Train private models
Fine-tune models on your own data, inside your own environment. You retain full ownership of the weights that come out, and they stay inside the boundary they were trained in.
Fine-tuning is not the answer to every question
It is worth being direct about this, because the wrong starting point costs months. Fine-tuning teaches a model your domain, your formats, and your way of doing things. It does not teach it today's facts.
Start with retrieval instead
If what you need is answers grounded in current documents, Enterprise RAG gets you there faster and stays correct as the documents change. Fine-tuning a model on facts means retraining it every time they move.
Fine-tune for behavior
Tone, structure, domain vocabulary, and the shape of a good answer in your organization are the things retrieval cannot give you and training can.
You need compute in your environment
Training runs where your data lives, which means GPU capacity on your side of the boundary. If you do not have it, BM in a Box ships the hardware pre-configured.
Smaller models, lower running cost
A tuned private model is usually much smaller than a general-purpose one for the same task, and it runs against your own capacity rather than metered public API tokens.
- BM in a Box The GPU capacity named in the precondition above, shipped pre-configured on NVIDIA DGX hardware.
- Privy AI The retrieval route named in the boundary above: answers from your own content, with the source named in the reply.
- Banking and finance Ledger Lens, where the models learn a firm's own transaction categories from the firm's own statements.
- Enterprise sales A decade of one organization's own bid data behind a recommended price, learned from the bids it actually won.
Bring the data you would fine-tune on
In one session we will look at what you have, say plainly whether training or retrieval is the right tool for it, and show you what the trained model would be built from.