Programme-level, evidence-first
Stop funding pilots and pick the two that should be systems
You have an AI mandate, a budget and a stack of pilots that never reached production. We help you decide which two deserve to become real systems, and then our engineers build them with your team.
We go through every pilot in flight, measure each against what the process costs today, and stop the ones that were never going to ship. The people who scope the programme deliver the first system in it, with the governance underneath that risk and audit will ask about.
- Who it is for
- Executives with a mandate, a budget, and fifteen pilots
- Bench
- 115+ engineers, averaging 15 years each
Most transformation programmes fund fifteen pilots and ship none, and it's rarely the pilots' fault. Nobody scoped which of them would still matter at production volume, under real permissions, with somebody accountable for the output.
What we work through
- Every pilot in flight, and what it would take for each to reach production
- Which processes would genuinely change, measured against what they cost today
- The permission and data model, because it decides what is buildable at all
- Where accountability lands once a system rather than a person makes the call
- What the programme costs to run, not just to build
What you get
- A ranked shortlist: what to build, what to stop, and the reasoning for both
- A sequence where each phase pays for the next rather than deferring value to the end
- The governance model underneath, so the systems can be defended to risk and audit
- Delivery of the first one, by the people who scoped it
Work we have published on this
Automotive finance · Our own product
AutoFabric
Origination and refinance steps that slow a deal down, such as credit pulls, VIN decode, deal structuring, stipulations and payoff quotes, run by agents in the lender's own environment. It came out of years of origination and refinance work for lenders and dealer groups. It connects to DealerTrack, RouteOne and CDK, and every action is checked against policy and recorded for the examiner.
Automotive / Fintech
Auto Approve
Auto Approve's loan agents were copying every refinance request by hand between the CRM, lender sites and Excel, a process that took two to three days. Our senior engineers worked hand in glove with Auto Approve's own IT staff to build the approval engine that automates it, and the company doubled the loans it processes each month without hiring more agents.
FAQ
AI transformation: common questions
What does AI transformation actually involve?
Deciding which work should be done by a system, building those systems, and putting governance under them. The deciding part is where most programmes go wrong, because it gets done as a strategy exercise rather than against what is technically buildable in the platform and permissions you have.
How is this different from a management consultancy?
We build. The same people who scope the programme deliver the first system in it, which changes what gets recommended, because nobody proposes a phase they will personally have to explain in month six. If you want a strategy document and no delivery, we are an odd choice.
We already have a strategy. Can you just build?
Yes, and that is a good way to start. Bring the strategy, we will scope the first system against it, and if we think the sequence is wrong we will say so once and then build what you decided.
How do you measure whether it worked?
Against the number the process was already judged by, agreed before the build. Cycle time, cost per case, exception rate, whatever the business already tracks. A new metric invented alongside the system is how a programme reports success nobody feels.
The rest of our AI work
AI Readiness Assessment
Leaders with AI on the roadmap and no clear view of what is in the way
AI Tools Assessment
Finance and platform leaders holding a renewal they cannot justify
Agentic AI development
Engineering leaders whose agent pilot cannot get past review
AI assistants and enterprise search
IT and data leaders asked for a company assistant that can't leak
AI workflow automation
Operations and engineering leaders with a process that half-completes
AI enablement
Engineering leaders whose team needs to own this, not outsource it