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.

Customize description matching

Use agent_discovery_examples to show the agent which recurring parts of a transaction description are useful for finding similar posted transactions. It improves the search terms; it does not assign an account directly.

Add one example per line as full description -> [stable part]:

agent_discovery_examples: |-
  WF HOME MTG DES:AUTO PAY ID:XXXXX37704 CO ID:XXXXX87461 WEB -> WF HOME MTG DES
  AMAZON RETA* NJ1JM7QA0 #goodies -> AMAZON RETA
  1* Product Alpha * 2* Product Beta -> [Product Alpha, Product Beta]

• On the left, use a real description from your transactions.
• On the right, keep recurring names or account identifiers; omit changing dates, amounts, quantities, and reference IDs.
• Use brackets with comma-separated values when more than one part is useful.

Set the property on an account for account-specific formats, an account group for shared formats, or the book as a general fallback. The agent uses the first non-empty value in account → first matching group → book order; values are not combined. When no account is known yet, only the book value applies. Custom examples replace the built-in examples.

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. It only fills missing properties and never overwrites properties already present on the draft. You can prevent selected property keys from being copied with agent_excluded_properties; this setting is read-only and is never populated automatically.

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

AI allowance behavior

Document extraction and learning use Gemini 3.6 Flash with medium reasoning through the Bkper AI allowance associated with the agent's installation principal. Lightweight classification, field utilities, and embeddings remain separate direct operations. For Business and Professional subscriptions, the Bkper AI allowance may be shared across the domain.

When the allowance is exhausted:

• document extraction fails without creating partial draft transactions;
• the agent does not bypass the limit through another model provider;
• invoice learning is skipped;
• pending statement learning remains flagged for a later run.

The Bkper AI usage dashboard shows the current allowance and usage attributed to the Bkper Agent.

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_parser_transaction for invoices and receipts, and agent_parser_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.

Legacy extraction instruction properties (agent_prompt, agent_prompt_transaction, agent_prompt_transactions, and agent_prompt_file) are migrated automatically when the agent next uses the account or group. Their values are copied to the corresponding agent_parser_* properties and the legacy properties are removed.

Configuration

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

agent_excluded_properties: "trade_date, settlement_date"

Learn more

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