How We’re Speedrunning Biotech Market Research with AI
A peek under the hood at the workflows we use to map a complex industry
Market research with AI can be an insane advantage, but in specialized industries like biotech, you need to architect your approach carefully.
The challenge isn't that basic tools like ChatGPT and Claude aren't useful—they're exceptional for rapid analysis and knowledge synthesis. But unless you take control of the source data they tend to smooth out the exact irregularities and edge cases that often contain the most valuable insights.
For example, if you ask ChatGPT to give you the org chart for a typical biotech company, it will do an ok job, but there's a clear regression to the mean — it makes faulty assumptions based on "standard" org charts. So you get 80% right but the 20% is actually what you most need to understand because it's what makes those companies unique.
We've recently built some workflows that address this issue and that have helped us move 10X faster (at least). Here's a view of our current stack which is tuned for biotech but applicable to this kind of work more generally:
1. Cast a Wide Net
AI means you can process a basically limitless amount of information, so the first step is to gather as much quality source material as you can. We pull from everywhere:
Granola to record customer calls and other meetings with experts
Latest content from a select number of industry podcasts and newsletters (via RSS)
Conference talks and presentations
Technical blog posts and papers
Market research databases (Apollo, Clay, AlphaSense)
Academic research
Company blogs
The goal isn't to manually review all of this, it’s just to aggregate a corpus of quality source material relevant to our work. We also don’t worry about tagging this ourselves, we use AI for that too (more on that in a sec).
It's not a perfect solution, but right now we're maintaining our knowledge base of source material in Airtable. The main appeal is that it's fairly simple to use and build workflows on top of - especially for semi-technical BD people.
2. Build an AI Intern to Screen Content
In general you don’t need to be precious about the content you store, but adding irrelevant information makes everything harder (and more expensive). So we maintain a prompt flow that’s basically an AI intern that screens for relevance on content we’re not sure is quality.
The prompts include things like:
Key technical areas we're investigating
Specific market dynamics we're tracking
Types of customer problems we care about
When we're not sure about something, we chuck it at the prompt and get back a quick relevance score with reasoning. If it’s good, we add it to the database.
Maintaining this screening system has become a valuable exercise in itself. Writing down what we care about and why - then updating it as our understanding evolves - forces us to maintain really clear thinking across our team about our research priorities. As with a lot of AI workflows, the process of creating clear instructions leads to clearer thinking overall.
3. Enrich Content at Scale
Once you have raw source material in a database, you can enrich it in any way you want. For example, run a prompt over podcasts to extract examples of places where companies are currently using AI within regulatory compliance. Or a simple scraper that grabs information from company landing pages.
We use Pipedream for this - it hooks up nicely to Airtable and makes it easy to run content through LLMs or scrape additional data.
These all become searchable columns in Airtable. Nothing fancy - just automated enrichment that would be tedious to do manually.
4. Research Reviews at Lightning Speed
Here's where it all comes together - and where ChatGPT has been a game-changer. When we need to investigate something specific (like "What workflows do platform biotech companies use to source potential partnerships?”), we:
Pull relevant source material/extracted insights from our database
Feed it into ChatGPT with context about what we're trying to learn
Iteratively refine the analysis, asking follow-up questions, adding more sources
Summarize the analysis using ChatGPT’s document feature
The magic? I can then just share the chat history with others on our team — they don’t just get the summarized doc; they can also trace back how I got there.
Shortening the Path to Market for Bio
The above is a scrappy system we’ve whipped up for ourselves at Robo over the past month or so, but we’re also working on some more advanced versions with a few design partners in biotech. There’s low hanging fruit for any company, but especially as you get closer to biological data you need better evals and a bit more infrastructure.
What I find exciting about this work is that it’s a bit of an arbitrage — especially for cutting edge biotech, a big challenge is solving the needle in the haystack problem. There is a good partner or customer out there for you, but there’s also a good chance you don’t find them before time runs out. AI’s ability to process immense amounts of data means we can increase the odds of that successful match happening.
We’re just scratching the surface here. If you're doing market research in biotech - whether that's competitive intelligence, customer discovery, or opportunity analysis - we'd love to compare notes. We're particularly interested in what data you’re attempting to digest/synthesize and where current tooling falls short.