Solutions · 01 — Custom AI Models & LLMs
Models that know your domain.
Fine-tuned language models and retrieval pipelines built on your corpus, evaluated against your questions, and deployed wherever your policy says the weights may live.
A general model has read the internet, not your files. It does not know your programme names, your policy numbering or the way your officers write. We close that gap with retrieval first, fine-tuning where it earns its place, and an evaluation set you own.
What we deliver
Retrieval pipelines
Search over your document estate, feeding the model the passages it should answer from — with citations an officer can check.
- Hybrid keyword + vector search
- Access-filtered at query time
- Source citations on every answer
- Incremental re-indexing
Fine-tuned models
Open-weight models adapted to your terminology, document formats and tasks, trained in an environment your data never leaves.
- LoRA and full fine-tuning
- Training on-premise or air-gapped
- Weights delivered to you
- Version-pinned releases
Evaluation and alignment
A test set built from your real questions, scored before every release, plus red-teaming for the failure modes that matter to you.
- Client-owned evaluation set
- RLHF and preference tuning
- Red-team report per release
- Regression gates in CI
How an engagement runs
01
Scope the decision
Which task, which users, what happens when the model is wrong. This sets the evaluation bar.
02
Build the test set
Real questions with known answers and their source passages, written with your subject-matter experts.
03
Retrieval, then tuning
Retrieval is measured first. Fine-tuning is added only where the test set shows it helps.
04
Release and monitor
Shipped behind a release gate, then watched in production for drift against the same test set.
Built for regulated environments
Controls that ship with it. Not options to negotiate later — the defaults every engagement starts from.
No cross-client training
Your data and anything derived from it trains your model only. It is never pooled.
Classified-ready
Training and inference can run fully air-gapped, with offline model updates.
Model lineage
Every release records its base model, training data snapshot and evaluation results.
Exit by design
Weights, adapters and indexes are returned in open formats at the end of the engagement.