The challenge
Medical and commercial teams needed a systematic way to identify where external AI answers diverged from approved brand information and trace each finding to evidence.
Make external AI answers observable
The platform systematically probed how AI answer engines responded to healthcare-professional questions about pharmaceutical brands. The engagement was delivered through a consulting partner.
The evaluation covered answers from ChatGPT, Claude, Gemini, and Perplexity, alongside Google AI experiences and other answer engines. Controlled questions provided a repeatable basis for comparison.
Capture the answer and its supporting citations
The pipeline submitted the question set and captured generated responses with their citations. It evaluated those responses against governed pharmaceutical reference content.
This linked external answer monitoring to the approved medical and commercial content used by the organization. The review could move from a concerning answer to the source material relevant to that finding.
Measure coverage and potential divergence
The measurement model included Share of Answer and approved-claim coverage. It also examined missing claims, citation behavior, and potential off-label drift.
Each finding was traceable to the corresponding approved label or source content. That traceability supported review of why the issue was raised, rather than leaving the team with a score detached from its evidence.
Connect monitoring to content decisions
The platform helped teams identify where AI-generated responses diverged from approved messaging and determine which approved content could address the gap.
The delivered monitoring workflow connected an external answer to an approved reference. This gave the team a concrete basis for reviewing a claim or citation and deciding which content gap to address.
Key design decisions
Keep questions controlled
A repeatable question set gives the evaluation pipeline a consistent input. That supports systematic review across answer engines rather than a collection of unrelated manual searches.
Evaluate citations with the response
The pipeline captures generated answers and their citations together. This allows the review to consider the answer alongside the sources presented to the reader.
Attach findings to governed content
Claim coverage, missing claims, and potential off-label drift are linked to approved reference material. The connection gives medical and commercial teams a concrete basis for reviewing a finding.
How the workflow fits together
- 01
Submit the controlled question set
Healthcare-professional questions probe how the selected AI engines discuss pharmaceutical brands.
- 02
Capture the answer evidence
The pipeline records the generated response and its citations for evaluation.
- 03
Compare with approved reference content
The evaluation identifies coverage and gaps, including citation behavior and potential divergence from approved messaging.
- 04
Connect the finding to a content decision
The team can inspect the relevant approved source and assess which content could address the observed gap.
Questions for a similar implementation
Use these review points when you assess this architecture for your own environment.
- Can a finding be traced to both the captured answer and the relevant approved source?
- Are missing claims distinguished from incorrect or unsupported claims?
- Does the review preserve the difference between an observed answer and a conclusion about the model as a whole?
The outcome
Medical and commercial teams gained a repeatable way to inspect external AI answers against approved content, with evidence attached to the findings.
- Multi-engine AI evaluation
- Governed pharmaceutical reference content
- Citation analysis
- Claim-level traceability
Client and delivery-partner names are withheld.