LLM features in your product
Chat, summarisation, drafting, classification — added to your existing product without breaking what already works.
We help teams build LLM features, RAG apps, and data pipelines that hold up under real use — not just demos.
We focus on the boring-but-important parts: making sure the answers are right, the cost is sensible, and the thing keeps working when it's been live for a month.
Chat, summarisation, drafting, classification — added to your existing product without breaking what already works.
Answering questions over your own data, with the citations, chunking, and guardrails needed to keep the answers honest.
Getting your data from wherever it lives into a place where it's useful — and then keeping it clean as the schema drifts.
Tools for your own team — analyst copilots, draft generators, classifiers — that save real time on real work.
Eval harnesses, regression tests, and safety checks so you actually know whether a prompt change made things better or worse.
Pulling structured data out of unstructured input — emails, PDFs, transcripts — with measurable accuracy you can defend.
We start small. Most AI projects fail not because the model is wrong, but because nobody checked the answers against real use.
What's the job to be done, who's the user, and what does "good enough" look like? We get specific before writing code.
A working prototype with real data, so you can see how it performs before committing to a bigger build.
Evaluation, cost controls, fallbacks, and monitoring — the parts that decide whether it survives in production.
We document the prompts, the evals, and the trade-offs — so your team can iterate without us.
We pick tools we trust to be maintainable for the long haul. If you already have a stack, we'll usually meet you there.