This case study outlines a practical audit model for biotech and pharmaceutical brands that want to understand how visible they are in AI-assisted discovery environments.

Audit the prompts, not only the pages

The first step is identifying the question types that matter most. Disease-state prompts, brand prompts, comparison prompts, and market prompts often produce different visibility patterns.

Trace the retrieved sources

Once the prompts are mapped, the audit tracks which sources are surfaced repeatedly and which internal pages are missing. This reveals where authority and clarity are breaking down.

Review the machine-readable layer

Structured data, glossary support, and internal content relationships all influence how well the site can be interpreted by systems assembling summaries.

Turn findings into a roadmap

The best outcome is not a score. It is an ordered plan covering entity clarity, article gaps, supporting page types, and source improvements that increase the probability of inclusion.