Models put to work where they actually pay
Language models applied to the parts of your operation that scale by hiring — routing, drafting, matching, reconciling — with the evaluation and cost controls that keep them trustworthy in production.
The question is which work, not whether
Almost every business has a queue somewhere that grows in proportion to headcount: support tickets, invoice matching, document review, lead qualification. Those queues are where a model earns its keep, because the alternative is another salary.
Plenty of other things get labelled AI when a database query would be faster, cheaper and correct every time. We'll tell you when that's the case. A studio that puts a model into everything is optimising for looking modern, not for your margin.
Evaluation is the part people skip
A demo proves a model can do something once. Production asks whether it does the right thing on the tenth thousand input, including the strange ones. The difference between those two is an evaluation harness, and it is the single most common thing missing from AI work we're asked to rescue.
We build one alongside the feature: a fixed set of real cases with known-good answers, run on every change, so you can see whether a prompt edit or a model upgrade made things better or quietly worse. Without it you're shipping on vibes.
Cost is a design decision
Token spend is easy to ignore in development and painful in production. We design for it: routing simple cases to smaller models, caching what repeats, and putting a ceiling on what any single request can consume.
You get visibility into what the AI actually costs per transaction, so the unit economics are a number you know rather than a surprise on an invoice.
What's included
- LLM integration
- Models wired into real workflows, not a chat box bolted on.
- Workflow automation
- The steps between systems that a person currently does by hand.
- Analytics & scoring
- Ranking, matching and prioritising against your own data.
- Evaluation harness
- Real cases with known answers, run on every change.
- Cost controls
- Model routing, caching and per-request ceilings.
- Human review paths
- Where a person stays in the loop, and how they intervene.
Where we've done this
- Retail commerce
Inventory and customer service both running on manual work that scaled by hiring.
Read the build - Government
Adaptive control for a signalised corridor still running fixed timing plans set once a year.
Read the build - Aviation
A marketplace for aircraft parts, replacing the WhatsApp groups and spreadsheets MRO buyers were sourcing from.
Read the build
Questions we get asked
Work that is repetitive, judgement-light, and currently done by a person reading something and deciding where it goes. Ticket routing, document extraction, matching records that don't share an identifier. If the task has one correct answer that a database already knows, it isn't an AI problem.
Three things, in order of effect: ground the model in your own data rather than its training, constrain the output to a schema so it can't ramble, and keep a person in the loop wherever a wrong answer is expensive. The evaluation harness then tells you how often it's wrong, which is the number that actually matters.
It depends on volume and which model each step needs, but it's knowable in advance and we model it during design. Most workloads get cheaper after launch, because the first optimisation pass usually finds that the largest model was only needed for a fraction of requests.
Rarely, and later than most people expect. Prompt design, retrieval over your own data, and output constraints solve the majority of cases at a fraction of the cost and none of the lock-in. Fine-tuning makes sense once you have volume and a stable task, not at the start.
The integration is written against an interface, not a specific model name, and the evaluation harness lets you swap the model and immediately see whether quality held. Migration becomes a test run rather than a rewrite — which is most of why the harness is worth building.