INVOICE PROCESSING

Flag recurring-vendor invoices that exceed their historical baseline

When a new vendor invoice lands in Airtable, compares its amount to that vendor's trailing-12-month average in BigQuery and routes any statistically high invoice to a Slack…

CategoryInvoice Processing
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew invoice row created in AirtableAirtableAirtable
  • ActionFetch vendor 12-month baseline from BigQueryGoogle BigQueryBigQuery
  • LogicCompute z-score; pass if within 2 sigma
  • ActionWrite deviation summary back to AirtableAirtableAirtable
  • OutputPost flagged invoice to AP Slack channelSlack

What it does

Every incoming invoice from a recurring vendor is scored against that vendor's own spending history before it enters the approval queue. Invoices that fall within the normal band pass straight through; invoices that spike are held and surfaced to a human with the exact deviation spelled out.

When to use it

Use this when you pay the same vendors on a regular cadence (rent, SaaS, utilities, retainers) and want to catch a $4,200 bill that should have been $420 before someone clicks approve. Ideal for AP teams that trust the vendor but not the keystrokes.

How it works

  1. 1A new invoice row is created in Airtable (vendor, amount, period).
  2. 2The flow queries BigQuery for that vendor's trailing-12-month mean and standard deviation.
  3. 3A logic step computes the z-score; invoices within 2 sigma are marked Auto-OK and skip ahead.
  4. 4Outliers get an enriched note (expected vs. actual, percent over) written back to Airtable.
  5. 5A Slack message posts the flagged invoice to the AP approvals channel with approve/hold context.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AirtableBases, tables, views, automations.
  2. 2
    Connect BigQueryDatasets, queries, schemas.
  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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