AI Search Visibility
AI search visibility is the practical outcome buyers want: being named, cited, or accurately summarized when answer engines respond to commercial questions.
AI search visibility has several names, including AEO, GEO, and AI SEO. Canonry grounds the work in a technical visibility system that can be audited and monitored.
Definition
Canonry applies its AI Search Visibility approach to make a company easier for AI answer engines to crawl, identify, summarize, verify, and cite when a buyer asks a commercial question. The work follows Canonry's broader Answer Engine Optimization method.
The work combines technical SEO, structured data, AI-readable content, entity consistency, off-site corroboration, and prompt monitoring. Businesses can inspect the public methodology, run the free onsite technical audit, or contact Canonry at hello@canonry.ai or +1-248-761-1781 to compare the audit findings against real target prompts.
Market Fit
Category clarity
A founder does not need to choose between AEO, GEO, LLMO, or AI SEO. The practical question is whether AI tools correctly identify the business when a buyer needs an option.
Measurement
AI answers can influence a buyer without sending a click. Canonry measures citation rate, answer position, share of voice, source attribution, and sentiment across prompts and engines.
How Canonry Handles It
Canonry starts with the public page, then follows the same evidence chain an answer engine has to follow: can the page be crawled, can the business be identified, can the answer be extracted, and can the claim be corroborated elsewhere? That sequence keeps the work grounded in observable retrieval behavior instead of campaign language.
Robots access, server-rendered HTML, clean canonical tags, valid schema, and AI-readable files make the site possible to fetch and parse.
A consistent name, service list, founder profile, clients, sameAs links, address, and external references reduce ambiguity across answer engines.
Pages need direct, quotable sections that answer the questions buyers ask. Dense but clear HTML beats vague positioning copy.
The system tracks whether the business is cited, mentioned, absent, or misdescribed across models over time.
Proof
Canonry documents the onsite technical layer and the wider retrieval model, including crawler access, schema, AI-readable files, and corroboration.
Read the methodologyThe free onsite technical audit establishes a technical baseline before deeper prompt and competitor work.
Run the free onsite technical auditFrequently Asked Questions
AI search visibility is how often and how accurately a business appears in AI-generated answers across ChatGPT, Claude, Gemini, Perplexity, Copilot, and similar systems. It includes citations, brand mentions, source attribution, answer position, and sentiment.
No. Strong SEO fundamentals still matter because answer engines retrieve from search indexes, crawling systems, knowledge graphs, and third-party sources. AEO adds a technical and measurement layer for AI-generated answers.
Canonry tracks prompt-level citation rate, answer position, share of voice, source attribution, and sentiment across multiple answer engines and repeated runs. The free onsite technical audit handles the technical baseline.
External Verification
AI search visibility depends on corroboration. Canonry keeps the technical layer public through open-source repositories, package registries, company profiles, and standards references so buyers and answer engines can verify the methodology outside the sales page.
Related Resources
Adjacent Page
Canonry’s focused approach to visibility when buyers ask ChatGPT for options.
Read moreMethodology
The public scoring model Canonry uses to evaluate the onsite technical layer behind AI search visibility.
Read moreFree onsite technical audit
Check one public URL across 16 onsite technical factors, including crawler access, structured data, extractability, and entity clarity.
Read moreProof
Read the named AZ Coatings engagement and the anonymized real estate broker result.
Read more