Answer Engine Review
How often your brand, topics, and supporting pages appear in AI responses.
AI Search Visibility Audits
An AI search visibility audit records where a pharmaceutical brand appears, which sources are cited, and where unclear content or entities reduce accurate representation.
Entity and Schema Review
Structured data, page semantics, and topical clarity that improve retrieval quality.
Action Plan
Priority fixes for content, architecture, and signals that matter most for AI discovery.
Competitive Benchmark
Where competitor and aggregator content is winning AI citations that your brand should own.
An AI search visibility audit is a repeatable observation process. It tests defined questions across selected AI interfaces, records the answer and cited sources, checks whether the brand is represented accurately, and connects each gap to a page or entity that can be improved.
What gets tested
The prompt set is grouped by intent instead of built from random questions. Brand and product prompts test entity recognition. Disease-state and therapy prompts test educational visibility. Comparison prompts show which third-party sources frame the market. Corporate, pipeline, access, and support prompts reveal a different group of competitors and source preferences.
Each prompt is documented with the interface, date, location or account condition when relevant, answer summary, citations, brand presence, factual concerns, and next action. AI answers change. A screenshot from one afternoon is not a benchmark by itself.
What the audit measures
- Brand inclusion and the context in which the brand appears.
- Source citations and repeated domains by question type.
- Accuracy of company, indication, audience, and product relationships.
- Coverage gaps between the questions being asked and the pages available to answer them.
- Structured data, authorship, source visibility, crawl access, and internal-link signals.
- Traditional search visibility for the same topics, because search indexes remain part of the retrieval system.
No magic AI markup
Google's guidance for generative AI features says the foundation remains technical access, useful non-commodity content, clear structure, and ordinary SEO. It also says special AI files are not required for Google's AI search features. Public machine-readable resources can still serve other systems and portfolio goals, but they should not be sold as a secret ranking switch.
For ChatGPT discovery, OpenAI provides separate publisher and crawler guidance. That is why the audit records crawler access by platform instead of assuming one robots rule controls every interface.
What the deliverable contains
The result includes the prompt inventory, evidence captures, source-frequency table, accuracy notes, query-to-page coverage map, entity and structured-data review, and a prioritized remediation list. High-impact accuracy issues come first. Content opportunities follow. Cosmetic scoring comes last.
How the baseline is reused
The same prompt groups are checked again after meaningful changes. The comparison looks for new citations, more accurate summaries, stronger source diversity, and better alignment between the cited page and the question. It does not promise stable rankings inside an interface that can change its model, retrieval process, or presentation.
This audit evaluates discoverability and representation. It does not provide medical, legal, or regulatory advice. Request an AI visibility review with the brand, primary audiences, and markets that matter.