Ask Mira anything about your trial’s data.

Any question, over any of the study’s data.

Ask in plain language and Mira answers with charts, tables and written analysis, computed from the study's own data — scores and model predictions, item-level discordance, adherence, protocol windows, transcripts, secondary endpoints. It runs the analysis with clinical-trial tools built for it, inside your environment; nothing is recalled from memory.

Overnight it also looks on its own, and leaves a short briefing of things worth a second look.

A trained model scores the primary endpoint.

In CNS trials the primary endpoint is typically a rater-administered scale — MADRS, HAM-A, HAM-D — so rater error lands straight on the result the study is judged by. Mira’s model is trained on expert-rated interviews and scores the same questionnaire audio item by item, independently of the site rater.

Where the two disagree, that gap is measurement error on your endpoint — drift, over- and under-scoring, assessments worth a second look — caught while the study is still running. One engine covers any interviewer-rated scale, and every score traces back to the moment in the interview that drove it.

One site, in study context.

Every site becomes a review-ready dossier: whether its scales agree with what the primary endpoint implies, whether the questionnaire battery is complete, whether visits happen in window, and how enrolment and retention are tracking.

Nothing is judged in isolation — each site is weighed against every other site and against the study as a whole, so one that looks acceptable on its own still surfaces as the weakest in the study. Early enough to act on while the site is still enrolling, not a finding at database lock.

All of it in one secure platform.

Mira sits as the analytics layer on the data you already collect — in your own cloud or our dedicated Virtual Private Cloud, connected to your EDC, CTMS, data lake or warehouse.