AI agents for real-world evidence
Most of your analysts' time goes to cleaning and joining data rather than generating insight, and every new question restarts the engineering. TrialMind runs natural-language cohort extraction across the sources you already license, turning a feasibility question into an answer in minutes — where the data lives, with a reproducible audit trail on every definition.
The question becomes the interface
Feasibility without the engineering cycle
Today a feasibility question takes weeks to months of data engineering before any analysis begins, so the go/no-go decision arrives late. TrialMind turns the question itself into the interface — the epidemiologist who has the question runs it.
Across the data you already license
Claims, EHR, registry, and genomic sources do not join cleanly, and each new question restarts the work. TrialMind reasons across the sources you already have, so adoption does not require another data purchase or an internal decision about which vendor wins.
Evidence that can go into a submission
FDA, EMA, and PMDA keep widening their acceptance of real-world evidence, so the same team is now asked for output that must withstand regulatory scrutiny — not only fill a slide.
Built to pass data governance and privacy review
Two things decide whether an RWE project happens: where the data has to go, and whether the method survives scrutiny. Both are answered in the architecture.
Runs where the data lives
TrialMind deploys into your environment and raw records never move. This is the objection that usually stops these projects, and it is answered architecturally rather than contractually.
Data-neutral by design
Works across the sources you already license. Adoption does not require another data purchase or a decision about which data vendor wins internally.
Reproducible cohort definitions
Definitions, funnels, and survival analyses carry a full audit trail, which is what lets the output support regulatory use rather than only internal discussion.
Why not just use a general-purpose model?
Your RWE data science team can build against a general-purpose model — that option is in the room whether or not anyone names it. The difference shows up when the analysis has to be reproduced, defended to a regulator, or run again next quarter on refreshed data.
Bring us a feasibility question you are working on right now — the kind that would normally take weeks of data engineering — and we will run it against your data. Minutes versus weeks is the demo.
Frequently Asked Questions
Do we need to buy data from you?
No. TrialMind is data-neutral and works across the claims, EHR, registry, and genomic sources you already license. Adoption does not depend on another data purchase, and it does not require you to settle which data vendor wins internally.
Does raw patient data leave our environment?
No. TrialMind deploys into your environment and analyses run where the data already lives. Raw records never move, which is what makes privacy and governance review passable.
Can the output support a regulatory submission?
That is what the audit trail is for. Cohort definitions, index dates, censoring rules, and analysis steps are captured and reproducible, and the underlying methods are published in the peer-reviewed literature so the approach can be defended by citation.
Who actually runs it — an epidemiologist or a data engineer?
The epidemiologist who has the question. Natural-language cohort extraction is the point: it removes the data engineering cycle that currently sits between the question and the answer.
How does it handle joining claims and EHR data?
TrialMind reasons across sources rather than requiring them to be pre-harmonised into one warehouse first. Where your data is already in a warehouse, we connect to it rather than asking you to move or duplicate it.
What kinds of questions is this used for?
Patient feasibility under protocol-like criteria, line-of-therapy reconstruction, biomarker and mutation detection rates, time-to-event and outcomes analysis, treatment pattern and temporal trend analysis, and safety signal detection using longitudinal and disproportionality methods.
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