Generative AI applications
Product features powered by models: drafting, summarising, classifying, extracting — inside the workflow, not beside it.
AI engineering
Generative AI, agents and automation built on your data
Generative AI, assistants and agents built on your own data — with retrieval, evaluation and human approval designed in from the first sprint.
Overview
We build AI features on top of your own data, with evaluation and guardrails from day one — assistants, document and knowledge systems, and agents that complete multi-step work with human review where the risk sits. Answers you can trace, cost you can forecast, behaviour you can change.
- Generative AI applications
- AI agents & automation
- Evaluation & guardrails
- AI adoption strategy
Problems we solve
- Knowledge is trapped in documents
- Teams re-answer the same questions because the answer lives in a PDF, a ticket or someone's head.
- Pilots never reach production
- A demo that works on ten examples has no evaluation, no cost ceiling and no owner. It stops at the pilot.
- Nobody can explain the output
- Without retrieval and tracing, an answer cannot be checked — so the business cannot rely on it.
What we build
AI assistants
Grounded question answering over your documents and systems, with citations back to the source.
AI agents & automation
Multi-step work with tool and API calls, run under explicit limits and human approval where the risk sits.
Evaluation & guardrails
Test sets, regression runs, cost ceilings and monitoring, so behaviour changes only when you decide it should.
Typical use cases
- Internal knowledge assistant over policies and documentation
- Document intelligence: extraction, classification and review queues
- Support triage and drafted replies with human sign-off
- Intelligent search across product, CRM and content systems
How we deliver
- 01
Discovery
A scoped session to agree the problem, the constraints and the metric that will move.
- 02
Design & architecture
The smallest structure that solves it, written down before anyone writes code.
- 03
Build
Two-week increments with something reviewable at the end of each one.
- 04
Run & improve
Monitoring, iteration and a handover your own engineers can carry.
Technology we use
- Jira
- Miro
- Notion
- Confluence
- Metabase
- Linear
- Slack
- Loom
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- Do we need our own model?
- Almost never. Most value comes from retrieval, tooling and evaluation around a hosted model. We recommend training only when a measurable gap justifies it.
- How do you control cost?
- We instrument token and call cost per feature from the first week and set ceilings, so spend is a forecast rather than a surprise.
- What about accuracy?
- We build an evaluation set from your real cases before launch and run it on every change, so quality is measured rather than assumed.
Related capabilities
Keep going
Where to go next
- Read the case studiesChallenge, solution and measured outcome from delivered engagements.Continue
- Calculate project costA guided technical discovery that returns a budget range, timeline and team shape.Continue
- Book a strategy callForty-five minutes with the engineers who would actually build it.Continue
AI engineering
Start a ai engineering & strategy conversation
Tell us the problem and the deadline. You will speak to an engineer, and get a considered reply within two working days.
