Enterprises in regulated industries such as finance, healthcare and government want AI that keeps sensitive data inside and runs on infrastructure they control. That demand is moving them beyond retrieval-augmented generation (RAG) toward models trained or fine-tuned on their own language and ways of working.
Why private AI matters
Public AI APIs send data outside the network, which regulated sectors often cannot accept. Private deployments give data locality, self-hosted inference and a better fit with compliance obligations, at a higher upfront cost.
How RAG works
RAG retrieves relevant internal records at question time and gives them to the model as context. A security chatbot, for example, fetches failed-login records before answering an analyst who asks why an account was locked.
Where RAG runs out
- Retrieval errors flow straight into the answer.
- Generic models misread specialised jargon.
- Large record sets exceed the model’s context window.
- The model doesn’t learn from one session to the next.
Domain-specific training
Fine-tuning on internal terminology and analyst feedback builds deeper fluency and reduces hallucinations. It doesn’t replace retrieval; it makes the model better at using what it retrieves.
| RAG | Domain training | |
|---|---|---|
| Set-up | Fast and cheap | Needs curated data |
| Best for | Looking up facts | Jargon-heavy, specialised reasoning |
| Upkeep | Keep the index fresh | Periodic retraining |
Most mature systems use both: RAG to start fast, training to make the system feel native to the enterprise.
Security practices we insist on
- Keep secrets out of application code.
- Redact personal data and credentials before anything is used for training.
- Enforce access control on what the model can retrieve.
- Audit what flows into logs, caches and training sets.