Consulting
Most AI programmes die between the strategy deck and production. We work the whole distance — finding the opportunity, proving it's real on your data, and engineering the system that runs it.
Service lines
Most engagements start in one and end up touching two. The boundaries matter less than knowing which problem you're actually solving.
Find the AI work that's actually worth doing.
Most AI initiatives fail at selection, not execution. We map your workflows, find where a model genuinely beats the current process, and rank the options by value against effort — including the ones we'd advise you not to build.
Take the repetitive work off your team.
The highest-return AI work is rarely a chatbot. It's the operations nobody wants to do: triage, extraction, routing, summarising, reconciling. We design and build automations that run reliably in production and fail loudly when they should.
From idea to a product people can actually use.
We design and build AI-powered products end to end — the model work, the application around it, the data layer underneath and the interface that makes it feel obvious. The same way we build our own.
Make the software you already have significantly smarter.
You don't need a rewrite to get value from AI. We work inside existing applications — adding intelligence to the flows where it changes the experience, without destabilising what already works.
How we're different
The incentive in consulting is to find AI work everywhere, because AI work is what gets billed. That incentive produces a lot of expensive chatbots nobody uses.
Our discovery process is designed to kill bad ideas early. We measure your current process before proposing to replace it, we test the risky assumption on your real data in week one, and we say out loud when a deterministic rule would beat a model.
A discovery sprint that ends in “don't build this, fix the process instead” is a successful engagement. It cost you two weeks rather than two quarters.
Engagement models
Sized to the decision in front of you rather than to a retainer.
$ marks relative investment, not a fixed number — real pricing is scoped on the call.
A fixed-scope investigation. We map the workflow, test technical feasibility on your real data, and deliver a ranked set of opportunities with effort estimates and a recommended first build.
Outcome
A decision you can act on, or a clear reason not to.
Design and engineering through to a deployed system — architecture, models, evaluation, application, infrastructure. Weekly working software rather than monthly status decks.
Outcome
A production system, its evaluation harness and its documentation.
For teams shipping AI continuously. We work alongside your engineers on architecture, reviews and the hard parts, and hand over capability rather than dependency.
Outcome
Your team ships AI without us in the room.
Method
The same six stages whether the engagement is two weeks or six months. The early stages exist to make sure the later ones are worth paying for.
Understand the business before the technology. What the work costs today, where it breaks, who it frustrates, and what a good outcome would actually look like in numbers.
Who we work with
You have an idea or a prototype and need it to become a real product without hiring a team first.
There's expensive manual work in your operation and you suspect some of it shouldn't be manual any more.
You need an AI feature designed properly — with evaluation and guardrails — rather than bolted on and hoped for.
You want a second opinion on an AI architecture before committing engineering quarters to it.
You need something demonstrable and technically honest, not another slide deck about transformation.
You need a specialist alongside your engineers for the hard parts, and capability left behind afterwards.
Start here
Describe the problem and we'll tell you what the right first step is — including if it isn't us.