Bkper Agent

Automatically extracts transactions from uploaded documents using AI. Drag invoices, receipts, or bank statements into your book — the agent extracts dates, amounts, and descriptions, then finds the right accounts based on your transaction history.

The agent learns from your corrections and improves extraction accuracy over time.

How it works

Upload — select an account and drop a file into the book. The agent extracts transactions from the document and creates drafts. For bank statements and CSVs, it creates one transaction per line item. For invoices and receipts, it creates a single transaction.

Attach — drop a file onto an existing transaction. The agent extracts data and updates the transaction with the extracted amount, date, description, and custom properties.

Note: Attachments are always treated as invoices or receipts, even if the file is a multi-page statement.

The agent detects the document type automatically:

• CSV files are always treated as statements (one transaction per row).
• PDFs and images are analyzed to decide if they contain a statement table (multiple transactions) or an invoice/receipt (single transaction).

Account discovery

After extracting data, the agent assigns From and To accounts by searching your book's transaction history for similar patterns:

1. Extract key terms — AI identifies meaningful parts of the description (e.g. Office Depot from OFFICE DEPOT #1234 PURCHASE 04/15)
2. Search by account + creator — finds posted transactions with similar terms, scoped to the selected account and user
3. Search by account — same search, any user
4. Search by description — searches across all accounts
5. Pick by frequency — assigns the most commonly used accounts from matching results

When you correct an account and post the transaction, the agent re-runs discovery on all pending drafts in the book — one correction can fix many.

When the agent finds matching transactions to determine the accounts, it also looks at the custom properties on those historical transactions. If every matching transaction shares the same property (for example, a consistent category or project code), the agent copies that property onto the new draft. Properties that have already been auto-copied are tracked per-account so they are not suggested again.

The agent also extracts amounts and dates from transaction descriptions using AI, even without a file upload.

Learning from corrections

When you edit an AI-extracted transaction and post it, the agent compares the posted version against its original extraction. If the data changed significantly, it improves its extraction instructions:

1. Reflect — analyze what the extraction got wrong
2. Curate — generate new instructions and merge with existing ones
3. Validate — re-extract the document with the improved instructions and verify results are better without regressions

The improved instructions are saved to purpose-specific properties on the account or group: agent_prompt_transaction for invoices and receipts, and agent_prompt_transactions for statements. Future documents processed through that account or group benefit from the accumulated learning.

For invoices, this runs immediately on post. For statements, the agent flags the account or group and processes improvements asynchronously.

When an account or group has no stored prompt yet, the first posted transaction through that account can establish a baseline prompt automatically — even if you made no corrections. This gives the agent a starting point for future improvements.

Existing agent_prompt values are still honored as a silent fallback, but new prompt improvements are saved to the purpose-specific properties.

Configuration

agent_prompt_transactions: |-
  Extract transactions from the statement table.
  - Do not include header rows
  - Include the reference number as a property called "ref"

agent_prompt_skip: ["trade_date", "settlement_date"]

Learn more

• Recording transactions — how to record and manage transactions in Bkper