Solution · Tune
The fine-tune that makes the model speak your firm’s language.
Fine-tuning is parameter-efficient training — LoRA and adapters — on your precedents, records, tickets, and code, performed entirely inside your environment. The model learns your formats, terminology, and standards; your corpus never leaves; and the adapters are your property, portable across every future model upgrade.
Who it’s for
Organizations whose work has a house style.
Cloud fine-tuning has always carried a contradiction: the data most worth training on — two decades of firm precedents, clinical templates, engineering-change orders, internal code — is the data least defensible to ship to a third party. In-environment fine-tuning resolves it. The training pipeline runs where the data already lives, under the same access controls and audit logging as the inference stack.
It fits every deployment we build: law firms tuning on precedent corpora, clinics on documentation templates and vocabulary, financial institutions on policy and correspondence standards, manufacturers on the process archive. If your organization re-explains its own conventions to a chatbot daily, that is the signal.
The engagement
Corpus in, adapters out, evidence throughout.
- Corpus assembly. We identify, clean, and structure the training corpus with your teams — inside your environment, with access scoped to the project.
- Harness first. Before any training, we build an evaluation harness from held-out examples of your real work, with grading criteria your senior people approve.
- Train & iterate. LoRA/adapter training on in-environment or dedicated in-country hardware, iterating until the model clears your bar.
- Ship & carry forward. Adapters deploy to production; the harness re-validates them against every future base-model upgrade under the maintenance retainer.
Deliverables: trained adapters (your property), the evaluation harness and its results, corpus documentation, and a re-tuning runbook for future releases.
Questions we get
Frequently asked questions
How does fine-tuning work without our data leaving the building?
Training runs on the same in-environment hardware that serves inference, or on dedicated in-country training nodes. We use parameter-efficient methods — LoRA and adapters — over your corpus, and evaluate against a test harness built from your actual documents and tasks. Your data never transits a third party at any stage, and the resulting adapters are your property, portable across model upgrades.
What does fine-tuning actually improve over the base model?
Format, terminology, and standards. A base open-weight model is a strong generalist; a tuned one drafts in your house style, uses your defined terms correctly, follows your document structures, and applies your internal standards without being prompted into them each time. The gains are largest on repetitive institutional work — drafting, summarization, coding suggestions, correspondence — where consistency is the product.
How do we know the fine-tune worked?
Because it is measured, not vibed. Before training we build an evaluation harness from your real work — held-out documents, tasks, and grading criteria your senior people sign off on. The tuned model ships when it clears your bar on that harness, and the same harness re-validates every subsequent model upgrade or adapter migration.
Teach the model your standards — without exporting them.
The two-week sovereignty assessment scopes your corpus, workloads, and evaluation criteria, and hands you a written architecture with a real cost model.
Book a sovereignty assessment