Deep scientific analysis, in hours

We spent years building Panomics, a computational biology platform used at biopharma organizations around the world. That work put us close to translational and discovery teams across pharma, biotech, and academia. We saw the same pattern everywhere.

Most assets that move from preclinical to clinical fail. The warning signs are usually there in the data, but nobody has time to find them. Teams handle the routine parts: QC, normalization, basic stats. The bottleneck is the deeper work: comparing methods head to head, checking whether a biomarker holds across cohorts, following a weak signal across three datasets to see if it is real or noise. That work takes weeks of expert time. Most of it never gets done.

The signals that kill an asset in phase 2 are usually visible earlier, if someone has time to look.

So we built Inflexa to do that work. The platform runs analysis exhaustively: more methods, more comparisons, more follow-up than a person would attempt. It generates hypotheses, then tests them against the data and against the literature. When datasets are large and time is short, humans miss things. Inflexa doesn't.

The output isn't a black box. Every result links back to the data, the code, and the method that produced it, with PubMed-cited evidence chains tracing each claim to source. You get answers in hours instead of weeks, and you can defend every one of them in a review meeting.

Advance the assets that hold up under scrutiny. Catch the ones that don't, before they cost a trial.