You have read the research. You understand that AI search is growing, that citation behaviour differs from ranking logic, and that life science companies have a structural advantage they are not fully exploiting. The question now is what to actually do, in what order, and how to measure whether it is working.
Start here: open ChatGPT, Perplexity, and Google AI Mode and ask each to name the top three companies in your product category. Across Strivenn's exhibitor surveys at ELRIG Drug Discovery 2025 and SLAS 2026, 62% of respondents had never run this test. The same figure held at both events, separated by three months and an ocean. Among those who had checked, 75% found themselves listed.
This is your before picture. Record what you find. Everything that follows in this audit is calibrated against it.
The audit covers six phases and is designed to be executed with your existing marketing team - no specialist agency required. It diagnoses the three patterns we see consistently across the sector: the Unconsidered Set (absence from AI consideration), the Programme Gap (tools without structure), and the conditions that accelerate Citation Compression. At the end, you will know where you stand on each.
Diagnostic question to answer: Are you clearly communicating who you are and what you do?
Entity consistency is the foundation of AI discoverability. Before any optimisation work, you need to know whether the platforms that train and feed AI models have a coherent picture of your company. The entity problem runs deeper: organisations that cannot maintain internal data quality cannot maintain external identity coherence. Fix both together.
Step 1: Write your master entity statement. One paragraph, 100-120 words, that precisely describes: what your company does, which research applications it serves, what makes its technology distinct, which customer segments it serves, and where it operates. This statement becomes the source of truth for every platform.
Step 2: Audit six platforms for consistency against this master statement: your website About page, LinkedIn company page, Crunchbase profile, Wikidata entry, Google Business Profile if applicable, and any industry databases where you are listed. Score each platform 0-2 (0 = absent or wrong, 1 = partial, 2 = accurate and complete). Maximum score: 12.
A score of 10 or above means your entity foundation is solid. Below 8 means inconsistency is likely affecting your citation rate. Below 6 means fixing this is the single highest-leverage action available to you before any content work.
Step 3: Repeat for your three most important products and for your named expert authors. Each should have a verifiable, consistent public profile linked to their published work.
Diagnostic question: Can AI systems actually read your most authoritative content?
Map every significant piece of scientific and technical content your company has produced: application notes, white papers, technical comparisons, clinical validation studies, peer-reviewed publications, and keynote presentations. For each, record two variables: is it ungated (yes/no) and is it structured for extraction (yes/no).
The four-quadrant result: ungated and structured content is your current AI citation asset; ungated but unstructured content is an optimisation opportunity; gated but high-credibility content needs a proxy page; gated and unstructured content is a double barrier to fix.
For the gated content that represents your most credible scientific claims, the proxy page approach is the practical resolution. Write a structured summary: key findings in self-contained paragraphs, specific results with named methodology, named authorship with verifiable credentials, schema markup, and a link to the full document. The proxy earns the citation. The gate still captures the lead.
Diagnostic question: Have you told AI crawlers how to access your site?
AI platforms use different crawlers with different bot identifiers. Your current robots.txt may be blocking them by default. Check for the following bot identifiers and ensure they are not inadvertently disallowed: OAI-SearchBot and Claude-SearchBot for real-time AI search citations; GPTBot and ClaudeBot for model training. If you want AI citation visibility in real-time search results but are uncomfortable with your content being used as training data, the distinction matters: allow search crawlers, consider blocking training crawlers. Most brands have not made this distinction and are blocking both accidentally.
Beyond robots.txt, audit schema markup on your five highest-traffic commercial pages. Check for Organisation schema on your homepage and About page, Author/Person schema on expert-authored technical content, Article or BlogPosting schema on thought leadership, FAQ schema on product comparison pages, and Product schema on product pages. Pages using three or more schema types show measurably higher citation rates.
Diagnostic question: Is your content written to be extracted, not just read?
Select your ten highest-value pages, defined by commercial intent and traffic. For each, score against three criteria: does it contain at least three self-contained paragraphs of 50-150 words each (0-2); does it include at least two specific statistics with named sources (0-2); does it have named expert authorship with a verifiable profile (0-1). Maximum score per page: 5.
A score of 4 or above means the page is well-structured for AI extraction. Below 3 means restructuring is the priority action. Below 2 means content structure is a systemic problem across your programme.
For each page scoring below 3: rewrite the three most important claims as self-contained paragraphs; add specific data points with source attribution; add or verify named authorship; implement the appropriate schema types.
Diagnostic question: Where do you currently appear in AI-generated answers?
This is manual work. Run it monthly and build a simple tracking log. Query five platforms - ChatGPT, Perplexity, Google AI Mode, Microsoft Copilot, and Claude - with the ten questions your ideal customer would ask when evaluating suppliers in your product category.
Example queries for a reagent supplier: "Which DNA polymerases have the strongest data for long-range amplification?" or "How do I choose between suppliers in this category for GMP applications?" Adapt to your specific product category and the language your customers actually use.
Record for each query: Does your brand appear? Is it recommended, mentioned neutrally, or absent? Which competitors appear? What sources are cited when your brand appears? Update this log every 90 days minimum.
This baseline is more valuable than any agency audit report because it reflects actual query behaviour in your specific product category. It is also the only way to know whether you are moving out of the Unconsidered Set. For context: when 62% of life science exhibitors at ELRIG Drug Discovery had never run this test, those who did found themselves listed three quarters of the time. The test is the starting point. Everything else is commentary.
Diagnostic question: Is your best content being refreshed often enough to be retrieved?
Perplexity and Google AI Mode weight recently updated content significantly. Content updated in the past 90 days is cited 40-60% more frequently in retrieval-augmented AI responses than content that has not been touched in twelve months.
Build a quarterly refresh cycle for your ten most important commercial pages. The refresh does not require a full rewrite: add one new data point with a current source, update any statistics that have been superseded even if it's just with the date and a link to the latest certificate of analysis, add a paragraph responding to a question that appeared in recent customer conversations, verify all external links are live.
For thought leadership content, the same principle applies. A post from two years ago on AI adoption in life science marketing is not just dated - it is less likely to be retrieved by recency-weighted AI models even if the underlying argument holds.
The six phases take roughly four to six weeks of focused effort, spread over 90 days to allow time for implementation between diagnostics. The output is a clear picture of your current AI visibility baseline and a specific action list prioritised by impact.
The most important output is not the score. It is the habit of measurement. AI citation landscapes shift as models update and new platforms emerge. The companies with the clearest picture of where they stand at any given moment will respond faster to those shifts than the companies relying on annual audits.
Your competitors are selling to the same researchers. Some of them are already doing this work. The question is whether you start building this picture now, or wait until the gap in citation presence shows up in your pipeline numbers. The gap between knowing and not knowing is that simple.
Ninety days is enough to close a meaningful gap. Start at the entity audit. Everything builds on the foundation of clearly telling the AI bots who you are and what you do across multiple channels.
This is not about keeping up with AI. It is about determining whether AI keeps up with you.
To learn more, visit the AI Discoverability Content Hub