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We integrate large language models, build retrieval pipelines over your own documents, and automate the workflows that currently consume your team. Every engagement starts by defining what success is measured against.
These are the situations clients are usually in when they come to us for this work.
Staff spend hours on work that is pattern-matching rather than judgement
Answers are buried in documents nobody can search effectively
An AI pilot demoed well but never made it into production
You need AI features but cannot send data to a third-party model
Every engagement runs in these stages, with a decision point at the end of each — you are never locked into the whole thing up front.
Pick the workflow where AI has measurable value and define what success is, so the result can be judged rather than admired.
Prepare and index your content so answers are grounded in your own material instead of the model guessing.
Implement with an evaluation set, so accuracy is measured against real examples before anyone relies on it.
Production rollout with cost controls, human-in-the-loop review, and monitoring for quality drift.
The things people usually want to know about this service, answered straight.
No. We use enterprise API tiers where your data is excluded from training, and we can deploy self-hosted open models when data cannot leave your environment.
Retrieval grounding, citations back to source documents, and an evaluation set that measures accuracy before launch and monitors it after.
Token costs are modelled during design and controlled with caching and model routing. You get a projected monthly figure before the build starts.
Tell us what you are trying to build. We will come back with an honest view of scope, timeline, and cost — no obligation.