AI agents for real-world evidence
Most analyst time goes to cleaning and joining data, and every new question restarts the engineering. TrialMind turns a feasibility question into an answer in minutes — across the sources you already license, where the data lives.
The question becomes the interface
Feasibility without the engineering cycle
Feasibility takes weeks of data engineering before any analysis begins, so go/no-go arrives late. TrialMind makes the question itself the interface — the epidemiologist who has it runs it.
Across the data you already license
Claims, EHR, registry, and genomic sources do not join cleanly, and each question restarts the work. TrialMind reasons across what you already license — no new data purchase, no vendor decision.
Evidence that can go into a submission
FDA, EMA, and PMDA keep widening their acceptance of real-world evidence, so the same team now owes output that withstands regulatory scrutiny.
See TrialMind in action
Real screenshots from a HER2 metastatic NSCLC study — the cohort attrition table, then the survival analysis run across it.
Weeks → minutes
Feasibility study turnaround
20+
Data scientists and technical sales users
A precision oncology diagnostics company
Feasibility studies from weeks to minutes
Twenty-plus data scientists run oncology feasibility studies. They upload the attrition table they already use; TrialMind writes the SQL and returns the answer.
Described by organisation type. Named references available under NDA.
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 — the objection that stops these projects, answered architecturally.
Data-neutral by design
Works across the sources you already license. No new data purchase, and no internal decision about which vendor wins.
Reproducible cohort definitions
Definitions, funnels, and survival analyses carry a full audit trail — what lets the output support regulatory use, not just 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 — no additional purchase, and no need to settle which 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 methods are published 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 pre-harmonised into one warehouse. Where your data already sits in a warehouse, we connect to it rather than move 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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From top pharma companies to academic medical centers
