INVOICE PROCESSING

AI agent that investigates suspected duplicate invoices

When a new invoice can't be cleared by exact-match rules, an AI agent reviews paid history for near-duplicates (split bills, renamed vendors, rounding) and recommends pay, hold…

CategoryInvoice Processing
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerInvoice failed exact-match check
  • ActionPull vendor's paid invoices from PostgresPostgreSQLPostgres
  • ActionAgent reasons over near-duplicate signalsOpenAI
  • LogicMap confidence to pay / hold / reject
  • OutputSend recommendation and rationale to SlackSlack

What it does

Handles the gray-area cases exact matching misses. When a new invoice arrives without a clean match, an agent reads the invoice and the vendor's prior paid invoices from Postgres, reasons about fuzzy signals (same work split across two invoices, a vendor renamed mid-relationship, totals off by tax or rounding), and produces a recommendation with a written rationale for a human to act on.

When to use it

Use this when your duplicates are not byte-identical and rules alone produce too many false negatives or noisy false positives. The agent adds judgment where a deterministic check stalls.

How it works

  1. 1An invoice that failed exact-match checks triggers the agent.
  2. 2The agent pulls the vendor's recent paid invoices from Postgres.
  3. 3It compares line items, amounts, dates, and vendor aliases for likely overlap.
  4. 4A logic step maps its confidence to pay, hold, or reject.
  5. 5The agent drafts a rationale explaining the suspected duplicate.
  6. 6The recommendation and reasoning are delivered to the AP reviewer in Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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