Built so professionals shape the AI for their field.
Clinical evidence review is going to be transformed by AI. The question isn't whether — it's whose judgment shapes it. Generic AI labs are training on public clinical data and producing tools that work for nobody in particular. TrialScope's bet is different: that AI for clinical evidence review should be calibrated by the actual professionals who do this work, with their corrections captured per-persona and compounded into role-specific intelligence over time.
The architecture supports that thesis from day one. Verified contributors via LinkedIn or a confirmed institutional email. Sixteen professional personas served, from regulatory affairs specialists to medical writers to investment analysts evaluating biotech evidence. Twenty-two recognized document types with built-in knowledge of ICH E3, CONSORT, and ICH M4 conventions. Seven review perspectives that adjust the AI's reasoning lens to the role you actually do. Structured corrections that compound into a defensible, persona-tagged calibration corpus.
TrialScope is currently in early access. It's functional, it produces useful output, and it's being actively shaped by the verified professionals who use it. The platform improves every time a regulatory affairs reviewer corrects a finding, every time a biostatistician flags a missed methodological concern, every time a medical writer adds clinical context the AI lacked. Your corrections today inform audits run by your peers tomorrow.
A NOTE ON THIS PAGE
This page will expand as the company grows. Founder bio, clinical advisory board, and detailed company history will be added as those structures formalize. For now, this is the strategic posture and the architectural commitments behind the work.
What we're committed to
Honesty about what AI is good at — and what it isn't. TrialScope is decision-support, not a replacement for professional review. Every output is a starting point for your expertise to engage with.
Verified-credential calibration — every contributor whose corrections shape the AI is identified through LinkedIn or a confirmed institutional email, with optional professional credentials on top. No anonymous-account training signal.
Persona-tagged contributions — corrections are captured with the contributor's verified persona, so the AI learns role-specific reasoning patterns rather than averaging across very different professional perspectives.
Clear consent framework — the Strategic Middle Path governs how calibration data can be used. Permitted uses preserve TrialScope's freedom to evolve; prohibited uses protect contributors from third-party commercial exploitation.
Building for the individual professional — the regulatory specialist, the medical writer, the working biostatistician, the independent reviewer. Not the $100K enterprise deal that requires a year-long procurement cycle.