Fine-tuning that never leaves your network: from raw documents to an evaluated, deployed, domain-expert model — entirely on GPUs you control.
We run the full adaptation pipeline on-premise: dataset extraction from your documents and systems, cleaning and deduplication, instruction formatting, LoRA or QLoRA fine-tuning, and rigorous evaluation against a held-out set before anything reaches production.
Fine-tuning pays off wherever your domain has vocabulary, structure, or language that frontier cloud models handle poorly — and wherever the training data itself is too sensitive to upload. Both conditions are common; together they make local training the only viable option.
Training hardware is a sizing exercise, not a shopping spree. A 7–14B parameter QLoRA fine-tune runs comfortably on a single modern workstation GPU; 70B-class work needs data-centre cards. We spec, procure or rent, and operate — and when a job only needs a big GPU for a week, we set up a temporary isolated node instead of selling you one.
Bring your corpus and your constraints — we'll scope the model, the hardware, and the pipeline.