Use Cases
Work that moves the needle.
Selected engagements. Client names are withheld by default — we're happy to discuss specifics on a call.
Building an ML engineering function from the ground up
Retrofit data company
Context
A fast-growing retrofit data company had secured investor backing to deliver a machine learning product that would help institutional investors make smarter, data-driven decisions in the retrofit and sustainable infrastructure space. The ambition was clear. The path to execution wasn't. The CTO had a strong engineering instinct but no prior experience building ML systems at pace or under investor scrutiny. The immediate questions were: what does a credible ML engineering function actually look like at this stage, and who do you hire first?
What we did
Veraiq worked directly with the CTO to develop a lean ML engineering strategy fit for an early-stage data company — one that could demonstrate rigour to investors without over-engineering for a product still finding its shape. This meant being explicit about what ML capabilities were genuinely needed now versus what could wait, and where a wrong hire early would be expensive to unwind. The engagement produced a clear hiring framework: the specific profiles to prioritise, the experience signals that actually matter at this stage versus credentials that look good on paper, the interview approach to stress-test ML thinking in candidates, and the team shape that would get them to their first production model without creating technical debt in their organisational structure.
The CTO left with a strategy they could use and a hiring process they could run with confidence.
When the founder is the client
Kite Pay
Context
The best way to pressure-test a methodology is to use it on something you can't afford to get wrong. Kite Pay is a payment initiation service built on open banking infrastructure, replacing traditional card machines with QR-code-based payments that settle directly between bank accounts. It is also a company we founded. When it came time to design the architecture for the MVP, we applied the same process we bring to every client engagement — because the constraints were just as real and the stakes were higher.
What we did
The brief had a genuine tension at its centre: the system needed to meet the security bar expected of a regulated financial product, while remaining simple enough that a small engineering team could ship, iterate, and support it without institutional overhead. Over-engineer it and you slow down a startup that needs to move. Under-engineer it and you build a security or compliance problem into the foundation that compounds over time. Veraiq designed and implemented the end-to-end data and systems architecture for the MVP — from how payment events are captured and stored, to how open banking API interactions are structured, to how the system handles failure states without data loss or compliance exposure. Every component earned its place. Nothing was built speculatively.
Kite Pay launches to its first merchants summer 2026. We know the architecture holds up because we built it to the same standard we would demand for any client in regulated financial services — and we had no one to blame but ourselves if it didn't.
See yourself in one of these? Let's talk.
Fixed scope. Fixed price. Senior delivery only. Start with a two to four-week Discovery Sprint — no long-term commitment required.