I built an AI bookkeeping agent that reached the AWS semifinals from 10,000+ entries

Table Of Contents The architecture The categorisation engine Few-shot learning that actually improves over time Handling real-world bank statements Batched processing with concurrency control Double-entry done right What I learned The numbers Every month, I sit down with bank statements from multiple clients and manually assign each transaction to the correct nominal code — a process called transaction categorisation. It takes hours. There are 166 standard UK nominal codes, five VAT rate categories, and endless edge cases. "AMAZON MARKETPLACE" could be office supplies, stock purchases, or a personal expense depending on the client. Multiply that across hundreds of transactions per client, per month, and you start to understand why 75% of CPAs are expected to retire in the next decade with fewer graduates replacing them. So I built LedgerAgent - an AI-powered bookkeeping agent that categorises bank transactions automatically using Amazon Bedrock. It reached the semifinals of the AWS 10,