodology URL: https://10xsearch.com/ai-visibility-audit-methodology/ > A credible AI visibility audit uses a frozen, versioned prompt panel and retains enough evidence for another analyst to independently reproduce every headline. The 10xSearch growth-audit-50.v1 panel contains 50 fixed prompts: 14 national category prompts, 12 luxury real estate specialization prompts, 12 competitive vendor prompts, and 12 problem and solution prompts. Geography is disabled unless the business intentionally wants local relevance. Each engine check stores the prompt, engine, provider model, execution scope, run date, timestamp, raw answer, cited URLs, mention and recommendation verdicts, exposed position, sentiment, provider request ID when available, error state, and SHA-256 capture hash. The first 10xSearch run produced 100 captures across Gemini and Claude with zero errors, 39 responses containing cited URLs, and zero mentions or recommendations of 10xSearch. Recognition, citation, recommendation, ranking, traffic, leads, and revenue remain separate measures. By Rick Janson, JD, MBA. Published 2026-08-13. Updated 2026-08-13. An AI visibility audit is credible only when another analyst can inspect the ruler. This is the ruler 10xSearch uses for its new native audits, including the fixed panel, tracked fields, raw evidence, verdict rules, and limitations. ## Original data - **50 fixed prompts:** Versioned as growth-audit-50.v1 - **2 engines in the first live run:** Gemini and Claude, named with their provider models - **100 raw hashed receipts:** One retained observation for every prompt-engine check - **39 responses with cited URLs:** Tracked separately from mentions and recommendations ## The fixed prompt panel The prompt list is deterministic and versioned. A model does not invent the questions during the audit. That makes repeated runs comparable and stops one favorable or accidental prompt from becoming the benchmark. - National category discovery: 14 prompts - Luxury real estate specialization: 12 prompts - Competitive and vendor comparisons: 12 prompts - Problem and solution research: 12 prompts - Intentional geography: zero for 10xSearch; four prompts replace problem prompts only when local relevance is deliberate ## Fields retained for every check Each prompt-engine result retains prompt ID, category, prompt text, engine, model, execution location, market scope, run date, timestamp, mention, recommendation, exposed position, sentiment, cited URLs, raw answer, provider request ID when available, error, and a SHA-256 hash of the capture. ## How recommendation rate is calculated Recommendation rate equals successful responses that pass the deterministic recommendation verdict divided by all successful prompt-engine responses. Errors stay visible and do not enter the successful denominator. Recognition, citation, position, and sentiment remain separate fields. ## A self-audit that changed this site On August 13 we crawled our own public site and found 402 sitemap URLs, 49 template pages visibly publishing an internal outline label, 363 non-blog URLs stamped with the current date, and only 16 of 402 sitemap URLs with external source links beyond shared navigation. Those findings triggered this consolidation, noindex, durable last-modified, and authority-page rebuild. ## The first published 10xSearch baseline The August 13 production run submitted all 50 fixed prompts to Gemini and Claude, yielding 100 raw hashed captures with zero errors. Thirty-nine responses included one or more cited URLs, but none mentioned or recommended 10xSearch. That is the baseline to beat, not a favorable one-off query. ## How prompts enter and leave a scored panel Prompt research and scored measurement are separate activities. Candidate questions can come from customer interviews, sales calls, search queries, competitor comparisons, product questions, and exploratory model sessions. Before scoring, each candidate is assigned to an intent family, checked for duplicate meaning, reviewed for accidental brand or location bias, and written so one engine run does not depend on prior conversation. The approved list is frozen with a panel ID. A prompt can be retired when the commercial decision disappears, wording proves ambiguous, or the business changes scope, but that change creates a new panel version. Historical results remain attached to the version that produced them. A bridge analysis may run the old and new panels in parallel, but the report must not blend their denominators silently. This discipline prevents a favorable question from entering after the baseline or an unfavorable question from vanishing before the comparison. It also makes the panel a durable operating asset rather than a one-time demonstration. - Maintain a research pool outside the scored panel. - Assign every prompt a stable ID, intent family, text, market scope, and inclusion rationale. - Review branded wording, geographic assumptions, and semantic duplicates before freezing the list. - Create a new version for additions, removals, wording changes, or category changes. - Use bridge runs when a business needs continuity across materially different panel versions. ## The raw-capture and provenance contract Every prompt-engine observation needs enough provenance to distinguish what the provider returned from what the audit later inferred. The request record identifies the prompt, engine, provider model, execution scope, timestamp, and provider request ID when one exists. The response record retains the unedited answer text, cited URLs, completion or error state, and a cryptographic hash of the capture. Deterministic processing adds mention, recommendation, position, sentiment, and citation-host fields without overwriting the raw response. The run record freezes the panel version, subject identity, aliases, market scope, start and completion times, expected observation count, and artifact hash. Public reports may redact protected provider metadata or client-confidential content, but the durable database receipt must keep the internal evidence needed for audit and retry. A screenshot is useful visual corroboration, yet it cannot replace text that can be hashed, searched, reconciled, and compared across interfaces. - Store raw provider output before applying verdict logic. - Keep request identity, response identity, and derived verdicts in separate fields. - Hash the retained capture and the complete public artifact. - Record expected and actual observation counts so missing cells cannot disappear. - Publish redaction and omission rules when the public artifact is narrower than the durable receipt. ## Definitions for recognition, recommendation, citation, position, and sentiment Recognition means the answer prose identifies the audited subject or an approved alias. A URL-only match does not become recognition unless the methodology explicitly defines and labels that broader rule. Recommendation requires language that presents the subject as a suitable choice for the prompt's decision, not merely as an example or source. Citation records the URLs exposed by the engine, whether or not those URLs belong to the subject. Position describes where the first qualifying recognition or recommendation appears in the ordered answer. Sentiment classifies the immediate context as positive, neutral, mixed, or negative under a published rule. These dimensions can diverge. An answer may cite a 10xSearch page but recommend another provider, mention 10xSearch negatively, or name the company after several competitors. The report should preserve each outcome separately. Deterministic rules make large panels repeatable; sampled human review checks whether those rules still match the intended commercial meaning. - Publish approved subject names and aliases with the run. - Do not count a source URL as prose recognition by default. - Require recommendation language to match the commercial intent of the prompt. - Capture the first qualifying position and the surrounding sentiment. - Sample positive and negative verdicts for human quality review after logic changes. ## How errors, retries, and missing observations are handled A completed run is not merely a report page with a score. It has an expected observation count equal to prompts multiplied by configured engines, and every expected cell reaches a terminal success or terminal error state. Timeouts, provider refusals, rate limits, malformed responses, and parser failures remain visible. A retry creates a traceable attempt and does not erase the earlier failure. If the provider returns a valid answer without citations, that is a successful uncited observation, not an error. If an engine is unavailable for a material share of the panel, the report should disclose the coverage gap and avoid comparing the partial result to a complete baseline as if the denominators matched. Publication waits for the durable run and observation records, not only for an in-memory task to finish. This distinction matters because a clean-looking percentage can hide missing rows. Reconciliation checks expected cells, unique prompt-engine keys, raw captures, hashes, and artifact contents before the run becomes a public baseline. - Compute the expected prompt-engine matrix before requests begin. - Persist every attempt, terminal error, and successful uncited answer. - Prevent duplicate attempts from inflating the scored denominator. - Disclose material provider coverage gaps beside any comparison. - Reconcile database rows, hashes, and the rendered artifact before publication. ## How before-and-after comparisons remain compatible A defensible comparison keeps the subject identity, panel version, aliases, verdict contract, engine, model family where practical, and execution scope visible for both periods. Some provider drift is unavoidable because models change, so the report should name that change instead of presenting the second run as a controlled laboratory experiment. The before state is frozen and retained. The after state runs only after material work is live and accessible. Results compare counts and rates using the same successful-denominator rule, while errors and engine coverage appear alongside the headline. Page changes, new external sources, profile corrections, and publication dates form an intervention ledger; they show what happened between runs without proving that any one change caused a model response. A bridge analysis is required when the prompt panel or verdict logic changes. Traffic and pipeline outcomes use their own comparable windows and attribution definitions. This approach supports operational learning while avoiding stronger causal claims than the evidence allows. - Freeze the baseline artifact and never regenerate it in place. - Record material site, source, profile, and measurement changes between runs. - Compare compatible prompt, engine, scope, and verdict dimensions explicitly. - Name provider-model drift and denominator differences instead of hiding them. - Treat observed movement as evidence for prioritization, not automatic proof of causation. ## Public evidence, client confidentiality, and retention Transparency does not authorize disclosure of client-confidential information. The measurement contract begins by defining which subject names, prompts, answers, citations, screenshots, performance metrics, and customer statements may be public. A public evidence asset can expose the prompt panel, dated verdicts, cited URLs, hashes, and redacted captures while the service-role receipt retains protected details. Redaction must be consistent and disclosed; it cannot remove unfavorable observations or alter the scored denominator. Customer outcomes are published only at the level supported by the source and permission. A live-site credit verifies a relationship, not performance. An approved qualitative case verifies the described public observation, not private traffic or revenue. Retention policies should preserve immutable baselines, versioned logic, raw receipts, and publication permissions long enough to audit comparisons. Deletion or access restrictions should follow contractual and legal requirements. The result is an evidence surface that is useful to buyers and answer engines without turning operational data into an uncontrolled public dump. - Document public, client-only, and service-role-only fields before the first run. - Keep redactions visible in the methodology and consistent across observations. - Never remove negative evidence to make a public result look stronger. - Bind each customer statement and metric to current publication permission. - Retain immutable baseline artifacts, logic versions, hashes, and approval records. ## How audit findings become remediation priorities The audit should produce a decision ledger, not a generic checklist. Each finding identifies the affected audience and prompt family, the page or external evidence involved, the observed failure, the proposed change, the responsible owner, the release gate, and the measurement date. Technical blocks and contradictory identity records come first because they can invalidate every downstream page. Next come overlapping commercial URLs, missing source ledgers, weak direct answers, and unsupported claims. External profiles, reviews, original research, and earned coverage follow on their real timelines rather than being represented as completed site work. Expected impact is a prioritization hypothesis, not a guaranteed score gain. After a change ships, production proof confirms the intended HTML, schema, links, analytics, and robots state. Only the unchanged prompt panel can show whether answer visibility moved, and only persisted lead and revenue records can show whether the movement produced clients. - Bind every finding to a page, prompt family, evidence gap, owner, and verification date. - Repair crawl, identity, and canonical contradictions before increasing publication volume. - Separate site-controlled work from authenticated profiles and third-party editorial outcomes. - Verify the live release before rerunning the unchanged measurement panel. - Use observed prompt and pipeline movement to reprioritize the next cycle. ## Customer evidence ### [10xSearch fixed-panel baseline](https://10xsearch.net/audit/growth-audit-10xsearch-com-2a901b54) **Result:** The first published baseline contains 100 prompt-engine observations, 100 raw hashed captures, 39 responses with at least one cited URL, 0 recommendations, and 0 errors. **Method:** The versioned growth-audit-50.v1 panel submitted 50 fixed national prompts independently to Gemini and Claude. Every observation retained the model, United States API execution scope, raw answer, citations, verdicts, timestamp, and SHA-256 capture hash. **Date:** Measured August 13, 2026 **Limitation:** This is a two-engine point-in-time API baseline. A cited URL does not mean 10xSearch was mentioned or recommended, and consumer interfaces or other locations can return different answers. ### [Doug Leibinger AI visibility](https://10xsearch.net/evidence/ai-visibility/doug-leibinger) **Result:** 40 questions produced strict recognition in the newest August 10 batch. The durable win ledger contains 43 won questions, with 3 older wins labeled historical rather than current. **Method:** 59 questions were checked across six engines. A question counts as current only when at least one engine names Doug in answer prose in the single newest batch. **Date:** Measured August 10, 2026 **Limitation:** The legacy panel did not persist provider-model or execution-location fields. The public asset labels those fields not recorded instead of inferring them. ## Methodology 1. Define the business category, intended audience, deliberate geographic scope, and name aliases from source evidence. 2. Freeze the prompt-panel version before any provider request runs. 3. Submit each prompt independently to each configured engine and retain the raw provider response. 4. Apply deterministic name, negative-signal, position, sentiment, and citation extraction rules. 5. Persist the run and each prompt-engine observation in service-role-only tables before treating the report as fulfilled. 6. Render methodology, limitations, prompts, captures, citations, and hashes into the report so the public number can be checked. 7. Repeat the same panel after material work is live, and compare only compatible panel versions and verdict contracts. ## Limitations - No agency controls whether an answer engine cites a specific page on a specific date. - AI answers vary by engine, model, interface, account context, date, and location. - Recognition, recommendation, citation, ranking position, sentiment, traffic, and revenue are separate measures and should not be blended into one score. - Case results show what happened for the named client in the stated window. They are not a guarantee of the same outcome for another business. - API answers may differ from a consumer product interface, especially when the consumer product uses account history, personalization, or a different retrieval layer. - A deterministic text verdict can be audited, but it is still an operational classification rather than access to the provider's internal ranking logic. - Comparisons across panel versions require an explicit bridge analysis. The audit does not silently blend incompatible baselines. ## Pricing and buying guidance - **Standard: $2,500 per month.** For a brand with a workable technical foundation that needs the AI visibility audit, schema audit, 60-day asset plan, publishing velocity, and ongoing monitoring. - **Super Affiliate: $3,500 per month.** For a network leader or established brand that also needs a higher-touch entity graph, authority-source expansion, and press-placement scoping. - **Founder: $10,000 upfront, then $2,500 per month.** For a principal who wants maximum founder involvement in positioning, category framing, and competitive response, with the lifetime monthly rate described on the pricing page. ## Commercial questions ### Why use 50 prompts? A 50-prompt panel is large enough to cover several commercial decision families without letting one prompt dominate the result, while remaining small enough to rerun consistently and inspect at the raw-answer level. ### Why not generate prompts with AI for every audit? Generated prompts can improve discovery, but they weaken repeatability. The scored panel is fixed. New candidate prompts can be researched separately and admitted only through a versioned panel change. ### Does a citation count as a recommendation? No. A source URL can be cited without the business being recommended. Citation, strict recognition, recommendation, position, and sentiment are tracked separately. ### How is location handled? The audit stores prompt market and execution location separately. A city appears in the scored panel only when the business deliberately wants that local market. For 10xSearch, Chicago is not a primary scored market. ### Can I get the raw data? Yes. New reports render the raw captures and preserve database receipts. Approved public case assets can also expose machine-readable JSON. ## Sources - [OpenAI crawler documentation](https://developers.openai.com/api/docs/bots): Primary source for crawler identities and controls. - [Google AI features guidance](https://developers.google.com/search/docs/appearance/ai-features): Primary source for eligibility and site guidance. - [Google sitemap lastmod guidance](https://developers.google.com/search/docs/crawling-indexing/sitemaps/build-sitemap): Primary source for using accurate last modification dates. - [10xSearch fixed-panel baseline](https://10xsearch.net/audit/growth-audit-10xsearch-com-2a901b54): The live growth-audit-50.v1 run with prompts, raw responses, cited URLs, models, dates, scope, and verdicts. - [2026 Luxury Presence Visibility Index](https://10xsearch.com/luxury-presence-june-2026/): 10xSearch original cohort research covering 102 luxury real estate portfolios, 2,040 AI answers, aggregate crawl findings, methodology, limitations, and a machine-readable aggregate extract. - [Doug Leibinger raw evidence asset](https://10xsearch.net/evidence/ai-visibility/doug-leibinger): Example of current-versus-historical reconciliation and raw capture publication. - [Official Colorado entity record](https://data.colorado.gov/resource/4ykn-tg5h.json?entityid=20261025290): The Colorado Department of State open-data record verifies 10xSearch Inc., entity ID 20261025290, its January 7, 2026 formation date, good-standing status, and Centennial legal office. It verifies legal identity, not service quality or performance. - [OpenGovCO entity record mirror](https://opengovco.com/business/20261025290): OpenGovCO publishes a crawlable third-party directory page derived from the Colorado business-entity dataset. It corroborates the same legal identity and office record, but it is a data mirror rather than an editorial endorsement. - [Inman Golden I Club finalist announcement](https://www.inman.com/2026/07/21/inman-announces-the-2026-inman-golden-i-club-finalists/): Inman's editorial team lists 10xSearch as a 2026 Top Luxury Tech/Tool finalist. It verifies third-party industry recognition, not product performance or a customer outcome. - [NAR: Find Your GEO to Land Your Next Referral From AI](https://www.nar.realtor/news/real-estate-news/technology/find-your-geo-to-land-your-next-referral-from-ai): NAR's May 18, 2026 article identifies Rick Janson as the creator of 10X Search, links to 10xsearch.com, and describes the platform's page-evaluation method. Performance statements in the article remain attributed claims, not an independent audit. - [NAR Tech & Innovation: AI Becomes Early Step in Homebuying Journey](https://tech.realtor/2026/07/14/ai-becomes-early-step-in-homebuying-journey/): NAR Tech & Innovation's July 14, 2026 republication identifies Rick Janson as the creator of 10X Search and connects the company to answer engine optimization. It is a second NAR-owned publication surface, not a separate independent endorsement or performance audit. - [Greater Albuquerque Association of REALTORS: AI Becomes Early Step in Homebuying Journey](https://www.gaar.com/blog/article/ai-becomes-early-step-in-homebuying-journey): The Greater Albuquerque Association of REALTORS' July 8, 2026 republication identifies Rick Janson as the creator of 10X Search and connects the company to answer engine optimization. It adds a crawlable association domain for the same underlying NAR reporting, not a separate independent endorsement or performance audit. - [NAR NXT 2026 speaker profile](https://narnxt.realtor/speaker/rick-janson/): NAR NXT's official 2026 speaker profile identifies Rick Janson as the founder of 10xSearch.com and lists his session, How Agents Get Found in AI Search, Google Search and Maps. It verifies the founder connection and subject expertise, not product performance. - [McKissock: How Real Estate Professionals are Using AI in 2026](https://www.mckissock.com/blog/real-estate/how-real-estate-agents-use-ai-webinar/): McKissock's March 6, 2026 editorial recap identifies Rick Janson as founder of 10Xsearch.com and Colibri Real Estate School's resident AI expert. It corroborates identity and industry expertise, not a customer outcome. ## Next step **Audit the questions you intend to win.** Use a stable panel, keep the raw receipts, and make every headline traceable to a dated observation. [Schedule a working session](https://10xsearch.com/schedule/) # 10xSearch results and case studies URL: https://10xsearch.com/results/ > The strongest current public 10xSearch AI result is Doug Leibinger: 40 strict recognition questions in the newest August 10 scan batch, including 30 non-branded questions. The public 43-question ledger is reconciled as 40 current questions plus three older wins, and the underlying answer captures can be inspected. The strongest public technical result is Mountain Rose Realty's documented 100 Lighthouse Desktop performance score after its site rebuild. The Kink Team case records public search and AI-answer inclusion during the first 45 days, but the client has not authorized proprietary rankings, traffic, or conversion counts. These are different evidence types and should not be blended into one lift claim. AI recognition does not prove leads, technical performance does not prove revenue, and a client case does not guarantee another client's outcome. Evaluate the dated method, source, denominator, limitation, price, and conversion path for each result before buying. By Rick Janson, JD, MBA. Published 2026-08-13. Updated 2026-08-13. This page separates current measured outcomes, historical evidence, technical case records, and qualitative customer stories. Every number has a date and method. Every case states what it cannot prove. ## Original data - **40 current strict AI questions:** Doug Leibinger, newest batch - **30 current non-branded questions:** Doug's name absent from the question - **3 historical carry-forward questions:** Shown separately, never labeled current - **100 Lighthouse Desktop performance:** Mountain Rose Realty post-rebuild case record ## What our own baseline says On August 13 the fixed 50-prompt panel produced 100 raw observations across Gemini and Claude. Thirty-nine responses carried cited URLs, but 10xSearch received zero mentions and zero recommendations. The run completed with zero errors. We publish the miss because it is the honest starting point for measuring whether this work improves real visibility. ## How to read these results A case result is useful when its unit, date, denominator, source, and limitation are visible. AI question wins do not equal leads. Lighthouse performance does not equal revenue. Qualitative public-surface inclusion does not equal a proprietary traffic lift. We publish each result at the level the evidence supports. ## Why the scoreboard changed The earlier scoreboard treated 43 won ledger questions as one current headline and used the phrase 0 to 43. The public evidence now shows 40 current strict questions and 3 historical carry-forward questions. It also declines to claim a comparable zero baseline because the April scan used an older recognition contract. ## What customer evidence we still need Public AI recognition and technical performance are useful, but a complete business case should also include qualified lead volume, conversion rate, closed revenue, cost, attribution rules, and a comparable pre-period. We will add those only when the source data and publication permission support them. ## What outside sources currently verify Inman's editorial site lists 10xSearch as a 2026 Top Luxury Tech/Tool finalist. A National Association of REALTORS article names Rick Janson as the creator of 10X Search, links to the company, and describes its page-evaluation system. NAR NXT and McKissock independently identify him as 10xSearch's founder and a real-estate AI educator. A separate Inman announcement distributed through PRWeb verifies event participation, while KE Team Hawaii discloses a customer-provider relationship. These sources corroborate the company, founder connection, public method, recognition, subject expertise, and disclosed relationship. They are not independent performance audits and do not prove traffic, leads, or revenue. ## The evidence ladder behind a case-study claim Case evidence becomes stronger as the observation moves from a promotional statement to an independently inspectable record. The first level is a named customer statement or public provider disclosure. It verifies the relationship and the customer's reported experience, but not an independently measured outcome. The next level is a public technical or search observation with a named tool, URL, date, and reproducible method. Prompt-level AI evidence adds the exact question, engine, model, answer, cited URLs, verdict, and capture hash. Commercial evidence adds comparable lead, opportunity, and revenue records with an attribution rule. Independent editorial or third-party analysis can corroborate the company, method, or result, depending on what the source actually reviewed. No single level should borrow authority from another. A customer-site credit cannot prove revenue, and an award cannot prove a product outcome. The case page should state the highest supported level, link the source, and identify the evidence still missing. - Relationship evidence: named customer statement, public disclosure, or live-site credit. - Technical evidence: public URL, named tool, device or scope, date, and reproducible capture. - AI evidence: prompt, engine, model, raw answer, citations, verdict, and hash. - Commercial evidence: comparable lead, opportunity, revenue, cost, and attribution records. - Independent corroboration: an outside source limited to the facts it actually reviewed. ## How a before-and-after result is designed A useful before-and-after comparison freezes the baseline before the intervention and preserves both states. For a technical case, that means the same public URL, tool, device profile, test settings, and a clear capture window. For search visibility, it means the same query set, market, interface or API scope, and measurement method. For AI visibility, it means the same panel version, verdict logic, engine coverage, and successful-denominator rule. For pipeline, it means comparable dates, qualification definitions, attribution logic, and seasonality notes. The intervention ledger records material site, content, profile, source, and campaign changes between measurements. That ledger improves interpretation but does not automatically prove which change caused the outcome. If a baseline is unavailable or incompatible, the case must say so and publish a narrower current-state observation. A favorable after screenshot without the comparable before state is evidence of current visibility, not evidence of lift. - Capture and retain the baseline before implementation begins. - Use the same unit, scope, settings, and denominator in the comparison. - Record interventions and external changes between the two dates. - Disclose missing, partial, or incompatible baseline evidence. - Avoid causal language when the design supports only an observed association. ## Why current, historical, and cumulative wins stay separate AI answers vary over time, so a result ledger needs more than one time dimension. Current strict recognition describes questions that qualify in the newest comparable batch. Historical wins describe questions that qualified in an older batch but not the newest one. A cumulative ledger can show every question that has ever qualified, but it must not use that total as the current recommendation rate. The Doug Leibinger evidence illustrates the distinction: 40 questions qualify in the newest August 10 batch, while three additional questions are older wins. The 43-question ledger remains useful history when those rows retain dates and captures. The current headline is 40, not 43. A compatible baseline also matters. If an older scan used different prompts, engines, or verdict logic, the page should not imply a clean zero-to-current lift. This reconciliation prevents ordinary model volatility from becoming permanent marketing inflation and keeps future regressions visible. - Current: qualifying questions in the single newest compatible batch. - Historical: older qualifying questions that do not qualify in the newest batch. - Cumulative: every distinct question that has ever qualified, always labeled as a ledger total. - Baseline: an immutable earlier batch comparable under the published contract. - Regression: a prior current win absent from the newest compatible batch. ## How customer permissions shape the public result A public case study should never outrun the client's permission or the source data. Permission can cover the customer name, logo, relationship, site URL, screenshots, prompt answers, traffic, lead counts, revenue, testimonial language, and publication duration separately. If the client authorizes only a qualitative account, the case can describe public inclusion and the work performed but cannot fill the gaps with estimated numbers. If the client permits aggregate reporting but not identification, the record should stay anonymous and avoid combinations of facts that reveal the subject. A current permission record should be connected to each public claim because authorization can change. 10xSearch uses narrow claims on the Kink Team case because proprietary rankings, traffic, and conversion counts are not authorized for publication. That makes the case less dramatic, but more reliable. Evidence discipline protects the client and keeps marketing copy from becoming a conflicting source that answer engines repeat. - Record permission separately for identity, logo, URLs, screenshots, metrics, quotes, and duration. - Publish only the narrowest claim supported by both evidence and current authorization. - Keep unapproved clients anonymous and remove identifying combinations of facts. - Recheck permission before republishing a result in a new report or channel. - Remove or qualify a claim when the source or permission no longer supports it. ## How visibility evidence connects to pipeline evidence Prompt recognition and citations are leading visibility indicators, not customer outcomes. The commercial chain begins when a visitor reaches a tracked page or identifies the source during a form submission. The lead record should retain the landing page, referrer, campaign fields, and approved click identifiers. A scheduling event becomes stronger evidence when it links to the persisted lead rather than existing only as an analytics click. Sales qualification, opportunity creation, and closed revenue each add a durable downstream state. The report must name its attribution rule because first-touch, last-touch, self-reported, and influenced-pipeline views can assign the same client differently. Direct traffic and dark social can also obscure the first exposure. When the chain is incomplete, the case should stop at the last verified stage. A prompt win with no visit remains an AI visibility result. A booked call with an AEO-page first touch is a pipeline result. A closed client with reconciled CRM and billing records is the business outcome. - Keep prompt observations, web sessions, leads, bookings, opportunities, and revenue as linked but distinct records. - Persist source context when the visitor submits, not only in a transient browser event. - Use documented qualification and attribution rules for every pipeline headline. - Reconcile CRM and billing evidence before labeling revenue closed. - Stop the claim at the last durable stage when later links are missing. ## What a buyer should compare across case studies Compare the decision relevance and evidence quality before comparing the size of the number. A national brokerage, a luxury agent, and a local team may have different markets, entity structures, publishing permissions, and conversion cycles. Check whether the case matches your problem, whether the baseline is compatible, which work the vendor controlled, and which external factors changed. Follow the public sources and inspect whether the page names the tool, prompt set, engine, model, location, dates, denominator, and limitation. Then compare scope, responsible team, content ownership, technical ownership, measurement access, reporting cadence, pricing, contract term, and exit rights. A small reproducible result may be more useful than a large percentage without a denominator. Ask for references or private evidence where public disclosure is limited, but do not accept confidentiality as a reason to provide no inspectable method at all. The provider's own honest baseline is also revealing because it shows whether the same standards apply to its marketing. - Match the case's market, entity type, starting condition, and objective to your situation. - Inspect dates, denominators, methods, sources, limitations, and publication permissions. - Separate controlled work from recrawling, model changes, seasonality, and other external factors. - Compare asset ownership, data access, responsible team, cadence, price, term, and exit rights. - Prefer narrow reproducible evidence over an unsupported universal lift claim. ## Customer evidence ### [10xSearch fixed-panel baseline](https://10xsearch.net/audit/growth-audit-10xsearch-com-2a901b54) **Result:** The first published baseline contains 100 prompt-engine observations, 100 raw hashed captures, 39 responses with at least one cited URL, 0 recommendations, and 0 errors. **Method:** The versioned growth-audit-50.v1 panel submitted 50 fixed national prompts independently to Gemini and Claude. Every observation retained the model, United States API execution scope, raw answer, citations, verdicts, timestamp, and SHA-256 capture hash. **Date:** Measured August 13, 2026 **Limitation:** This is a two-engine point-in-time API baseline. A cited URL does not mean 10xSearch was mentioned or recommended, and consumer interfaces or other locations can return different answers. ### [Doug Leibinger AI visibility](https://10xsearch.net/evidence/ai-visibility/doug-leibinger) **Result:** 40 questions produced strict recognition in the newest August 10 batch. The durable win ledger contains 43 won questions, with 3 older wins labeled historical rather than current. **Method:** 59 questions were checked across six engines. A question counts as current only when at least one engine names Doug in answer prose in the single newest batch. **Date:** Measured August 10, 2026 **Limitation:** The legacy panel did not persist provider-model or execution-location fields. The public asset labels those fields not recorded instead of inferring them. ### [Mountain Rose Realty technical rebuild](https://10xsearch.com/case-studies/mountain-rose-realty/) **Result:** The public case study documents a 100 Lighthouse Desktop performance score after the production rebuild and cutover. **Method:** Before and after Lighthouse Desktop captures were taken on the public homepage at migration cutover. URL structure and visible content were preserved. **Date:** Case study published May 3, 2026 **Limitation:** This is a technical performance result. It does not by itself prove traffic, lead, or revenue growth. ### [The Kink Team launch phase](https://10xsearch.com/case-studies/10xsearch-gets-you-found-online/) **Result:** The public case study documents AI-answer inclusion and stronger public search surfaces during the first 45 days of the engagement. **Method:** The outcome was checked in public Google, Maps, and AI assistant surfaces during the engagement. **Date:** Case study published May 3, 2026 **Limitation:** The client has not authorized publication of proprietary rankings, traffic, or conversion counts, so the result remains qualitative. ## Methodology 1. Use the newest single scan batch for current AI recognition claims. 2. Require strict prose recognition; URL-only matches do not become recognition wins. 3. Label older positive batches historical and show their evidence date. 4. For technical results, name the tool, device profile, public URL, and capture window. 5. For qualitative results, state that no proprietary numeric outcome is being claimed. 6. Publish customer names only when the current publicity permission allows it. ## Limitations - No agency controls whether an answer engine cites a specific page on a specific date. - AI answers vary by engine, model, interface, account context, date, and location. - Recognition, recommendation, citation, ranking position, sentiment, traffic, and revenue are separate measures and should not be blended into one score. - Case results show what happened for the named client in the stated window. They are not a guarantee of the same outcome for another business. ## Pricing and buying guidance - **Standard: $2,500 per month.** For a brand with a workable technical foundation that needs the AI visibility audit, schema audit, 60-day asset plan, publishing velocity, and ongoing monitoring. - **Super Affiliate: $3,500 per month.** For a network leader or established brand that also needs a higher-touch entity graph, authority-source expansion, and press-placement scoping. - **Founder: $10,000 upfront, then $2,500 per month.** For a principal who wants maximum founder involvement in positioning, category framing, and competitive response, with the lifetime monthly rate described on the pricing page. ## Commercial questions ### Are these results guaranteed? No. They are dated records from specific clients, sites, panels, and measurement windows. ### Can I inspect the 43 Doug Leibinger questions? Yes. The public evidence asset shows the full ledger reconciliation, prompt list, engines, dates, raw answers, citations, and durable scan IDs. It labels 40 current and 3 historical. ### Why are some case results qualitative? The source or publication permission does not support a public proprietary number. We prefer a narrower truthful claim to an unsupported metric. ### What should I compare before buying? Compare scope, accountable outputs, prompt and ranking baselines, evidence access, author and source controls, technical ownership, reporting definitions, pricing, contract term, and what the vendor refuses to guarantee. ### Where can I see pricing? This page summarizes current public buying bands. The pricing page contains the current package presentation and the schedule page provides a fit conversation. ## Sources - [10xSearch fixed-panel baseline](https://10xsearch.net/audit/growth-audit-10xsearch-com-2a901b54): The current public baseline: 50 prompts, 100 raw captures, 39 responses with cited URLs, 0 recommendations, and 0 errors. - [Doug Leibinger AI visibility evidence](https://10xsearch.net/evidence/ai-visibility/doug-leibinger): Raw prompt-level evidence and machine-readable JSON. - [Mountain Rose Realty case study](https://10xsearch.com/case-studies/mountain-rose-realty/): Technical rebuild method, result, and reproducibility notes. - [The Kink Team case study](https://10xsearch.com/case-studies/10xsearch-gets-you-found-online/): Qualitative 45-day launch narrative with explicit confidentiality limits. - [10xSearch pricing](https://10xsearch.com/pricing/): Current public package and buying guidance. - [Official Colorado entity record](https://data.colorado.gov/resource/4ykn-tg5h.json?entityid=20261025290): The Colorado Department of State open-data record verifies 10xSearch Inc., entity ID 20261025290, its January 7, 2026 formation date, good-standing status, and Centennial legal office. It verifies legal identity, not service quality or performance. - [OpenGovCO entity record mirror](https://opengovco.com/business/20261025290): OpenGovCO publishes a crawlable third-party directory page derived from the Colorado business-entity dataset. It corroborates the same legal identity and office record, but it is a data mirror rather than an editorial endorsement. - [Inman News exhibitor announcement](https://www.prweb.com/releases/inman-announces-exhibitors-for-inman-connect-san-diego-2026-302827969.html): Inman's July 16, 2026 announcement lists and describes 10xSearch as an Inman Connect San Diego exhibitor. It verifies company and event participation, not performance or an independent endorsement. - [Inman Golden I Club finalist announcement](https://www.inman.com/2026/07/21/inman-announces-the-2026-inman-golden-i-club-finalists/): Inman's editorial team lists 10xSearch as a 2026 Top Luxury Tech/Tool finalist. It verifies third-party industry recognition, not product performance or a customer outcome. - [NAR: Find Your GEO to Land Your Next Referral From AI](https://www.nar.realtor/news/real-estate-news/technology/find-your-geo-to-land-your-next-referral-from-ai): NAR's May 18, 2026 article identifies Rick Janson as the creator of 10X Search, links to 10xsearch.com, and describes the platform's page-evaluation method. Performance statements in the article remain attributed claims, not an independent audit. - [NAR Tech & Innovation: AI Becomes Early Step in Homebuying Journey](https://tech.realtor/2026/07/14/ai-becomes-early-step-in-homebuying-journey/): NAR Tech & Innovation's July 14, 2026 republication identifies Rick Janson as the creator of 10X Search and connects the company to answer engine optimization. It is a second NAR-owned publication surface, not a separate independent endorsement or performance audit. - [Greater Albuquerque Association of REALTORS: AI Becomes Early Step in Homebuying Journey](https://www.gaar.com/blog/article/ai-becomes-early-step-in-homebuying-journey): The Greater Albuquerque Association of REALTORS' July 8, 2026 republication identifies Rick Janson as the creator of 10X Search and connects the company to answer engine optimization. It adds a crawlable association domain for the same underlying NAR reporting, not a separate independent endorsement or performance audit. - [NAR NXT 2026 speaker profile](https://narnxt.realtor/speaker/rick-janson/): NAR NXT's official 2026 speaker profile identifies Rick Janson as the founder of 10xSearch.com and lists his session, How Agents Get Found in AI Search, Google Search and Maps. It verifies the founder connection and subject expertise, not product performance. - [McKissock: How Real Estate Professionals are Using AI in 2026](https://www.mckissock.com/blog/real-estate/how-real-estate-agents-use-ai-webinar/): McKissock's March 6, 2026 editorial recap identifies Rick Janson as founder of 10Xsearch.com and Colibri Real Estate School's resident AI expert. It corroborates identity and industry expertise, not a customer outcome. - [KE Team Hawaii provider disclosure](https://keteamhawaii.com/powered-by): A customer-owned page identifies 10xSearch as the provider behind the site's search infrastructure. It verifies the disclosed relationship, not a neutral review or measured outcome. - [2026 Luxury Presence Visibility Index](https://10xsearch.com/luxury-presence-june-2026/): 10xSearch original cohort research covering 102 luxury real estate portfolios, 2,040 AI answers, aggregate crawl findings, methodology, limitations, and a machine-readable aggregate extract. ## Next step **Ask for the receipts before you buy.** We will show you the baseline, the exact work, the evidence we retain, and the limits before recommending a plan. [Schedule a working session](https://10xsearch.com/schedule/)