Process
De-risked by design, stage by stage
AI projects fail through vagueness: undefined success, unbounded scope, quality asserted rather than measured. Our process is the inverse — every stage ends in a named artifact and advances through a gate you control.
Discovery & use-case definition
We separate what you asked for from what you actually need, and put numbers on it before anyone writes code.
You get
- Working sessions with your team
- Data & systems audit
- KPI definition — what 'working' means, measured
- Feasibility read: what AI can and cannot do here
Design & fixed scope
The architecture, the guardrails and the evaluation criteria — agreed before the build, so the pilot has a finish line.
You get
- Agent / conversation architecture
- Guardrail & handoff design
- Evaluation criteria and gold-set plan
- Fixed pilot scope and price
Build & evaluate
Hardest parts first, demos every week, and an eval suite growing alongside the system — quality measured from day one, not asserted at the end.
You get
- Weekly working demos
- Eval suite run on every change
- Your team in the loop throughout
Validate & launch
A formal acceptance gate against the KPIs from step one, then a phased rollout — a go/no-go decision, made on evidence, that you control.
You get
- User acceptance testing
- Go / no-go review against agreed KPIs
- Phased rollout with rollback ready
Operate & grow
Monitoring, spend governance and iteration after launch — because an AI system is a living thing, and the second month's answers matter as much as the demo's.
You get
- Observability & quality monitoring
- Cost caps and spend reports
- Analytics reviews and improvement cycles
Three ways in
Match the engagement to your certainty
The less certain you are, the smaller and cheaper the first step should be. That is how we designed it.
“We know AI could move the needle, but not where to start.”
Free opportunity call
Thirty minutes with a founder. We map your workflows to what AI genuinely does well today, tell you what we'd build first — and tell you plainly if the honest answer is 'nothing yet'.
“We have a use case and need to know if it works.”
Fixed-scope pilot
A working agent on your own data in about 30 days, evaluated against criteria we agree up front — ending in a go/no-go recommendation and a scaling roadmap. Fixed scope, fixed price, no commitment beyond it.
“We've validated it. Now it needs to be real.”
Full build & operate
Production engineering by the founders themselves: guardrails, evaluation gates, observability and cost governance included — then we run it with you, or hand it over clean. You own all of it.
Our commitments
Hold us to these
You own everything
Code, prompts, evals, infrastructure — in your repos and your cloud. No lock-in engineered in.
Founders do the work
No hand-off to juniors after the sales call. The people you meet are the people who architect, build and answer.
We say no early
If AI is the wrong tool for your problem — or we are the wrong team — you hear it in the first call, free.
Honesty over advocacy
Every case study on this site lists what the system does not do. Expect the same candour about yours.
Tell us the pain point. We'll tell you honestly what AI can do about it.
A founder replies within 24 hours. If the answer is 'AI is wrong for this', you'll hear that too — free either way.