Financial Data Pipeline & AI Retrieval
A Plaid integration that pulls accounts, balances, and transactions into a documented PostgreSQL schema, keeps it current without duplicates, and answers questions about it with SQL and semantic retrieval.
- Client
- Financial services company
- Industry
- Insurance & Financial
- Services
- AI AgentsWeb Development
Financial services teams lose hours to administrative work. This company wanted the groundwork for an assistant that could retrieve customers’ banking data and answer questions about it: not a standalone advisor chatbot, but a foundation one could stand on.
What we built
How the system works, step by step.
Plaid sandbox integration
Items and connected institutions, accounts, balances, and transaction history, built entirely in Plaid’s Sandbox with about 12 months of representative data.
A schema to build on
Plaid’s JSON mapped into a documented PostgreSQL schema that analytics and AI tools can build on, instead of raw JSON in a table.
Initial and incremental sync
Plaid’s transactions sync API imports existing history once, then adds changes as they happen.
Safe to run twice
Duplicate and update handling keeps accounts and transactions accurate when a sync runs again.
Retrieval by question type
A small AI retrieval proof of concept: “How much did I spend in March?” is a SQL query, while “How has my spending changed lately?” combines SQL with semantic search.
Documented
A README and an entity-relationship diagram.
- Plaid
- PostgreSQL
- RAG
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