Service · Fine-tuning

Models adapted, to your domain.

We adapt proprietary and open-source models to your data, your vocabulary and your decisions — turning a general-purpose system into one that performs reliably on the work your institution actually does.

How we adapt models

LoRA & parameter-efficient tuning

Low-rank adaptation and related techniques specialise large models at a fraction of the compute and storage cost, enabling rapid iteration and clean separation between base weights and your proprietary adapters.

Instruction tuning

We teach models to follow your task formats, response structures and house style by training on curated instruction-response pairs grounded in your domain and operating procedures.

RLHF & preference optimisation

Where quality is a matter of judgement, we capture human preferences and apply reinforcement learning from human feedback and direct preference methods to align outputs with expert standards.

Distillation

We transfer the competence of a large model into a smaller, faster, cheaper one — reducing inference cost and latency while preserving the accuracy your use case requires.

Data curation & governance

Representative, de-duplicated and bias-reviewed datasets, with documented provenance, versioning and controls for confidentiality, leakage and regulatory traceability.

Evaluation & benchmarking

Held-out test sets, task-specific metrics and rigorous A/B comparisons against base and prior versions, so every gain is measured and every regression is caught before release.

Get in touch

Let's talk about your project

Tell us about the task, the data you hold and the standard you need to meet. We will scope a fine-tuning programme with explicit metrics, milestones and governance.