Biotechnology
Sequence-function modelling, structure and design pipelines, and the assay data infrastructure underneath — with a documented answer to the only question that matters: how much should you trust this model?
The premise
Most protein ML work fails on validation, not architecture.
A model that scores well on a public benchmark and then fails on your internal data has not saved you time — it has cost you a build cycle, and you find out months later.
VyteK came out of test engineering. That is an unusual origin for an ML team, and it is the whole point: we start where most teams stop. Held-out benchmarks. Calibration checks. A documented failure mode. Performance written down in terms your partners and regulators can actually read.
Verification before belief.
Capabilities
Four of these you could assemble from open tooling given enough time. The first one is where projects actually succeed or quietly fail.
Held-out benchmarking, calibration checks, and documented model performance that stands up to partner and regulatory diligence. Every model ships with a golden test set, a stated failure mode, and a number that says how much to trust it.
Sequence-function models and variant effect prediction, wrapped in active-learning loops that prioritise which variants are worth building next — so wet-lab cycles are spent on the candidates most likely to move.
Production deployment of AlphaFold, ESMFold, ProteinMPNN and RFdiffusion workflows, with reproducible versioning and compute cost under control rather than discovered at the end of the month.
Stability, expression, affinity and developability predictors trained on your internal data — not only on public benchmarks that may look nothing like your constructs.
Ingestion from LIMS and ELN systems, schema design, and clean linkage between construct, assay and outcome. Most modelling problems turn out to be data-linkage problems first.
No exceptions, including for our own internal work.
Let's work together
A system that has to hold, a model nobody can vouch for, a team stretched past capacity — start with the problem and we will tell you honestly whether we are the right people for it.