Deep scientific analysis, in hours

We spent years building Panomics, a computational biology platform. That work put us close to research teams across pharma, biotech, and academia. We saw the same pattern everywhere.

Teams could handle the routine parts: QC, normalization, basic stats. The bottleneck was the deeper work: comparing methods head to head, checking whether a result holds under different assumptions, following a weak signal across three datasets to see if it's real. That work takes weeks of expert time. Most of it never got done.

From scattered evidence to grounded findings, in hours instead of weeks.

So we built Inflexa to do it. Give it transcriptomics, compound screens, microscopy images, it doesn't matter. The platform runs the analysis exhaustively: more methods, more comparisons, more follow-up than a person would attempt. It generates hypotheses and then tests them. When datasets are large and time is short, humans miss things. Inflexa doesn't.

But as we watched teams use the analysis agent, a pattern emerged. The deepest analyses still required extensive manual context gathering: searching databases, reading literature, cross-referencing clinical trials. The data analysis was fast, but the biological grounding was still a bottleneck. So we built Synapse: a target intelligence engine that continuously synthesizes evidence from 20+ databases and the full scientific literature. Now every Cortex analysis is grounded in structured, scored biological intelligence.

The output isn't a black box. Every result links back to the data, the code, and the method that produced it. You get answers in hours instead of weeks, and you can trace exactly how each one was reached.

Not the routine stuff: the findings nobody had time to look for.