LLM & SLM engineering
We adapt models to your domain — retrieval systems, fine-tuning and alignment, synthetic training data and honest evaluation — so quality is something you can measure instead of hope for.
Typical starting points
Capabilities
Retrieval that actually retrieves: chunking and indexing tuned to your documents, hybrid search, reranking, grounded answers with citations and measured answer quality.
Domain-specific models built on open-weight bases, adapted to your terminology, formats and task structure instead of prompt-patched into approximate behaviour.
Supervised fine-tuning, LoRA/QLoRA and other PEFT methods, plus preference alignment with DPO, ORPO, KTO or RLHF where behaviour matters more than raw accuracy.
When labelled data is thin, we generate and validate task-specific training data — seeded from your real examples, deduplicated, quality-filtered and checked for leakage.
Task-level benchmarks built from your own data, so every fine-tune, prompt change or model swap is judged on the work it has to do.
Distilling a frontier-model workflow into a small model you can serve cheaply — with quantization, serving setup and latency work included.
Choosing an approach
The decision should come from measurements, not fashion. We set up the evaluation first so the choice is evidence-based.
Knowledge changes often, answers must cite sources, and the model needs facts rather than new behaviour. Usually faster, cheaper and easier to keep current.
The task has a fixed shape, a house style or a format the base model keeps missing — or the volume makes a smaller specialised model far cheaper to run.
Questions
Next step
Bring the workflow, the constraints and the data. We will tell you what is realistic, what it costs and how we would build it.
