Data before parameters
We begin with the objective, a baseline and a traceable dataset. Then we choose fine-tuning, retrieval or a combination.
05 / What we build
Not larger. Better adapted.
Adapt an open-weight model to your domain, language and tasks. Measure the difference on your own data and fit inference to your hardware.
From training to serving
LoRA, QLoRA, evaluation and quantisation are separate steps. We distinguish trained, evaluated and production-ready models.
WHAT YOU GET
Targeted training
Domain-specific instructions and tasks, with separate evaluation data.
Efficient inference
Quantisation where it helps quality, memory use and speed.
Meaningful comparison
Measurements on your tasks, without universal benchmark claims.
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