Data infrastructure for the AI age in financial services
Financial institutions are deploying AI faster than the data infrastructure beneath it can support. This paper makes the architectural case that the dominant failure mode of financial AI is not the model - it is the fragmented data estate underneath it. It sets out the three architectural commitments a trustworthy financial AI platform requires, and how a unified, transactional, governed foundation satisfies SR 11-7, the EU AI Act, BCBS 239, MiFID II, and DORA structurally rather than procedurally.
Key takeaways
Why only 2 of 31 global systemically important banks are fully BCBS 239 compliant after twelve years - and why the root cause is architectural, not procedural.
The three non-negotiable commitments a financial AI platform requires: a unified multi-model substrate, structured agent memory with provenance, and governance by design.
How bi-temporal data, record-level access control, and write-back audit trails satisfy SR 11-7, the EU AI Act, BCBS 239, MiFID II, and DORA structurally rather than procedurally.
Five questions every institution should answer before choosing between open infrastructure and closed AI platforms.
A reference architecture mapping each layer - from object storage to reasoning model - to the financial AI capability it enables.
What's inside
- A note from the CEO
- Why financial AI keeps failing at the foundation
- Why financial data is structurally different
- What the architecture of trust requires
- Agent Memory: Agent Memory built into the database
- SurrealDB v3.1: the current platform
- Open infrastructure vs. closed platforms: five questions
- The reference architecture
- Four principles for the AI age in financial services
- Key regulatory frameworks referenced
