Find the gaps.
Audit technical signals, current AI answers, competitors, citations, and the buyer questions that matter.
Canonry Managed · For businesses
Your AI search program, operated end to end.
Canonry plans the work, implements the changes, and monitors how ChatGPT, Claude, Gemini, Perplexity, and Copilot describe and cite your business.
Service model
One accountable team across technical AEO, answer-ready content, entity signals, off-site corroboration, and ongoing measurement.
Trusted by forward-thinking teams
End-to-end execution
One team accountable from baseline to ongoing measurement.
The work spans the places AI systems use to understand a business: the site, structured data, direct-answer content, business profiles, reviews, citations, and the sources models retrieve.
Every recommendation is tied to observable evidence. We implement the priority changes, then track how answers move across models, buyer questions, and markets.
The engagement
From diagnosis to iteration.
Prioritize the work.
Turn the evidence into a sequenced plan across technical, content, entity, and off-site surfaces.
Ship the changes.
Implement schema, site architecture, direct-answer content, entity cleanup, and corroborating signals.
Track what moves.
Recheck answers, citations, competitors, and visibility changes, then use the evidence to choose the next work.
Managed client results
Published work, observed outcomes.
These engagements document what shipped and what Canonry observed. They are not a guarantee of identical outcomes.
Anonymized real estate broker
Recorded ChatGPT observations after a new-site launch.
Canonry built the site, aligned metadata and entity signals to one high-intent local query, and published structured data and direct-answer content. The case study records prompt-specific ChatGPT observations from roughly four weeks after launch; results vary by prompt, time, and user context.
Read the case study
AZ Coatings
Structured data, regional authority, and direct citations.
Starting from a seven-page WordPress site with effectively no structured data, Canonry shipped regional pages, canonical entity markup, direct-answer content, and ongoing citation monitoring. Six weeks in, AZ Coatings was cited in ChatGPT and Gemini for relevant Michigan buyer queries.
Read the case studyWhat Canonry runs
The work between the audit and the result.
Technical and entity foundation
Site architecture, crawlability, structured data, entity consistency, and platform-specific technical issues.
Answer-ready content system
Service definitions, buyer questions, market pages, FAQs, and content structures that give models direct evidence.
Off-site corroboration
Business profiles, reviews, industry sources, citations, and other surfaces that support the entity beyond its own site.
Ongoing measurement and iteration
Prompt tracking, model comparisons, citation evidence, competitor movement, and the next prioritized work.
Canonry Managed starts here
See what AI engines can understand today.
Run the free onsite technical audit first. After the baseline, request a detailed report with market context, competitor gaps, and prioritized actions.