LLM fine-tuning services
We adapt open-source models to your domain with LoRA, QLoRA and full fine-tuning — trained on your infrastructure so proprietary data never leaves, and evaluated against your real tasks before anything ships.
Our fine-tuning method
Fit assessment
Fine-tuning is not always the answer. We first test whether prompting or retrieval-augmented generation solves the problem more cheaply — and tell you honestly if it does.
Data curation
Auditing, cleaning and structuring your examples into training sets, with privacy controls and documentation of data lineage.
Training
Parameter-efficient methods (LoRA, QLoRA) by default to cut compute cost; supervised fine-tuning or preference optimisation (DPO) where behaviour shaping is needed.
Evaluation
A task-specific benchmark suite comparing the tuned model with the base model — the go/no-go decision is made on your metrics, not ours.
Deployment
Adapters or merged weights served through your existing on-premise stack, with a repeatable pipeline for future refreshes.
Frequently asked questions
Fine-tuning or RAG — which do we need?
Use retrieval when the problem is access to knowledge; use fine-tuning when the problem is behaviour, format or domain language. Many production systems combine both. Our assessment phase settles this with evidence, and our guide on fine-tuning vs RAG covers the decision in depth.
How much data do we need to fine-tune?
Useful behaviour changes often start from a few hundred high-quality examples with parameter-efficient methods. Data quality matters far more than volume.
Does our training data leave our environment?
No. Training runs on your hardware or in your private cloud tenancy. We work inside your perimeter under your access controls.
Who owns the resulting model?
You do — weights, adapters, training pipeline and evaluation suite are all handed over as deliverables.