Mechanistic + machine-learning modelling
Calibrated uncertainty and abstention

Biology first
We start from the biological or clinical problem, not from a preferred AI architecture.

Uncertainty matters
Our systems quantify uncertainty and can abstain when the evidence is insufficient.

Constraints are built in
Biological, clinical and safety constraints are part of the model and evaluation process.

Validation before claims
We benchmark, test and define explicitly what the evidence does — and does not — support.

Constraint-aware inference
Predefined validation gates
Reproducibility and auditability

Therapeutic discovery
Experimental validation, target biology, translational models.

Bioprocessing
Cell-culture data, prospective experiments, process-development validation.

Clinical research
Clinical datasets, imaging cohorts, protocol-design and feasibility workflows.