Start from the outcome
Every engagement opens with a written statement of what will be different when it’s done, and how we’ll know. If we can’t write that sentence, we don’t start.
Our approach
Opinionated about method. Flexible about technology.
The tooling changes every few months. How you scope, evaluate, govern and hand over work is what decides whether an AI project survives contact with reality.
Principles
Six rules we don’t bend
Evaluate before you scale
Nothing goes wide without a scored test set. “It seemed good in the demo” is how projects quietly fail six months later in front of customers.
Governance is part of the build
Access, retention, audit trails and human oversight are designed in from the first sprint. Retrofitting them is always more expensive than including them.
Humans stay in the loop where it matters
We’re explicit about which decisions a model may make alone and which need a person. That line is a design decision, made with you, and written down.
No lock-in, ever
Your repositories, your cloud accounts, your data, your IP. If you want to take everything in-house tomorrow, nothing about our setup makes that painful.
Optimise for the smallest thing that works
Cheaper models, tighter scope, fewer moving parts. Complexity is added when the evidence demands it, not because it’s interesting to build.
Delivery model
Two-week increments, with an off-ramp at every one
You should never be more than a fortnight from a decision point. Each increment ends with something demonstrable, a written update, and an honest read on whether continuing is still the right call.
- Working sessions, not status meetings. Your subject-matter experts are in the room while we build, because they know what “correct” looks like.
- Your infrastructure by default. We build in your cloud tenancy and repositories from day one, so production isn’t a migration project.
- Fixed scope per increment. Changes are welcome — they go into the next increment, with the cost stated, rather than quietly absorbed.
- Kill criteria agreed up front. We define what result would make us stop before we start. It’s much harder to be honest about that afterwards.
Technology posture
Model-agnostic, and deliberately boring where it counts
We choose based on your data sensitivity, latency requirements, cost per transaction and where the workload is allowed to sit. Not on what’s trending.
Where the model runs
Hosted frontier models, models deployed inside your own cloud tenancy, or open-weight models where isolation matters more than raw capability. Data residency is part of the selection, not an afterthought.
What surrounds it
Retrieval, structured outputs, tool use, guardrails, caching and fallbacks. Most of the quality in a production AI system lives in this layer, not in the model choice.
How we know it works
Golden test sets built from your real cases, automated scoring in CI, latency and cost budgets per request, and dashboards your team can read without us.
Handover
We’re trying to make ourselves unnecessary
A consultancy that leaves you dependent has done a bad job and charged you for it. Every engagement ends with a deliberate transfer, not an invoice and a goodbye.
- Architecture and decision records — including the options we rejected and why
- Runbooks for the things that will go wrong at 2am
- Live working sessions with the people inheriting the system
- An optional light-touch retainer for evaluation reviews and model updates — offered, never assumed
If that sounds like how you’d want it done.
Book a free 30-minute consultation and we’ll apply the method to your actual problem before you’ve spent anything.