Background
This case study documents the AI visibility program of a clinical-stage biotech developing small-molecule therapeutics in a specialized oncology indication. The company is publicly listed, generates pre-commercial revenue from research collaborations, and has one program in phase 2 and two in phase 1 at the time the program began. The team included 85 employees, a dedicated investor relations function, and an external communications agency.
The company faced a specific visibility challenge. Its therapeutic area was well-defined clinically but not widely discussed outside the specialty community. AI assistants responding to questions about the indication typically cited major pharma incumbents or large-cap biotech, rarely mentioning clinical-stage players. For a public biotech, the effect was that AI-sourced investor and patient research missed the company entirely, even when direct competitors in the same indication were mentioned.
The Starting Point
The baseline audit covered 40 prompts across ChatGPT, Claude, Perplexity, and Gemini, spanning therapeutic-area queries, pipeline queries, investor queries, and company-specific queries. The company appeared in 4 of 40 prompts across all four platforms in month 1, with most of the citations coming from the most-recent SEC filings rather than editorial or scientific sources. Its closest-stage direct competitor appeared in 14 of 40.
Root-cause analysis identified a classic pattern for clinical-stage biotech. Only one phase 1 program had reached peer-reviewed publication. Press coverage was limited to financial press at SEC-filing events. The company's Wikipedia entry was minimal. The investor relations site had deep content but thin structured data. The scientific rationale for the lead program was well-described on the company site but not discoverable by AI crawlers because the content lived inside a JavaScript-rendered investor presentation portal.
The Intervention
The 12-month program ran across four phases coordinated with the communications agency, IR team, and scientific leadership.
Phase 1 (months 1 to 2): foundation. Update the Wikipedia entry with well-sourced references to published research, regulatory designations, and financial events. Complete the Wikidata entity with every relevant property. Add Organization schema markup to the corporate site. Unblock AI crawlers in robots.txt (several had been blocked by a legacy policy).
Phase 2 (months 3 to 5): scientific surface. Publish dedicated, crawlable HTML pages for each pipeline program, each with structured MedicalCondition, MedicalIntervention, and Study schema where applicable. Publish a dedicated therapeutic area page that serves as the canonical description of the disease biology, unmet need, and competitive landscape. Publish a dedicated mechanism-of-action page for the lead program, with citations to published rationale.
Phase 3 (months 6 to 9): publication and editorial strategy. Coordinate with the clinical team to accelerate manuscript submission for the phase 1 readout that was ready to publish. Secure editorial coverage in Endpoints, STAT, and BioSpace aligned with phase 2 interim data. Place a pipeline-overview op-ed in a specialist trade publication. Coordinate analyst day materials to be indexable rather than slide-only.
Phase 4 (months 10 to 12): durability and IR integration. Build quarterly refresh processes into investor communications so AI-visibility work continues without campaign-mode effort. Integrate AI-visibility metrics into quarterly IR reporting so leadership sees the channel alongside analyst coverage and trading metrics. Train scientific affairs team on AI-friendly content structure so new publications and presentations are AI-citable by default.
The Results
By month 12, the company appeared in 24 of 40 audit prompts, or 60 percent visibility. Pipeline-specific queries showed the largest gains: visibility on lead-program queries moved from 5 percent to 70 percent. Investor queries showed strong improvement as AI-cited coverage expanded beyond SEC filings to include trade press and published research. Company-specific queries became dominated by accurate, current descriptions rather than stale or missing information.
Qualitative impact was significant. The IR team reported fielding fewer calls from prospective investors who had been misled by outdated AI descriptions of the company. The communications agency reported that reporters were arriving at briefings better-informed because their AI-assisted background research now returned accurate information. A notable inbound partnership conversation was initiated by a business development contact at a larger pharma who had used an AI assistant to identify clinical-stage companies in the target indication.
What Made the Difference
Wikipedia completion: the most durable single change. The updated Wikipedia entry produced visibility gains that compounded across every AI platform update during the program and will continue to compound into future model releases.
Dedicated pipeline and therapeutic-area pages: moving key scientific content out of JavaScript-rendered investor portals into crawlable HTML unlocked AI citation for the most specific, high-value queries.
Coordinated publication and editorial strategy: accelerating the manuscript submission and sequencing trade-press coverage around phase 2 data produced a step-change in visibility precisely when new retail and institutional investors were researching the indication.
Lessons for Biotech
AI visibility for clinical-stage biotech is primarily an authority problem. The fix is not content production in the traditional marketing sense. It is getting the right editorial and scientific sources in place.
Wikipedia matters even for companies that feel too small for it. If your company is public and has clinical programs, you meet Wikipedia notability. Not having a complete entry is leaving AI visibility on the table.
JavaScript-rendered investor materials are invisible to AI. Biotechs with deep investor content locked inside portals, slide decks, and non-indexable formats should invest in HTML equivalents as a priority.
Publication timing matters. AI platforms increasingly retrieve recently-published scientific content through RAG. Coordinating publication around key investor milestones amplifies both the clinical narrative and the financial narrative.
AI visibility is becoming material for public biotech. The rate of AI-assisted investor and business development research will only increase. Companies treating AI visibility as optional are accepting a compounding disadvantage.
How Presenc AI Helped
Presenc AI provided the baseline audit, monthly tracking across AI platforms and therapeutic-area prompt sets, and early warning when AI characterization drifted from factual (which happened twice during the program, both times traceable to outdated press coverage and both resolvable with corrections at source). For the IR team, Presenc AI's quarterly reports became a standing agenda item alongside analyst coverage and trading metrics. For the communications agency, Presenc provided the measurement infrastructure that made AI-visibility deliverables accountable and trackable throughout the 12-month program.