Photo by Erik Mclean on Pexels
Photo by Erik Mclean on Pexels
When a general counsel types a question about M&A litigation into ChatGPT or Gemini before ever opening a search engine, the model answers from whatever structured, citable evidence it can find about your firm's deal history — not from your reputation among peers. Most M&A litigation sites bury outcomes in PDF press releases AI crawlers cannot parse, so the model defaults to whichever competitor has structured its practice pages as machine-readable entities. GEO corrects this by encoding matter outcomes, deal values, and practice-group credentials as JSON-LD, publishing an llms.txt briefing that states plainly what your firm litigates and has won, and building the citation trail — bar association recognition, deal-league tables, legal directories — that large language models treat as authoritative. The result is that when a model is asked to recommend counsel for a contested acquisition, your firm's actual record, not a generic description, is what gets surfaced.
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Models weigh claims against corroborating sources — court records, legal directories, bar recognitions, press coverage. Unsupported claims on your own site carry little weight; the same claim echoed by Chambers, Martindale, or a court filing becomes citable fact a model will repeat confidently.
Yes — GEO doesn't require disclosing settlement terms. It structures what's already public (case names, practice areas, verdicts of record, credentials) so models can accurately characterize your track record without any confidential detail being exposed.
AI HALO re-scans at 30 days. Crawl and re-indexing cycles for the major assistants vary, but structured data and citation changes typically begin showing measurable shifts in model responses within that window.
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