We find the most expensive human workflows inside large organisations, rebuild them around AI, and run them until they hold. Then we turn what we learn into software.
The model is rarely the problem. The problem is the ten systems, four committees and two regulators between a working prototype and a workflow that runs at 2 a.m. without anyone watching.
We don't start from a technology and look for a use. We start from a workflow that costs a lot of people a lot of hours, and ask what it looks like redesigned around a machine that can read, reason and act.
Every engagement is built to leave two things behind: a system your team owns, and a piece of reusable software we can deploy again. That is how consulting becomes a product company, and how your second use case costs less than your first.
Read our approach
Enterprise transformation and the boardroom.
From the workflow to the system to the business case, and back.
Production systems, data infrastructure and reliability.
What works in the demo has to work at 2 a.m.
That is where our most senior people spend their time.
Which workflow, and is it worth it?
You keepRanked opportunity map with cost, feasibility and risk; one workflow chosen; a business case the CFO can read
Does it work on our data, and how well?
You keepWorking system on real data; evaluation harness and baseline; go / no-go for production
Can it run inside our controls?
You keepIntegrated, governed, monitored system in the hands of your people; runbooks; audit trail
What do we do next, and cheaper?
You keepYour team owns the system; we retain the reusable parts and bring them to the next workflow
Anywhere people read, extract, check and decide all day.
Where volume is high and quality is measured by sampling.
Work that exists only to catch what other work got wrong.
The invisible workflows that consume the most senior time.
Where a good analyst is scarce and the data already exists.
If it costs a lot of skilled hours and follows a pattern, bring it to us.
Executives who know AI matters and don't yet know where it pays
A business owner with a chosen workflow and the authority to change how it's done
Teams that already build AI and need the enterprise parts
Core banking, payments and settlement, treasury, credit, digital banking, cloud and data platforms, and the committees that govern them.
ExploreNon-life: claims, bancassurance, reinsurance operations, IFRS 17, telesales compliance and fraud analytics, and the governance around them.
ExploreMarket-data engineering and data-platform reliability inside investment firms, and quantitative strategy work.
ExploreAutomotive, aerospace and manufacturing: robotics and automation on the factory floor, process automation across plants and back offices, and digital transformation programmes for carmakers.
ExploreWorking in another industry? Most of the problems above look the same from the inside. Bring us yours
Writing in progress.
Tell us what it is, who does it today, and roughly how many hours it takes. We'll come back within a week with a view on whether AI changes it, and how we'd prove it.