Measure where you're missing, and why.
Our audit & monitoring tool (in alpha access) crosses two complementary views of your visibility inside the LLMs.
Visibility inside the LLMs
We query ChatGPT, Gemini… with real user prompts, grouped by intent, with several samples per prompt to measure stability.
- ScoreAEO visibility score — the synthesis (e.g. 17/100).
- SOVShare of voice — who AI names, and in what proportion.
- GapsOrganic gaps & untapped — your content roadmap and the open ground to claim first.
- FidelityCitation timing & source footprint — when, and with which sources, you're cited.
Who AI names. Share of voice per brand and prompts where competitors are cited, not you.
AEO score. Spontaneous visibility, alternative visibility, citation timing and stability across 3 samples.
Gaps. 23 organic gaps + 250 untapped prompts — your roadmap, by intent.
Technical & semantic audit
We audit the product page the way an agent sees it: access (HTTP, robots, WAF), readability (raw HTML vs rendered JS), structured data (Schema.org) and semantic depth.
- ReachReach & read — accessible and readable without JS? llms.txt, markdown, chunkability, Content-Signal.
- DataProduct data — name, price, availability, description & Product JSON-LD server-side, without JS.
- SemanticSemantic — benefits, use cases, audience, differentiators, FAQ, specs, reviews.
- AgentsReal reading — what Gemini and OpenAI actually extract (and their conflicts).
Score /100. Sub-scores Reach & read / Product data / Semantic + prioritized quick wins.
As agents see it. Price, JSON-LD and description often missing from raw HTML → citation unlikely.
Semantic depth. Decision-grade description, use cases, FAQPage, AggregateRating — extractable.
Two symptoms, one problem: product data. Demand tells you where you're missing, supply tells you why. You act on the shared variable to close the gap on both sides.
