Artificial Intelligence
LLM Engineering
Large language models, made dependable in production.
Most LLM projects stall between a promising demo and a system you can trust. We close that gap — fine-tuning, grounding, evaluating and deploying models that hold up under real traffic.
Whether you need a private model on your own infrastructure or a fine-tuned adapter for a specific task, we build the full pipeline around it: prompts, context, safety, monitoring and cost control.
What's included
- ✓Fine-tuning and lightweight adapters (LoRA)
- ✓Prompt and context engineering systems
- ✓Private and on-premise model deployment
- ✓Inference optimisation and caching
- ✓Safety, red-teaming and evaluation
- ✓Token-cost and latency budgeting
How we work
A way of working that holds up.
Right model, right job
We pick the smallest model that meets the bar — balancing quality, speed and cost rather than reaching for the biggest by default.
Measure everything
We build evaluation sets specific to your use case, so quality is a number you can track, not a vibe.
Own your stack
Where it matters, we deploy models you control — your infrastructure, your data, no third party in the loop.
Questions
LLM Engineering, explained.
Do we need to fine-tune a model?
Often not. Strong prompting and retrieval solve most problems. We recommend fine-tuning only when evals show it clearly earns its keep.
Can the model run on our own servers?
Yes. We deploy open-weight models privately when data residency, cost or control require it.
Let's build your llm engineering.
We take on a small number of teams at a time. Tell us what you're trying to build — we usually reply within a day.
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