Banking that runs on
AI-native engineering.

Most banking consultancies still write the delivery plan first and add AI as a workstream later. That’s the wrong way round. AI agents sit inside our engineering pipeline from the start: drafting infrastructure-as-code for the regulated Cloud landing zone, generating tests for the core decomposition, forecasting cost on the data platform before the bill arrives. The bank gets the engineering rigour the PRA expects, on a timeline AI makes possible.

Yes. The reporting stack stays. We layer templated pipelines on top – pre-built report types, automated data quality checks, an audit-defensible lineage trail. Our Financial Services Regulatory Reporting accelerator gets the first report into production in 4 to 6 weeks. Against your existing systems. No greenfield ledger nobody asked for.

That’s the only way it works at scale. Strangler pattern. New event-streamed services run alongside the legacy core; customer journeys move across one product line at a time; the old core retires in stages once traffic has shifted. AI-assisted code translation compresses discovery. The cut-over discipline protects the live service – and that’s the part most modernisation programmes get wrong.

Model governance is part of the architecture, never an annex bolted on before a supervisor visit. Model cards, decision logs, and feature attributions sit inside the inference path. When the regulator asks why a transaction was flagged, the answer is in the system. Explainability is a runtime contract on every production model we ship – including the ones using third-party LLMs.

All three. PRA, FCA, and BCBS standards apply across the board; the workload pressures look very different in each. A retail bank’s pain isn’t a wholesale prime broker’s pain. Senior practitioners on each engagement bring the segment-specific experience. We won’t reuse a retail-banking playbook in a markets engagement and call it sector knowledge.