Track whether ChatGPT, Perplexity, Gemini, Google AI Overviews and Claude associate your firm with the right practice areas, jurisdictions, industries and credentials, and which firms appear instead.
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Real prompts from the live product configuration. This is the clearest proof Seeno is built for your industry, not a generic prompt box.
In-house teams and founders ask an assistant which firm handles a matter type in a jurisdiction. If AI does not connect your firm to that practice, you are off the shortlist before the RFP.
AI may know your firm exists yet still not recommend it for construction disputes, because the cited sources emphasise your corporate work. Being known is not being recommended.
The rankings AI leans on come from a narrow set of legal directories and publications. Presence and accuracy there directly shape which firms get named.
The same six signals, in the language of law firms.
A configured product example. Firms shown are an illustrative set, not a published ranking.
Example practice-area competitor view, not an authoritative "Top UAE Law Firms" ranking.
The domains AI leans on when answering law firms questions. Presence and accuracy here is what moves the recommendation.
Legal buyers act on specifics. Seeno flags where AI describes your firm incorrectly, so you can correct the record before it costs a mandate.
Recommendations tie to specific question clusters, competitors and sources — not generic advice.
Strengthen a practice-area page with lawyer, matter, industry and jurisdiction evidence.
Align lawyer biographies with the question clusters you want to win.
Resolve inconsistent firm descriptions across the legal directories AI cites.
Publish authoritative answers on recurring regulatory questions in your target practice.
Managing this for clients? See Agency Mode →
Not yet. Until a full public Legal benchmark ships, the page shows a clearly labelled configured example. Your own audit measures your firm against your real competitor set.
By practice area, jurisdiction, client industry and matter type, the way buyers actually ask, and the way answers are decided.
Yes. The claim-accuracy module flags wrong practice areas, outdated rosters and unsupported credentials AI is repeating.