Enterprise AI · 2025
Retraining open-source LLMs for fully on-premise use
Fine-tuning and deploying open-source language models inside client infrastructure, so sensitive data never leaves the building.
- Client
- Confidential
- Services
- LLM fine-tuning · MLOps · Infrastructure
- Status
- In production
The challenge
The organisation wanted what everyone wants from a language model: a capable assistant that understands its internal documents, terminology and processes. What it could not accept was routing confidential material through a third-party API. Off-the-shelf cloud AI was ruled out before the project began.
Our approach
We started from a strong open-source base model and built a curated training set from the client’s own documentation and historical records. Using parameter-efficient fine-tuning, we adapted the model to the client’s domain without the cost of training from scratch, then quantised it to run comfortably on hardware the client already owned.
The model is served entirely behind the client’s firewall, with an evaluation harness that measures accuracy on real internal tasks and a defined path for retraining as the organisation’s data grows.
The outcome
The client now runs a language model that speaks its own language, on its own machines, with zero data leaving the network. The same pattern, open weights, domain fine-tuning, on-premise serving, is one we now offer to any organisation whose data cannot travel.