Put AI on regulated safety and quality data — without exposing it, and without losing the record
The problem
Adverse-event intake and GMP/lab records are exactly the data pharma most wants AI on and least can afford to expose or leave unverifiable. Data-integrity enforcement is tightening — FDA drug/biologics warning letters are up sharply year over year, and a large share cite data-integrity failures.
Why Luminous fits
Luminous runs AI on adverse-event and lab data inside hardware-isolated, attested compute, so the data and the model stay protected in use, while every processing step is independently verifiable. Records are tamper-evident and append-only.
Evaluated on synthetic data only — never real PHI or GxP records pre-NDA.
Luminous makes the system of record and the computation on it verifiable and tamper-evident — it does not, and does not claim to, prevent bench-level human fraud unless the data capture itself is instrumented. The record can't be silently altered and every result is verifiable; that is not the same as preventing fraud, and we won't say it is.
The proof, and the ask
We have no reference logos yet, so we don't ask you to take our word. It's a working system, reduced to practice on real silicon, with reproducible evidence you can inspect. And the founder will walk you through it directly.