Service 03

Fine-tuning & Adaptation

Domain-specific model adaptation and evaluation frameworks grounded in your actual business metrics.

The problem

Generic models miss the vocabulary, tone, or reasoning your domain requires — and blindly fine-tuning without measurement burns budget without a signal on whether it actually helps.

Our approach

We build the evaluation harness first — the metric that ties model quality to business outcome — then run a bounded adaptation pass and report improvements against that metric, not vanity benchmarks.

Use cases

Extracting structured fields from unstructured domain text (clinical notes, contracts, technical reports)
Adapting tone and vocabulary for a specialised professional audience
Building the evaluation harness that proves whether adaptation actually moved the metric that matters
Deciding, with evidence, whether fine-tuning is worth it versus a better prompt or retrieval layer

Representative stack

Open-weight models for adaptation where licensing allowsClinician/expert-reviewed evaluation datasetsPer-field accuracy and regression trackingPrompt-engineering and retrieval baselines as a control

How we engage

We won't recommend fine-tuning if a better prompt or a retrieval layer solves it cheaper — the evaluation harness tells us which, honestly.

Where you can see it

AI Kraft adapts tone, slang, references and rhythm per cultural market — a native voice for 10+ regions from a single brief. It's fine-tuning where the metric that matters is cultural fluency, not perplexity.

LiveAI Kraft

Have a problem shaped like this?

Tell us what you’re building and we’ll scope where Fine-tuning & Adaptation fits.

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