Why Regulated AI Apps Need More Than a Vector Database

Developers at Apex Grid, building a regtech tool for Nigerian microfinance banks, have outlined a key architectural distinction between vector databases and data substrates in AI systems. A vector database excels at fast, embedding-based similarity search, making it useful for the retrieval phase of RAG pipelines, while a data substrate provides a versioned, traceable, and auditable layer linking AI decisions to specific data sources. In financial compliance contexts, the ability to trace why an AI flagged a transaction — including the regulation referenced and the data version used — is a regulatory requirement that vector databases alone cannot meet. The team uses a hybrid approach, leveraging vector databases for initial retrieval and a data substrate for governance and auditability. While this adds storage and schema complexity, they argue the tradeoff is justified given the high cost of untraceable AI decisions in Nigeria's regulated microfinance sector.