Not Her First Rodeo in Biotech AI
Relieving the massive documentation burden of drug submissions
My guest on Life Sciences Today this week was Anita Modi, CEO and founder of Peer AI. Anita and her team are using AI to solve the painful, low-tech problem of documentation in drug submissions.
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Intro
Anita Modi has seen the clinical-trial documentation mess from the inside — and now she’s building Peer to fix one of the industry’s biggest anti-patterns: running drug development through Word docs, spreadsheets, and reactive review cycles.
Anita Modi is not a first-time founder parachuting into biotech with a shiny AI pitch.
This is very much not her first rodeo.
Before starting Peer, she was Chief Quality & Business Transformation Officer at Science 37, one of the companies that helped define decentralized clinical trials.
At Science 37, she had a front-row seat to how drug development actually works: fragmented systems, endless review cycles, manual data entry, and massive documentation burdens that still shape how therapies get to market. In other words, she didn’t arrive at this problem from theory — she lived it.
What pushed her to build Peer was a painfully concrete example of just how broken the process can be.
A poorly written protocol, caused by what sounded like a copy-paste eligibility criteria error, cost millions of dollars at a clinical site. That moment made clear that regulatory documentation isn’t just administrative overhead; it is the operational backbone of how trials run and how approvals happen.
The biggest anti-pattern in the industry is DIY
One of the sharpest points Anita made is that many teams still think they can DIY AI-driven documentation.
On the surface, that sounds reasonable: take a large language model, add some prompts, connect a few internal documents, and assume you have an AI solution.
But in drug development, documentation is the wrong abstraction.
This is not just a writing problem.
It is a quality problem, a workflow problem, a traceability problem, and ultimately a patient-access problem.
That may be the biggest anti-pattern in the industry: treating high-stakes regulatory documentation like generic content generation. In reality, these documents are interconnected, reviewed in cycles, tied to underlying data, and expected to hold up under regulatory scrutiny. If one change does not propagate correctly, or one inconsistency slips through, the downstream cost can be enormous.
Outro
Peer’s thesis is that AI should act less like a drafting tool and more like a true partner to expert teams — orchestrating workflows, checking consistency, improving quality, and helping teams get ahead of issues before regulators find them. That is a much more serious vision of AI in life sciences — and a much more credible one.
Watch the pod here
Visit Peer here to author and accelerate your submissions with speed and quality.
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I write a weekly essay on strategy, technology, and decision-making.
I am founder of OpenCRO and advise WHO EU on cyber and privacy in digital health.