Technology / Training & Fine-Tuning
Your model. Your knowledge. Your perimeter.
We train and fine-tune small, local models on an organisation's own governed knowledge — sovereign AI that is private, efficient, and economically defensible.
Small models on governed knowledge — not bigger models on generic data.
The default reflex is to reach for the biggest general model. For a bounded, well-structured domain it is often the wrong tool: expensive, slow, and trained on everything except what makes your organisation distinctive. We take the opposite route — smaller models, trained on your own knowledge, that you actually own.
What we do
Fine-tuning on your knowledge
We adapt open models to your domain, terminology and tasks — trained on a governed selection of your own data, not generic web text.
Small language models (SLMs)
Smaller models, specialised. On a narrow, well-structured domain an SLM can approach large-model quality at a fraction of the cost and latency.
Local & sovereign deployment
Models that can run on your infrastructure. Your data never leaves your perimeter — private by construction, not by policy.
Continuous updating
Knowledge ages. We build the loop that keeps a model current as your governed knowledge changes, instead of a one-off training run.
The substrate
Powered by OIDA.
Training a model on organisational knowledge first requires making that knowledge computable. OIDA — our epistemic infrastructure — gives every unit of knowledge a typed, scored, time-aware status, so what we train on is governed and current, not a dump of stale documents.
Explore OIDA →