Clinical R&D
Find better development paths.
DeepTrain
Your hardest decisions need more than chat
Pharma’s hardest decisions span teams and systems, draw on evidence accumulated over time, and involve choices that affect one another. DeepTrain enables frontier models to work for days or weeks, exploring those connections and uncovering paths a chat interaction can’t examine.
Use cases
Patient services
The opportunity is bigger than productivity
Each generation of frontier models opens up work that was previously out of reach. Given a goal and time to pursue it, agents can explore more possibilities, challenge assumptions, and investigate problems that span teams and systems.
That matters in pharma, where decisions rarely turn on a single fact. A development path or patient support issue can span evidence, systems, and teams—and still require expert judgment. Agents can work through that complexity, test possibilities, and give experts a clearer basis to act. Getting these decisions right can create enormous value.
Built around your problem
Models alone are not enough for complex enterprise work. They need the problem's evidence, relationships, rules, history, and current state—along with tools to investigate options and learn from tests, simulations, and results.
DeepTrain builds a custom environment around your problem, connecting its context and tools. Agents investigate different possibilities, test and challenge their findings, and use failed attempts and new evidence to guide the next pass.
Read Too Smart to PromptHow we approach
a problem.
A better-supported development decision. A resolved patient support issue. We build systems that let agents pursue these outcomes through sustained investigation, testing, and revision.
Connect the evidence
Bring together the study, biomarker, and decision records that explain the question. Identify conflicting findings and the evidence that is still missing.
Give agents the right environment
An out-of-the-box agent isn’t built for your hardest problems. We connect it to the relevant context, tools, and feedback so it can coordinate work across attempts and use verification to test findings and guide what happens next.
Investigate, challenge, refine
Agents pursue competing explanations in parallel. Other agents look for contradictions and try to disprove the findings. Failed attempts guide the next investigation; promising findings get tested further.
Turn findings into action
Deliver the next study decision or resolved support action. Show what supports it, what remains unresolved, and what would change the recommendation.