AI Foundation for a UK RegTech Startup
AI foundation, cloud infrastructure, and data-quality framework delivered; enterprise assurance proposition supported with verifiable accuracy claims.
Theme Risk, Compliance & Governance · Also Scaling
In brief
Situation. A UK RegTech startup operating in the process-mining space was building an enterprise product whose commercial proposition rested on a single word: assurance. Enterprise clients would only buy if they trusted the analytics. They would only trust the analytics if they trusted the data underneath.
Complication. Startup pace and enterprise-grade rigour pull in opposite directions. Move fast and you cut corners; move carefully and you do not ship. The founders needed both.
Resolution. Leading a team of three across eight months, I designed and implemented cloud infrastructure (multi-vendor), established the AI integration pattern for the process-mining engine, and built the data-quality framework that guaranteed the level of accuracy needed for the startup to make assurance claims to its enterprise clients. Data lineage, validation, and accuracy controls were architectural, not bolted on. The engagement was paced for short iterations and weekly founder check-ins.
Impact. AI foundation delivered. Cloud infrastructure operational. Data-quality and assurance framework now in production, supporting the startup’s enterprise client conversations with verifiable accuracy claims.
The longer story
Selling RegTech to an enterprise is a strange theatre. The buyer is not buying software. The buyer is buying a story they can tell their auditor. If that story has a single weak link, a single “and how do we know the data is right?” that ends in a shrug, the deal dies, often quietly.
The job, then, is not to build a piece of process-mining software. The job is to build a piece of process-mining software AND simultaneously build the paper trail that lets a Chief Risk Officer sleep at night. We baked the assurance layer into the architecture from week one because retrofitting it later would have been like trying to add foundations to a finished house.
The lesson generalises. Every startup selling to large institutions eventually discovers that 60% of their product is the actual product, and 40% is the artefacts that let the buyer prove to a third party that they did the right thing in buying it. Treat the assurance layer as a feature, not as overhead. It is the only feature the auditor reads.