How Revolut runs AI at scale

Nikolay Donets, Head of Machine Learning Engineering at Revolut, on what it takes to run AI across more than 70 million customers, 200+ products, and 40+ countries - and why the hard part is no longer the model but the control plane around it: one gateway, a use-case-based governance layer, fallback chains, cost controls, and mandatory human oversight. Recorded at RAAIS 2026.

Chapters:

0:00 Intro - Revolut's AI at scale

1:24 The problem: classical ML and three libraries

2:54 The 2022 shift to API-served models

4:25 Four internal groups, four sets of needs

9:39 The decision: govern the use case, not the model

10:54 One central gateway vs. distributed libraries

14:10 Performance monitoring and drift detection

17:33 Lesson: fallback chains and the silently-dead model

20:02 Lesson: frontier vs. non-frontier cost (up to 8x)

20:48 Lesson: the platform is the org chart

22:59 Case study: from Rita to AIR

26:40 Voice support at scale

28:21 AIR, the in-app assistant

30:25 Q&A: human oversight, hallucinations, AI as judge