INVOICE PROCESSING

Silent Price-Creep Detector from Gmail Invoices

Watches an inbox for incoming vendor invoices, extracts each line item's unit rate, compares it to the last invoice from that vendor.

CategoryInvoice Processing
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew invoice email arrives in GmailGmailGmail
  • ActionExtract vendor and per-SKU unit rates with OpenAIOpenAI
  • ActionLook up last stored rate per SKUPostgreSQLPostgres
  • LogicCompute rate delta and check for prior notice
  • ActionUpdate baseline rate in ledgerPostgreSQLPostgres
  • OutputPost flagged price creep to SlackSlack

What it does

This workflow catches vendors who quietly bump unit prices on recurring invoices. Each time a new invoice lands in Gmail, it parses the line items, looks up the prior unit rate per SKU from your ledger, and flags any increase that arrived with no price-change notice. Confirmed creep is posted to a Slack review channel with the old rate, new rate, and percentage delta.

When to use it

Run this when you pay the same vendors on a recurring basis (SaaS seats, raw materials, logistics) and want to stop rubber-stamping invoices that crept up 3-8% without anyone noticing. Best for finance and procurement teams processing invoices by email.

How it works

  1. 1A new email matching your invoice label arrives in Gmail and triggers the run.
  2. 2An OpenAI extraction step pulls vendor, invoice date, and per-SKU unit rates from the attachment or body.
  3. 3The flow queries Postgres for the most recent stored rate per SKU for that vendor.
  4. 4A logic step computes the delta and checks whether a notice email exists; small or pre-announced changes are filtered out.
  5. 5Confirmed silent increases are written back to Postgres as the new baseline.
  6. 6A Slack message delivers the flagged creep to the procurement channel for sign-off.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GmailRead, draft, send, label.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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