INVOICE PROCESSING
Outlook vendor invoices with PO line-item matching and exception queue
Watches a shared Outlook inbox for vendor invoice attachments, extracts line items, matches them against purchase orders in BigQuery.
How it runs
The automated pipeline, trigger to output.
- TriggerNew Outlook email with attachment in AP inboxOutlook
- LogicFilter to messages carrying a PDF or image invoice
- ActionExtract invoice header and line items with OpenAIOpenAI
- ActionLook up the referenced PO and its lines in BigQueryBigQuery
- LogicCompare each line within price and qty tolerance
- ActionWrite reconciled matches back to BigQueryBigQuery
- OutputPost mismatched lines to Slack exception queueSlack
What it does
Monitors a dedicated Outlook mailbox (e.g. invoices@) for inbound vendor invoices, pulls the PDF, extracts header and line-item fields, and reconciles each line against the matching purchase order. Clean three-way matches are logged as approved; anything off-tolerance lands in a Slack exception queue with the specific lines flagged.
When to use it
Use it when your AP team manually opens every emailed invoice and eyeballs it against POs in a warehouse. It removes the eyeball step for the 80% that match cleanly and concentrates human attention on real discrepancies.
How it works
- 1A new email with an attachment arrives in the monitored Outlook inbox.
- 2The flow filters to messages that actually carry a PDF or image invoice.
- 3OpenAI extracts structured fields: vendor, invoice number, PO number, and each line's SKU, qty, and unit price.
- 4BigQuery is queried for the referenced PO and its line items.
- 5Logic compares line by line within a price and quantity tolerance.
- 6Matches are written back to BigQuery as reconciled; mismatches post to a Slack exception channel naming the offending lines.
Set it up
What you configure once, before turning it on.
- 1Connect OutlookMail, calendar, contacts.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect BigQueryDatasets, queries, schemas.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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