Answers from your
own documents
A model grounded in your actual content, with citations, so staff stop searching a shared drive for a policy written in 2019.
Typical result: answers with a source attached
Build
AI & LLM Integration
Chat, retrieval and automation built on language models, with the evaluations and guardrails that make them safe to ship. A demo takes a weekend. Something you can put in front of a customer takes rather more, and that difference is the work.
Pilot
3 to 4 weeks
Evals
Always, from day one
Your data
Stays yours
Fallback
Always specified
Model
Swappable by design
How it works
The loop that separates a demo from a product.
Anyone can wire a model to a prompt and get something impressive. What makes it shippable is the cycle around it: a graded evaluation set, a measured pass rate, and a defined behaviour for the cases it fails. Without that loop you have a party trick with a billing account.
What we build
Three things this usually turns out to be.
Answers from your
Work that used
to need reading
Classifying, summarising and routing the things that arrive all day, with a human in the loop where the cost of being wrong is high.
Typical result: a queue that clears itself
A product feature,
not a chatbot
An assistant inside your software that can actually do things, scoped tightly enough that it does not do the wrong ones.
Typical result: a feature, not a novelty
The stack
What we reach for, and why.
Models change every few months, so we build so that swapping one costs a day rather than a rewrite. The evaluation set is the asset, not the prompt.
Models
ClaudeGPTOpen weightsLocal inference
Retrieval
Vector searchHybrid searchRerankingChunking
Quality
Eval suitesGolden setsRegression runsHuman review
Serving
StreamingCachingRate limitsCost tracking
How to buy it
The work is the same. The shape of the deal is not.
AI work is bought under any of the three engagements, though a forward deployed engineer is usually right because the scope moves as you learn.
Forward Deployed EngineerOne senior engineer owns it end to end. Best when the solution is not obvious yet.Read more →
Staff AugmentationYou own the plan, we supply the hands. Best when your spec is ready.Read more →
Dedicated TeamsA standing squad with QA and DevOps. Best for a programme of work.Read more →
Honestly
The 95% of pilots that produce nothing all looked fine at the demo.
Come to us when
Good fit
- You have something working in a notebook and no idea how to make it safe to ship.
- Quality is inconsistent and nobody can say by how much.
- The bill is growing faster than the usage.
- You want somebody who will tell you when the answer is not AI.
Go elsewhere when
Poor fit
- You want original research or a novel architecture. That is a lab.
- You want a chatbot on your website this week and nothing more.
- There is no data yet and no plan to get any.
Questions
Before you book the call.
Almost never, and you should be suspicious of anyone who suggests it for a normal business problem. Fine-tuning has a place. Training a foundation model does not, unless you have a research budget and a reason nobody else can serve.
Yes. Local or self-hosted inference is a normal request and we staff for it. It costs more in engineering time and usually less in tokens, and that trade should be made explicitly.
Then the first two weeks are finding out, and that is a legitimate use of the time. What we will not do is build a feature nobody can name a metric for.
Grounding, constrained output, and a verification pass that checks claims against the source before anything is shown. You cannot eliminate it; you can make the system prefer saying nothing to inventing something, and make the gap visible.
Next step
Tell us the task, not the technology.
Thirty minutes on what you are trying to automate, and an honest answer about whether a model is the right tool for it.