Local Fine-Tuning and Evaluation
A fine-tuned model on your data, inside your infrastructure, with an evaluation report you can defend. Weights stay yours - no lock-in, no data leaving your perimeter.
Some workloads in medicine should not run against a public API. Private patient data, proprietary research corpora, and internal protocols belong inside your infrastructure. We fine-tune open-weight base models on your data without your data ever leaving your agreed boundary. You get the adapter, the weights, the training pipeline as code, and an evaluation report against a held-out dataset we build with your team. If your compliance, IT, or information security leaders need to see the approach before we start, we'll walk them through it. Book a scoping call to discuss what it would take for your environment.
Some workloads in medicine should not run against a public API. Private patient data, proprietary research corpora, and internal protocols belong inside your infrastructure. We fine-tune open-weight base models on your data without your data ever leaving your agreed boundary. You get the adapter, the weights, the training pipeline as code, and an evaluation report against a held-out dataset we build with your team. If your compliance, IT, or information security leaders need to see the approach before we start, we'll walk them through it. Book a scoping call to discuss what it would take for your environment.
Benefit
Your data never leaves your perimeter. Training happens inside your infrastructure or a tenant-isolated environment.
Evidence it works. You receive an evaluation report against a held-out dataset — not just claims of improvement.
The weights are yours. Adapter and full-tune weights ship as your artifacts.
The pipeline is yours. Training code delivered as a versioned repository you can re-run.
Right-sized, not oversold. We pick the base model for the workload, not for the pitch deck.
What's Included
A recommendation and justification for the open-weight base model.
Joint data curation — de-duplication, formatting, and held-out split.
A fine-tuning run — LoRA, QLoRA, or full fine-tune.
An evaluation report with documented metrics against the held-out dataset.
A reproducible training pipeline delivered as code.
Weights delivered as your artifacts.
Optional ongoing support — refresh cycles and drift monitoring under a monthly retainer.
Process
Scoping call. Discuss the use case, base model options, data, and infrastructure.
Agreement. Scope, price, and evaluation criteria signed off before any work starts.
Data preparation. Joint curation of training and held-out sets.
Environment setup. Training environment stood up inside your perimeter.
Fine-tuning. The run executes with intermediate checkpoints.
Evaluation. Held-out performance measured against the agreed criteria.
Delivery. Evaluation report, training code, and weights handed over.
Optional retainer. Refresh cycles and drift monitoring, if you want them.
Unit of delivery: one adapter or full fine-tune, with an evaluation report. Pricing: fixed price per adapter, plus optional monthly retainer. Turnaround: 2 to 6 weeks depending on base model size and data volume.
Want to see whether your data and environment are a fit? Book a scoping call.
Benefit
Your data never leaves your perimeter. Training happens inside your infrastructure or a tenant-isolated environment.
Evidence it works. You receive an evaluation report against a held-out dataset — not just claims of improvement.
The weights are yours. Adapter and full-tune weights ship as your artifacts.
The pipeline is yours. Training code delivered as a versioned repository you can re-run.
Right-sized, not oversold. We pick the base model for the workload, not for the pitch deck.
What's Included
A recommendation and justification for the open-weight base model.
Joint data curation — de-duplication, formatting, and held-out split.
A fine-tuning run — LoRA, QLoRA, or full fine-tune.
An evaluation report with documented metrics against the held-out dataset.
A reproducible training pipeline delivered as code.
Weights delivered as your artifacts.
Optional ongoing support — refresh cycles and drift monitoring under a monthly retainer.
Process
Scoping call. Discuss the use case, base model options, data, and infrastructure.
Agreement. Scope, price, and evaluation criteria signed off before any work starts.
Data preparation. Joint curation of training and held-out sets.
Environment setup. Training environment stood up inside your perimeter.
Fine-tuning. The run executes with intermediate checkpoints.
Evaluation. Held-out performance measured against the agreed criteria.
Delivery. Evaluation report, training code, and weights handed over.
Optional retainer. Refresh cycles and drift monitoring, if you want them.
Unit of delivery: one adapter or full fine-tune, with an evaluation report. Pricing: fixed price per adapter, plus optional monthly retainer. Turnaround: 2 to 6 weeks depending on base model size and data volume.
Want to see whether your data and environment are a fit? Book a scoping call.
Question in mind?
Let's Connect
Question in mind?