The problem space
Living systems drift. Cell lines age, raw-material lots differ, equipment varies between cleanings. A batch that's "the same as last time" rarely is, exactly — and the deviations that matter are easy to miss until you're trying to release the lot. By then the most useful question — "what changed?" — is the hardest to answer from a stack of disconnected spreadsheets and instrument logs.
Release adds the regulatory layer: you not only need the process to be consistent, you need to be able to demonstrate that consistency in a way that holds up to a reviewer who wasn't in the room.
What we're exploring
We're building toward a digital twin that captures the trajectory of each batch in a form a team can actually compare — not just as graphs but as a model that knows what "normal" looks like for this process at this scale and can flag where this batch diverged. The goal is to make consistency observable, and release a confirmation of something you already understood.
The work is part of our FENG-funded R&D project; we'll share more as the platform takes shape.