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A model that speaks your organisation's language.
A general model is competent at everything and expert in nothing of yours. Fine-tuning closes that gap — but fine-tuning through a cloud service means handing over the very data that makes the model valuable.
The problem
The data that would make the model yours is the data you cannot share.
Fine-tuning teaches a model your terminology, your formats, and your judgement. To do that, it needs your records — matters, notes, reports, correspondence. A cloud fine-tuning service requires you to upload that corpus to its platform.
Read the terms and the picture sharpens: your data may be retained, may inform future models, and certainly leaves your control. For regulated work, and for anyone with trade secrets to protect, that is the end of the conversation.
Our solution
Train on your premises, keep the result.
Electric Azimuth fine-tunes an open-weight model on your data, on your hardware, with no upload at any stage. The trained model is yours to keep and run. We benchmark it against a general baseline on your own tasks, so the gain is demonstrated rather than promised — and the data that produced it never leaves the room.
Key features
Trained on your data
The model learns your terminology, formats, and conventions from your own records — offline, on your hardware.
Your weights, your asset
The trained model belongs to you. It runs where you choose and does not depend on a vendor to operate.
No training egress
Training data never leaves the network. There is no upload step and no external compute.
Measured against a baseline
We benchmark the tuned model against a general one on your tasks, so improvement is evidence, not assertion.
Versioned and reproducible
Each training run is recorded — data, parameters, and result — so you can audit and roll back.
Maintained over time
As your data and language shift, the model is retrained on the same private footing.
Use cases
Where it earns its place.
Tune a model to draft and review in the firm's house style and on its precedent, without that material training a public model.
Adapt a model to clinical coding and local terminology while patient data stays inside the trust.
Shape a model to internal product language and policy without exposing positions or client information.
Train on engineering reports and standards so the model speaks your discipline, with trade secrets contained.
Technical overview
Specifics for the people who will run it.
- Approach
- Parameter-efficient fine-tuning and full fine-tuning, chosen to fit the task and the hardware.
- Base models
- Open-weight models selected for licence, capability, and on-premise viability.
- Training data
- Your documents and records, prepared and curated with you; never uploaded.
- Compute
- GPU server or cluster sized to model and dataset; on-site or in your data centre.
- Output
- A versioned model artefact you own, plus a benchmark report against the baseline.
- Connectivity
- None required. Training and inference run inside your boundary.
Start with a scoped proof of value.
We tune a model on a defined slice of your data and report the measured improvement before any wider commitment.