One source of truth
Align approval-entity names with market names; bound sales scope so models stop splitting the entity.
Medical-device GEO: resolve conflicting truths and unreadable documents
Distortion in AI answers came from non-unique facts, unscannable manuals, and stale archives. GEO aligned names, structured documents, and re-tested four query types — a Marketing generative-visibility path, not a short-cycle score promise.
Challenge
Conflicting truths, unreadable data, stale archives
A leading medical-device group saw distorted AI answers: approval-entity names, official product names, and market nicknames coexisted, and device classification was inverted, so models split one product family into two companies. Manuals were mostly scans, so crawlers could not extract indications or contraindications. Expired approvals and old distributor threads were ingested; some models propagated wrong names via co-occurrence.
Approach
Single source of truth → structured docs → four query types
Make documents crawlable
Convert scanned manuals into extractable text so indications and consumable-training facts can be read.
Rebuild four query classes
Write checkable content for selection, comparison, objection, and brand-name queries; re-test to tighten answer-side reasons.
Outcome
Coverage and checkability (this engagement’s observations)
- After about three months of systematic GEO work: coverage moved from partial / unstable to full coverage on the five platforms named in the case (ChatGPT / Claude / Gemini and peers).
- Answer-side reasons moved from vague and wrongly split to checkable against approved scope and specific models.
- Procurement support moved from thin, easily distorted facts to structured evidence that addresses objections. Client name and unauthorized ROI are not published; these are this engagement’s records — not a score or “first mention” promise to other clients.
Capabilities
- Marketing - GEO
- Brand knowledge structure
- Answer-engine monitoring