Generate llms.txt guidance for canonical product, docs, and source pages.
Create a concise AI-readable source map that points answer engines toward canonical product, pricing, docs, support, and comparison pages.
What the page helps you evaluate
Judge AI visibility by evidence, not a detached score.
Commercial AI search pages should help teams decide what to monitor, what evidence matters, and what work should happen next.
Prompt-level evidence
See the answer snapshot behind the score so teams know what customers actually see.
Competitor context
Track which brands appear beside you, above you, or instead of you for the same prompt.
Citation intelligence
Classify owned, competitor, review, directory, media, and community sources that shape AI answers.
Action briefs
Convert weak answer evidence into clear content, source, crawler, and reporting actions.
Workflow
Move from the search query to a repeatable operating loop.
Canonical source map
List the pages AI systems should prefer when describing your product.
Content governance
Keep product, pricing, docs, support, and comparison claims aligned.
Citation cleanup
Use the file as an operating checklist alongside citation tracking.
Questions buyers ask
Does llms.txt guarantee AI citations?
No. It is not a ranking hack. It is a useful source-map convention that should be paired with crawlable, high-quality pages.
What should go into llms.txt?
Include concise product facts and the canonical URLs for docs, pricing, support, trust, comparison, and other answer-ready pages.
Next paths