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LLM & Generative AI Development
Getting a demo to work with a large language model takes an afternoon. Making it reliable, affordable and safe enough for real users takes engineering. We build LLM applications with the evaluation, guardrails and cost controls that turn a clever prototype into a product people can depend on.
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What we deliver
- Task-specific assistants, summarisation, extraction and classification pipelines
- Prompt and context design, with versioning and automated regression tests
- Fine-tuning and distillation when prompting alone is not enough or too costly
- Evaluation harnesses that score quality on your own data, not only public benchmarks
- Guardrails for safety, privacy and brand tone, plus fallbacks when a model fails
- Cost and latency optimisation: caching, routing between models, batching
Where it's used
How we work
- Discover. We look at your data, define success metrics and agree what "good" means before building anything.
- Build. We start with a baseline, iterate with measured evaluation, and keep you in the loop with working demos.
- Ship & monitor. We deploy with tests and rollback plans, then monitor quality, cost and drift.
Tools we use
AnthropicOpenAIGeminiLlamaMistralHugging FacevLLMLangChain
Questions
Should we use a hosted model or run our own?
It depends on privacy, volume and quality needs. Hosted models are the fastest start; open-weight models you host yourself give more control and can be cheaper at scale. We compare both on your data in discovery.
How do you stop the model from making things up?
We ground answers in your data where possible, constrain outputs, add automated checks and measure how often the system is wrong. We can't promise zero errors, so we design the workflow so mistakes are caught or cheap.