Language models tuned to your domain
When prompting a general model is not enough, we adapt one to your domain — parameter-efficient fine-tuning on your data, evaluated against benchmarks built from your own tasks, and deployable inside your environment.
Technology we build with
When off-the-shelf models hit their ceiling
Prompt engineering takes you far, but domain jargon, strict output formats and cost or latency targets eventually justify adapting a model. We run an eval-driven programme — curating data, tuning adapters, measuring against benchmarks built from your own tasks — so every training run is justified by a gain you can see rather than assumed.
- Data curation to build a high-signal training set from what you already have
- Parameter-efficient tuning (LoRA / QLoRA) that avoids the cost of full retraining
- Domain adaptation so terminology, formats and conventions match how you work
- Evaluation benchmarks built from your own tasks, so gains are measured not assumed
1from langgraph.graph import StateGraph2from tools import search_docs, lookup_order3graph = StateGraph(SupportState)5graph.add_node("retrieve", search_docs)6graph.add_node("act", lookup_order)7graph.add_node("review", human_approval)8agent = graph.compile()10result = agent.invoke(ticket) # traced end to end
A model program built on evidence, not vibes
Every step from data to deployment, measured against a domain-specific evaluation set.
Domain fine-tuning
Adapt open models to your terminology, formats and tasks with parameter-efficient training.
Parameter-efficient tuning
LoRA and QLoRA adapters that specialise a base model on your data without the cost or risk of full retraining.
Domain adaptation
Teach the model your terminology, output formats and conventions, so results match how your organisation already works.
Data curation
Sourcing and cleaning the examples you already have into a high-signal training set.
Eval-driven iteration
A domain benchmark that gates every checkpoint so improvements are proven, not assumed.
Optimized serving
Quantisation and efficient inference on runtimes such as vLLM, so a tuned model runs within your budget.
Custom models for specialized work
Where a purpose-built model outperforms a general one.
Evaluation-driven
Every build ships with an evaluation suite, so quality is measured rather than asserted.
Deployed your way
Your cloud account, VPC or on-premise — including open-weight models where data cannot leave.
Source-code handover
You receive the code and the documentation. No lock-in to us to keep it running.
Human in the loop
Approval gates and review queues wherever an automated mistake would be costly.
Common questions
When you need consistent domain formatting, lower latency or cost, or accuracy that prompting can't reach on specialized tasks. We validate the case with evals before recommending a training program.
Not sure fine-tuning is the right answer?
Often it is not. We benchmark a tuned model against your best prompt first, and only recommend training when it clearly wins.