INVOICE PROCESSING

Reconcile Vendor Statement Line Items Against Recorded Invoices

On a scheduled run, pulls each vendor statement's line items and matches them against invoices recorded in Postgres, then posts a Slack digest of unmatched, mismatched…

CategoryInvoice Processing
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMonth-end schedule fires
  • ActionLoad vendor statement line items
  • ActionQuery recorded invoices from PostgresPostgreSQLPostgres
  • LogicMatch line items and classify discrepancies
  • LogicFilter out fully matched rows
  • OutputPost per-vendor gap digest to SlackSlack

What it does

Compares the line items a vendor lists on their statement against the invoices you actually have on record in your accounting database, then reports exactly where the two disagree: charges on the statement you never recorded, invoices you recorded that the vendor omitted, and amount mismatches on shared invoice numbers.

When to use it

Run it monthly (or whenever a vendor statement lands) so AP catches missing invoices, duplicate billings, and price discrepancies before cutting a payment. Ideal for finance teams managing recurring vendors where statement drift quietly accumulates.

How it works

  1. 1A schedule fires at month-end and loads the parsed statement line items (invoice number, date, amount) for each active vendor.
  2. 2It queries Postgres for all recorded invoices belonging to those vendors over the same period.
  3. 3A reconciliation step joins both sets on invoice number, classifying each row as matched, statement-only, ledger-only, or amount-mismatch.
  4. 4A filter drops fully matched rows so only exceptions remain.
  5. 5A Slack message delivers a per-vendor gap digest with totals and the specific discrepant invoice numbers for AP to resolve.

Set it up

What you configure once, before turning it on.

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

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