A fixed-scope assessment that maps your data, workflows, and real opportunities, then tells you honestly what's worth building and what would waste your budget.
The expensive mistake is rarely bad engineering. It's committing a budget to a system that never should have been built, and only finding out six months in.
Around 80% of AI pilots never reach production. Some fail on engineering, but a large share fail earlier, on a decision: the wrong use case, data that was never usable, an ROI that never existed. The assessment exists to surface those answers in a planning meeting instead of a post-mortem.
It's deliberately fixed and fast. We map where your data actually lives, where AI removes real friction, and what it would genuinely cost to build. You walk away with a clear, prioritised picture, and a recommendation you can take straight to a budget decision, including the honest cases where the answer is "don't build this yet."
Concrete outputs, not a vague strategy conversation.
Where your data actually lives, who owns it, and whether it's usable for what you have in mind.
Where AI removes real friction in how your team works, and where it quietly wouldn't earn its cost.
Every opportunity ranked, so you attack the highest-value, lowest-risk work first, not the loudest idea.
A concrete technical direction for whatever you decide to build, sized to your real stack.
An honest recommendation for each opportunity, including what to skip and why.
What the first real build actually takes, in time and money, before you commit to it.
Interviews and a hands-on review of your data, systems, and commercial goals.
We weigh each opportunity against feasibility, risk, and real return.
A short, readable deliverable you can take straight to a budget meeting.
You're considering an AI investment but aren't sure where to start. You have data but don't know if it's usable. You want a clear plan, and a defensible number, before you commit budget to a build.
You already know exactly what to build and it's validated, in which case an Implementation Sprint ships it. Or you have a stalled prototype that needs saving, which is Production Rescue.
No. Messy, scattered data is the normal starting point, and telling you exactly how much work it needs is part of the assessment. You don't have to tidy anything up first.
Then we tell you, and you've saved a much larger spend. Part of the value is defining what not to build. We'd rather lose a sprint than send you toward something that fails in production.
Typically two to three weeks, depending on how many systems and stakeholders are involved. It's deliberately fast, the point is a confident decision, not a six-month study.
A short, readable document: the readiness picture, a prioritised opportunity-and-risk map, an architecture recommendation, and a clear build / don't-build call you can take straight to a budget meeting.
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