Services
Six ways to put AI to work.
Start from the problem, not the technology. Pick a request below and watch the router send it to the service that fits — then read exactly what you get and how we'll measure it.
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- AI agents & automation0.71
- Assistants on your data0.12
- Custom models & fine-tuning0.03
- AI inside your product0.06
- Data & evaluation pipelines0.05
- AI strategy sprint0.03
The six specs
Every service ships with three things: something that works, a way to measure it, and a person accountable for it.
AI agents & automation
An agent is a model that can use your tools. We decide with you what it may do on its own, connect it to the systems your team already works in, and design the moment a person takes over.
- What you get
- An agent wired into your helpdesk, CRM, email or internal APIs
- Guardrails: what it does alone, and what waits for approval
- A handoff to a person, with a written summary of the case
- How we measure it
- Resolution rate, error rate and cost per task on a fixed set of real cases — checked before and after every change.
- A good first step
- Pick one queue. We map it, then build the agent against last month's real tickets.
Assistants on your data
An assistant that answers from your own documents, databases and wikis, and shows where every answer came from — so people can trust it, and check it.
- What you get
- Chat and search over your sources, with citations
- Access that respects who is allowed to see what
- Ingestion that keeps up when documents change
- How we measure it
- Answer accuracy and citation correctness on questions your staff actually ask — and how often it says it doesn't know when it shouldn't guess.
- A good first step
- Collect fifty real questions and where their answers live. That becomes the test set.
Custom models & fine-tuning
Sometimes a general model isn't accurate enough, fast enough or cheap enough. Then we train, fine-tune or distil one for your task, and prove it beats the off-the-shelf option.
- What you get
- A dataset built and cleaned for your task
- A tuned or distilled model that you own
- A side-by-side comparison with the best general model
- How we measure it
- Quality, latency and cost per request against the off-the-shelf baseline, on data the model never saw in training.
- A good first step
- Show us where the general model fails. We measure the gap before we train anything.
AI inside your product
AI features inside the web and mobile apps you already have — or a new AI-first product, built end to end, from interface to infrastructure.
- What you get
- AI features designed around how your users actually work
- Web and mobile integration, streaming interfaces, graceful fallbacks
- Monitoring for quality and cost once it's live
- How we measure it
- Task success for your users, response time and cost per active user — tracked in production, not just in the demo.
- A good first step
- Name the feature your users ask for most. We prototype it inside your app.
Data & evaluation pipelines
The part most teams skip. Datasets, test suites and evaluations that tell you, with numbers, whether your AI is actually getting better.
- What you get
- An evaluation set drawn from real usage and labelled with care
- An automated test suite that runs on every change
- A readable report: what improved, what regressed, and why
- How we measure it
- We measure the measurement: how often the evals agree with human reviewers on a sample.
- A good first step
- Send us a week of real inputs and outputs. We turn them into your first test suite.
AI strategy sprint
Two weeks to find where AI pays off in your business. You finish with a ranked list of opportunities and a working prototype of the best one — not a slide deck.
- What you get
- Interviews, and a map of the workflows that cost the most time
- A ranked shortlist with effort, cost and risk for each
- A working prototype of the top pick, on your data
- How we measure it
- Every opportunity is scored on value, feasibility and risk; the prototype is tested against a bar we agree on day one.
- A good first step
- A one-hour call to choose the teams and workflows in scope.
Three ways to work together
Most teams start small and grow into the next shape once the first one has proved itself.
- Two weeksSprint
Find where AI pays off. Ends with a working prototype.
- A scoped projectBuild
From prototype to production: integration, security, evaluations and monitoring.
- OngoingRun
We watch it in production and keep improving it, measured against the same bar.
Not sure which one you need?
Tell us the workflow. We'll tell you which of these fits — or whether AI is the wrong tool for it.