Scientific data does not forgive sloppy modelling. The datasets are small and expensive, the effects are subtle, and the audience - reviewers, regulators, your own R&D lead - will ask exactly the questions a demo never has to answer. This is the environment our methods come from: models grounded in the physics and biology of the problem, uncertainty quantified on every prediction, and explanations an expert can interrogate. Our team spans applied biology to theoretical physics, and we publish in the fields we model.
Materials
From production floors to R&D labs: automated analysis of micrographs and sensor data, defect detection on the line, property prediction from composition and structure, and virtual screening across search spaces too large to measure - driven by experimental data or by atomistic models from quantum-mechanical calculations. Our current flagship is a self-supervised foundation model for microstructure imagery - the model behind our one-day weld experiment, and behind MicrostructureDB below.
Life sciences
Biostatistics for clinical and environmental studies, detection and classification in biological imagery - organisms, lesions, cells - and lab-workflow software with language models drafting the structured reports experts sign. The common thread with everything else on this page: measured error rates and calibrated confidence, because in this domain "the model said so" convinces no one and should not.
Products and projects
The fastest way to see what our materials AI does is to use it - MicrostructureDB is the productised form of the same foundation models we deploy in consulting, approaching public release.
MicrostructureDB
Microstructure analysis as a platform: upload micrographs, search by visual similarity, run foundation-model analysis without training anything yourself.
AI4MI course
Our ESF-funded training programme: AI for materials scientists, from working knowledge to working models.
AID4GREENEST
Horizon Europe project on AI-accelerated characterisation of green steels - our modelling, delivered at consortium scale.
Research alongside industry
We work on both sides of the applied-research boundary: consortium partner and subcontractor in European and Flemish projects (Horizon Europe, VLAIO, ESF), standing collaborations with the universities our team came from, and proposal support for companies that want a funded route to de-risking their AI plans. If your problem still looks like a research project rather than a contract, that is a conversation we know how to have.
How we can help
Data & ML
The modelling practice behind these applications - vision, language and prediction with uncertainty.
The method
Foundation models, domain adaptation, fast models, conformal calibration - how scientific rigor survives contact with production.
Development
Gathering new data, or visualising it in an app or on the web? We build and deploy the software around it.
Model validation & deployment
An independent review of a model you already rely on - calibration, evaluation leakage, shortcut learning. Two to three weeks, fixed price.