AI search visibility audit
The problem we solve

AI search visibility
audit

Finding out, concretely, how AI tools currently represent you.

An AI search visibility audit for pharmaceutical companies wanting to know how major AI answer engines currently describe their company and products, rather than assuming.

The problem
What is included

What an AI search
visibility audit involves

What it involves

Most companies have no concrete idea how AI tools currently represent them, and the answer is frequently inaccurate, outdated or absent entirely. An audit replaces assumption with direct evidence, checked against the actual questions your audience would plausibly ask.

What we deliver

Direct querying of major AI tools with realistic audience questions, an accuracy check against what is actually true, a citation and source analysis, and a prioritised set of recommendations to improve representation.

Diagnosis and a plan, not a promise

The audit gives you a clear diagnostic of your current AI citation visibility and a concrete set of recommendations to improve it — grounded in what we can actually verify today, not a guaranteed outcome from any AI provider.

case studies

Clients we've worked with

Real projects for industrial and pharmaceutical companies.
AI audit track record
Track record

What our AI search
visibility audits have covered

The scope our AI search visibility audit work has operated within.

+10
years building for regulated industries
+200
organisations have trusted Code
+1.500
documents migrated with their access permissions intact
+160
scientific papers in a single managed repository
AI visibility audit by concern
Who needs it

What pharma companies want from an AI search audit

The underlying question differs by company. An AI search visibility audit starts from that question.

Audit process
Four stages

How we run an AI search
visibility audit

Four stages, producing concrete evidence rather than a general opinion.

QUESTION MAPPING
01
01

What your audience would actually ask

We define realistic questions your audience would plausibly ask an AI tool, since the audit is only useful if it reflects real usage rather than a contrived test.

What we define

We build query sets grouped by audience, such as a clinician, a patient, or a procurement buyer, and by topic, covering direct product questions, general questions about the company, and comparison questions against named competitors, since each audience phrases things differently.

Result

The audit is built on questions people would realistically type into an AI tool, not a contrived list designed to make the results look better or worse than they actually are.

QUERYING AND RECORDING
02
02

Direct, documented evidence

We query the major AI answer engines directly with the defined questions and record exactly what is returned, including sources cited.

What we record

We run every question against the major AI answer engines, save the full response verbatim rather than a summary, note exactly which sources are cited for each claim, and check the answer against facts we know to be correct about your organisation.

Result

You see precisely what these tools are currently telling people about you, in their own words, rather than an estimate or a general statement about how AI search visibility tends to work.

ACCURACY AND GAP ANALYSIS
03
03

What is wrong, missing, or ceded to someone else

We analyse the results for inaccuracy, missing information, and cases where a competitor or third party is cited instead of you.

What we analyse

We flag every factual inaccuracy in the recorded responses, identify information a genuine answer should have included but did not, and compare how often a competitor or third-party source is cited in place of you across the same questions.

Result

You know specifically where the AI answer is wrong, where it is silent, and where a competitor is getting cited instead, which is a very different starting point than a general sense that visibility could be better.

RECOMMENDATIONS
04
04

What to fix, structurally

We deliver prioritised recommendations for improving structural factors within your control — content clarity, structured data, factual density.

What we deliver

We tie every recommendation directly to a specific gap uncovered in the analysis, such as a missing fact, an ambiguous claim, or thin structured data, rather than a generic checklist of AI-search best practice that may not address what is actually wrong on your site.

Result

Your team has a specific, ordered list of changes to make, each one linked to the gap it is meant to close, rather than a general observation that AI visibility could be improved.

AI search visibility audit questions

What comes up when checking AI representation for the first time.

Which AI tools do you check?

We check the major AI answer engines your audience is most likely to use in practice, such as ChatGPT, Gemini and AI-generated search summaries, rather than defaulting to a fixed list regardless of your actual audience behaviour. Which engines matter most is confirmed with you at the start of the engagement, based on where your buyers currently search.

What if the representation is inaccurate?

We document exactly where and how the representation is inaccurate, whether that is outdated pricing, a missing product line or a misattributed claim, and recommend the structural content changes needed to correct it. See AI search visibility for the implementation work — updated schema, clearer source content — that follows the audit and puts those fixes into practice.

Can you guarantee we get cited after this?

No, citation decisions are made entirely by each AI provider’s own ranking system, and outside parties do not control that outcome directly. What we improve are the structural factors within our control, such as content clarity, schema and source authority, and we report clearly on which of those changed and which citations still did not follow.

How often should this be rechecked?

This space changes faster than traditional search, since AI models and their source weighting are updated frequently and can shift how a brand is represented within weeks. We recommend rechecking regularly, at a cadence tied to how fast your market and competitors move, rather than treating the initial audit as a one-time exercise that stays accurate indefinitely.

Related pharmaceutical website problems

Other pharmaceutical website problems we solve

An AI visibility audit often connects to these related problems.

AI search visibility audit

Get your AI search
visibility audit

Uncertainty about how AI tools currently represent your company. Tell us your audience and we will tell you how we would approach the AI search visibility audit.

contact us
Contact Form

Tell us
about your project

Tell us about your organization’s context and the planned scope of the project.
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