Services · 2.3
Model adaptation for your business
We don't build the model — we adapt the best existing ones to your data, standards, and metric.
Overview
Frontier models are general by design. The value for your business is in the last mile: making a model answer from your knowledge, work to your standards, and optimize for your outcome. We don't build a model from scratch — we adapt the best available open and frontier models into a profession for your business.
Three layers, applied in the order the problem needs: knowledge (retrieval over your documents and data), skill (fine-tuning to your formats and edge cases), and outcome (optimization against your success metric). When data must stay inside your perimeter, we deploy locally — your data never leaves it.
What we take on
Knowledge, skill, outcome: we connect models to your data, teach them your standards, and optimize them against your metric.
- RAG: answers grounded in your documents and data
- Fine-tuning (LoRA) for your formats and standards
- Local deployment — your data never leaves your perimeter
What we provide
Retrieval (RAG)
We ground models in your documents, policies, and data, so answers come from your reality — with sources — not the open web.
Fine-tuning (LoRA)
We teach a model your formats, your standards, and your edge cases, so its output matches how your people actually work.
Outcome optimization
We drill the model against your success metric until its behavior is the result you wanted, not just a plausible answer.
Local & private deployment
On-premise or in your own cloud, when data can't leave your perimeter — full capability without sending data out.
Evaluation & guardrails
Test sets, scoring, and safety boundaries built around your task, so quality is measured, not assumed.
Frequently asked questions
- Do you train your own large language model?
- No. We adapt the best existing open and frontier models. The moat is domain adaptation — your knowledge, standards, and metric — not rebuilding the model itself.
- Can our data stay private?
- Yes. When data must stay inside your perimeter, we deploy locally or in your own cloud, and your data never leaves it.
- What's the difference between RAG and fine-tuning?
- RAG gives a model your knowledge to answer from; fine-tuning teaches it your skill and style. Most real systems use both, applied in the order the problem needs.
- How do you know the adapted model is good enough?
- We build an evaluation set and score against your success metric before anything ships, so quality is measured rather than assumed.
Who we work with
Manufacturers and factories, medical networks, and enterprises with complex, costly, manual-heavy problems — and the budget to solve them properly. We work with organizations driven to improve — worldwide.
Where we work
Worldwide. We're not tied to a location — we work remotely, and on site when the project calls for it.
We're selective, and we don't publish low-tier pricing. Every engagement is scoped to a real business metric. If applied AI can move your number, we should talk.