AI Search Visibility Audit & Strategy
Every major LLM — Claude, ChatGPT, Perplexity, Gemini, Bing Copilot, Google AI Overviews — describes your brand to users dozens of times per day. The descriptions are pulled from Wikipedia, Wikidata, structured data, news archives and the open web. We audit what they currently say and improve the source layer they retrieve from.
Where AI gets your brand
High-weight source for every major LLM. Whether your article exists, is neutral, is up-to-date.
Structured facts that knowledge graphs and AI systems consume. Whether your entity is identified, linked and accurate.
What independent press has said. AI systems retrieve from this when no encyclopedic source exists.
schema.org markup on your website that AI crawlers parse for ground-truth claims.
What an AI Search Visibility audit produces
- Snapshots of how 4–6 major AI assistants currently describe your brand
- Hallucination/inaccuracy log with severity scoring
- Source-layer diagnosis: what they're retrieving from and what's missing
- Recommended source-layer improvements (Wikipedia, Wikidata, owned structured data, press)
- Compliance review of any claims being made about you in AI answers
- Periodic re-audit cadence to monitor drift
What we will and will not do
- Strengthen Wikipedia + Wikidata as the highest-weight retrieval source
- Recommend independent press placements that AI retrieves from
- Audit and fix structured-data inaccuracies on owned properties
- Document hallucination patterns so you can correct them publicly
- Promise that AI will say specific phrases about your brand
- Inject prompt-injection content into the open web
- Manipulate retrieval through content farms or bot networks
- Pretend we have backdoor access to LLM training pipelines
See what AI says about your brand today
A scoped audit produces concrete snapshots, a source-layer diagnosis and a recommended path. The Wikipedia & Wikidata layer is usually the highest-leverage starting point.