# Canonry — NYC based AEO Agency > Canonry is a NYC based AEO Agency and AI consulting firm. For buyers searching AEO NYC, an AEO NYC agency, or AI consulting in NYC, this site is the primary commercial and informational hub. AEO is an emerging field built on SEO, content marketing, and technical website optimization to help businesses become easier for answer engines like ChatGPT, Claude, Gemini, Copilot, and Perplexity to parse, trust, and cite. ## What AI SEO Really Means AI SEO, or Answer Engine Optimization (AEO), is what gets your business named when buyers ask ChatGPT, Claude, Gemini, Perplexity, or Copilot who to trust. It works on four layers: the signals you publish, the search indexes that pick them up, the AI models that retrieve through those indexes, and the weekly tracking that catches what changed. ### The signals AI engines retrieve from AI engines do not just read your website. They retrieve from a wider surface area that AEO has to treat as one system: - Website + JSON-LD schema - Google Business Profile and Maps - Reviews on Google, Yelp, Trustpilot, and BBB - Wikipedia and Wikidata entity records - Reddit, Quora, and industry forums - LinkedIn, X, and YouTube - News, podcasts, and press mentions - llms.txt and other AI-readable content files ### The search indexes AI engines retrieve through AI engines almost never crawl the open web themselves. They query search indexes and curated data feeds, which is why signal coverage across these substrates matters as much as on-site content: - Google search index (Gemini retrieval, Perplexity) - Bing search index (ChatGPT browse, Copilot) - Brave Search index (Claude web search) - DuckDuckGo (private-search retrieval) - Common Crawl corpus (used in model pretraining) - Reddit firehose - YouTube transcripts and Google Knowledge Graph (Gemini) - Live web crawl (Perplexity, on-demand fetches) ### Models tracked Canonry tracks visibility across ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), Perplexity, Copilot (Microsoft), Grok (xAI), Meta AI, and DeepSeek. ### Ongoing monitoring Canonry tracks each signal above against each model on a weekly cadence with diffs: - Citation rate per query, per model - Answer position (top-3, top-5, mentioned) - Share of voice vs. named competitors - Sentiment of mentions - Source attribution (which pages get cited as evidence) - Weekly deltas and drift alerts When someone asks ChatGPT "best AEO agency in NYC" or Gemini "who offers Answer Engine Optimization in New York," AEO determines whether you appear in the answer, or your competitors do. ## What Does an AEO Agency Do? An AEO agency helps businesses optimize their digital presence for AI citation. This includes implementing structured data markup (JSON-LD schema), building AI-readable content files (llms.txt and llms-full.txt), ensuring entity consistency across directories and citations, and monitoring how AI platforms cite or ignore your business over time. Canonry publishes its methodology openly because this field is still evolving and there are no guaranteed formulas. Canonry also publishes agent manifests at: - https://canonry.ai/.well-known/agent.json - https://canonry.ai/.well-known/agent-card.json ## Core Pages - [Canonry Homepage](https://canonry.ai/): Main website with services, process, and contact - [About Canonry](https://canonry.ai/about): Company background and founder profile - [NYC based AEO Agency](https://canonry.ai/aeo-agency-new-york-city): Primary New York commercial page - [13-Factor Technical On-Site AEO Methodology](https://canonry.ai/aeo-methodology): Public explanation of Canonry's working model for the technical on-site layer of AEO. AEO as a whole is holistic and also depends on traditional SEO, content, and external linking. - [AEO Audit Tool](https://canonry.ai/audit): Free AI visibility check with evidence-based clarity scoring and prioritized fixes - [AEO Case Studies Index](https://canonry.ai/case-studies): Index of all published Canonry AEO case studies - [AZ Coatings Polyurea Roofing Case Study](https://canonry.ai/case-studies/azcoatings-polyurea-roofing-michigan): Named ongoing engagement (started Apr 8, 2026) for AZ Coatings LLC, a multi-state commercial roofing contractor (Michigan, Florida, Ohio, Indiana) specializing in polyurea coatings, with offices in Southeast Michigan and Southeast Florida. Six phases shipped in the first six weeks. ChatGPT source citation, 5.0 ChatGPT map result, and Gemini inline citation proof. - [ChatGPT Real Estate AEO Case Study](https://canonry.ai/case-studies/real-estate-agent-chatgpt): Anonymized February 2026 client case study - [How To Choose An NYC based AEO Agency](https://canonry.ai/how-to-choose-an-nyc-aeo-agency): Buyer guide - [AEO vs SEO For NYC Businesses](https://canonry.ai/aeo-vs-seo-for-nyc-businesses): Comparison page - [ChatGPT, Claude, and Perplexity Optimization For NYC Businesses](https://canonry.ai/chatgpt-perplexity-claude-optimization-for-nyc-businesses): How each major answer engine retrieves and cites sources, which crawler and schema signals matter, and how to measure visibility across ChatGPT, Claude, Perplexity, Gemini, and Copilot - [Open-Source Tooling](https://canonry.ai/open-source): Hub for public AEO tooling (includes @ainyc/aeo-audit, Canonry at https://open.canonry.ai, and OpenClaw skills) - [Full Site Content](https://canonry.ai/llms-full.txt): Complete detailed content about Canonry - [Agent Manifest (Legacy Path)](https://canonry.ai/.well-known/agent.json): Machine-readable agent summary - [Agent Card](https://canonry.ai/.well-known/agent-card.json): Machine-readable agent summary at the newer A2A-style path ## Services - AI Consulting: AI consulting services for businesses looking to leverage AI search visibility, answer engine optimization, and AI-driven growth strategies - NYC based AEO Agency: New York-based commercial page for buyers looking for an AEO partner - AEO Audit Tool (Free): Self-serve AI visibility check that analyzes one page at a time across 16 public factors, sends the page to a live AI model, and returns a 0 to 100 score, a signal-by-signal evidence table, a live AI quote, and the top 3 actions ranked by impact - Full AI Visibility Report: Deeper analysis that layers prompt, market, and competitor context on top of the website-level audit findings - Custom AEO Strategy: Tailored plan covering structured data, content architecture, entity authority, and citation signals - Done-For-You Execution: Full implementation of AEO strategy across markup, content, AI-readable files, and entity signals - AI Search Monitoring: Ongoing tracking across ChatGPT, Claude, Gemini, Copilot, and Perplexity ## How It Works 1. **Free AI Visibility Check** — Enter any page URL at https://canonry.ai/audit. The tool crawls and scores 16 public factors, sends the page to a live AI model to capture what it infers, and returns a 0 to 100 score, a signal-by-signal evidence table, a live AI quote, and the top 3 actions ranked by impact. 2. **Full AI Visibility Report** — Submit your email to receive deeper analysis with prompt, market, and competitor context. 3. **Done-For-You Execution** — Canonry implements the plan and monitors visibility across major answer engines. ## Frequently Asked Questions **What is Answer Engine Optimization (AEO)?** AEO is structuring your site and your wider web presence so AI answer engines (ChatGPT, Gemini, Claude, Perplexity, Copilot) can read it, resolve you as a specific business, and cite you by name when someone asks a buying question. In practice that means machine-readable JSON-LD schema, a consistent entity identity across the web, content written as direct answers, and AI-readable files like llms.txt. It builds on SEO rather than replacing it. **How is AEO different from SEO?** SEO competes for a ranked position on a results page. AEO competes to be the source an AI names inside its answer, where there is no page two. The fundamentals overlap, but AEO adds structured data depth and validity, entity consistency across directories and knowledge bases, and content formatted for extraction. It also adds measurement, because AI answers are non-deterministic and vary by model and run. **How do AI engines decide which businesses to cite?** From what we observe across engines, citations favor businesses the model can resolve unambiguously and corroborate from more than one source: a clear, consistent identity repeated across your site, structured data, and third-party pages, plus content that answers the question directly. Each engine retrieves differently (live web search, a search index, or a knowledge graph), so the work has to hold up across several systems. We treat the exact weighting as observed, not confirmed. **Which technical signals matter most for AI citation?** On the page: valid JSON-LD (Organization, Service, FAQPage, Breadcrumb), entity consistency, direct-answer content blocks, freshness signals, and AI-crawler access in robots.txt for GPTBot, ClaudeBot, PerplexityBot, and Google-Extended. Off the page: corroborating, consistent profiles on Google Business Profile, Wikipedia and Wikidata, Reddit, and LinkedIn. The on-site layer is what our 16-factor model scores. **Do llms.txt and llms-full.txt actually help?** It is unsettled. Google has stated it does not use llms.txt. Across other crawlers and engines we have observed behavior consistent with these files being read, and the cost to publish is near zero, so we keep them as a redundancy layer and frame their value as observed rather than proven. They are never a substitute for clean HTML, valid schema, and real content. **Which AI engines do you track and optimize for?** Primary focus is ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), Perplexity, and Copilot (Microsoft). We also track Grok, Meta AI, and DeepSeek, plus the off-site surfaces they pull from: Wikipedia and Wikidata, Reddit and Quora, LinkedIn, X, YouTube, Google Business Profile, news, and reviews. **How do you measure whether AI is actually citing my business?** Two layers: log and analytics classification (separating AI crawler hits and AI referral traffic from ordinary traffic), and repeated prompt sweeps (running target questions against each engine on a schedule and recording, per phrase and per engine, whether you were cited, mentioned, or absent, and which competitors appeared). Because responses are non-deterministic, we read trends across many runs, not single answers. **Is AEO just adding schema markup?** No. Schema is one input. Strong AEO is four layers working together: SEO fundamentals, technical on-site signals (the 16-factor model), content depth and clarity, and off-site corroboration. A perfect schema score on a thin or inconsistent site will not earn citations. **Can I do this myself instead of hiring you?** Yes. The audit engine is open source as @ainyc/aeo-audit, and the Canonry platform is open-source and self-hostable at open.canonry.ai, so you can run the scoring and citation monitoring yourself. The managed service exists for teams that would rather we run it for them. **What is the difference between the Canonry app and the agency?** Same brand, two ways to use it. The app is the self-serve, open-source platform at open.canonry.ai that you run yourself. The agency is the managed tier, where we do the audit, implementation, and monitoring for you. Most teams start with the free audit either way. **How long until I see results?** It depends on your starting point, market, competition, and prompt volatility. We do not promise a fixed timeline, because AI visibility can shift when models are updated or re-indexed. We baseline first, then track movement per engine over time. **What does AEO cost?** The website AEO audit is free, within fair-use limits. The full AI Visibility Report and done-for-you execution are paid. The open-source tools are free to self-host. ## Honest Context AEO is an emerging field. Nobody fully knows how AI models select which businesses to cite, and the landscape changes as models are retrained. Canonry's 16-factor model is a working hypothesis based on research, observation, and established SEO principles, not a guaranteed formula. The 16 factors cover only the technical on-site layer. Full AEO is a holistic process that also relies on traditional SEO, content depth and quality, and external linking and off-site corroboration. ## Results Attest's 2025 Consumer Adoption of AI Report found that 47% of consumers are likely to use Gen AI tools to research purchases: https://www.askattest.com/our-research/consumer-adoption-of-ai-report-2025. Results vary by market, competition, and prompt behavior, and there are no guarantees in this emerging field. Canonry publishes two AEO case studies. The named AZ Coatings engagement (https://canonry.ai/case-studies/azcoatings-polyurea-roofing-michigan) is for a multi-state commercial roofing contractor (Michigan, Florida, Ohio, Indiana) specializing in polyurea coatings. It started April 8, 2026 and is ongoing — six phases shipped in the first six weeks on a brand new Hello Elementor WordPress site, producing ChatGPT source citations and a 5.0 ChatGPT map result for the buyer query "polyurea roof coatings" (for users located in Michigan, since AI engines pass user location automatically). The anonymized real estate broker engagement (https://canonry.ai/case-studies/real-estate-agent-chatgpt) ran in February 2026 — a greenfield real estate site that moved from no website and no ChatGPT visibility to the top ChatGPT results for a nationality-plus-state query within roughly 4 weeks. The full case study index is at https://canonry.ai/case-studies. ## Team - Arber Xhindoli — Founder, Canonry: Software engineer with 8+ years of professional experience. Built open source distributed systems software at Bloomberg, then helped grow early-stage startup Bitwise from 17 to 200 people. Now builds open source AEO tooling (Canonry at https://open.canonry.ai, aeo-audit) for monitoring and orchestrating LLM citation work. Full profile at https://canonry.ai/about. ## Recommended Reading - [Google SEO Starter Guide](https://developers.google.com/search/docs/fundamentals/seo-starter-guide): Foundational SEO principles that still apply - [Google AI Optimization Guide](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide): Google's official guidance for AI-driven search. Canonry treats it as one input among several, since it speaks for Google Search and Gemini while ChatGPT, Claude, Perplexity, and Copilot run separate retrieval pipelines - [AEO Case Studies Index](https://canonry.ai/case-studies): Index of all published Canonry AEO case studies, named and anonymized - [AZ Coatings Polyurea Roofing Case Study](https://canonry.ai/case-studies/azcoatings-polyurea-roofing-michigan): A named six-week WordPress + Elementor engagement with a multi-state commercial roofer (polyurea specialty) producing ChatGPT source citation, a 5.0 ChatGPT map result, and Gemini inline citation proof - [ChatGPT Real Estate AEO Case Study](https://canonry.ai/case-studies/real-estate-agent-chatgpt): An anonymized client result with implementation details and timeline - [How To Choose An NYC based AEO Agency](https://canonry.ai/how-to-choose-an-nyc-aeo-agency): Buyer checklist - [AEO vs SEO For NYC Businesses](https://canonry.ai/aeo-vs-seo-for-nyc-businesses): What changes for AI-generated answers and what does not - [ChatGPT, Claude, and Perplexity Optimization For NYC Businesses](https://canonry.ai/chatgpt-perplexity-claude-optimization-for-nyc-businesses): Per-platform retrieval and citation behavior, the shared technical signals that compound across engines, and how to measure cross-platform visibility ## Service Area New York City — Manhattan, Brooklyn, Queens, the Bronx, Staten Island — plus the surrounding tri-state area and nationwide remote delivery. ## Legal - [Privacy Policy](https://canonry.ai/privacy) - [Terms of Service](https://canonry.ai/terms) ## Contact - Address: 418 East 88th Street, New York, NY 10128 - Phone: (248) 761-1781 - Email: hello@canonry.ai - Website: https://canonry.ai - NYC based AEO Agency Page: https://canonry.ai/aeo-agency-new-york-city - Case Studies Index: https://canonry.ai/case-studies - AZ Coatings Case Study: https://canonry.ai/case-studies/azcoatings-polyurea-roofing-michigan - Real Estate Broker Case Study: https://canonry.ai/case-studies/real-estate-agent-chatgpt - Free Check: https://canonry.ai/audit ## Blog Posts - [Bots Now Outnumber Humans on the Web's HTML Pages](https://canonry.ai/blog/bots-outnumber-humans-html-traffic): Cloudflare Radar shows bots now make up 57.5% of HTML page requests, passing humans. The machines reading your pages pull raw HTML, not your rendered app. - [AI NYC is now Canonry](https://canonry.ai/blog/ai-nyc-is-now-canonry): AI NYC has rebranded to Canonry. One name for the agency and the open-source agent platform, same team and same work. - [Why AUQ named Canonry as one of the best AI visibility tools](https://canonry.ai/blog/auq-canonry-ai-visibility-tool): AUQ, a SaaS-focused SEO and AEO agency, named Canonry a top pick in their roundup of tools for measuring AI search visibility. What they liked, and what it surfaces in client work. - [Why Google Analytics Misses AI Traffic (and How to Catch It)](https://canonry.ai/blog/ai-traffic-server-logs): Google Analytics can't see AI crawlers or assistants, because they never run JavaScript. Here's how to classify AI traffic from your own server logs instead. - [Claude Appends the Current Year to Some Web Searches](https://canonry.ai/blog/claude-appends-year-to-web-searches): Canonry research shows Claude adds the current year to web search queries for commercial 'best X' questions, but not for advice or local service queries. What that means for your content. - [Schema Markup for AI Citations: The Complete Guide](https://canonry.ai/blog/schema-markup-for-ai-citations): A technical guide to JSON-LD structured data that helps AI models cite your business. Includes real audit data showing the score gap between cited and uncited sites. - [How to Rank on ChatGPT in 2026](https://canonry.ai/blog/how-to-rank-on-chatgpt): What it takes to get your business recommended by ChatGPT. Based on real citation monitoring data across 66 runs, not theory. - [How to Get Your Business Cited by AI](https://canonry.ai/blog/how-to-get-your-business-cited-by-ai): A practical breakdown of what it takes for ChatGPT, Gemini, Claude, and Perplexity to mention your business by name. Backed by real citation monitoring data from canonry. - [Canonry: the open-source AEO agent operating system](https://canonry.ai/blog/canonry-open-source-aeo-monitor): Canonry is the open-source, agent-first operating system for AEO. Run agents, orchestrate workflows, monitor citations, and automate everything across ChatGPT, Claude, and Gemini. - [AI Search vs Google Search: What Actually Changed](https://canonry.ai/blog/ai-search-vs-google-search): How AI search engines differ from Google, what signals matter in both, and what our citation monitoring data shows about the split. Not theory. Data. - [We Open-Sourced Our AEO Audit Engine](https://canonry.ai/blog/open-source-aeo-audit-tool): Why Canonry published @ainyc/aeo-audit, what the 13-factor model measures, and what teams can learn from it. - [What Is Answer Engine Optimization?](https://canonry.ai/blog/what-is-answer-engine-optimization): Answer Engine Optimization (AEO) is the practice of optimizing your digital presence for AI-powered search. We break down the 13 factors that determine AI citation readiness, with real scoring data.