INVOICE PROCESSING

AI-code supplier invoices to GL accounts and route to the owning ClickUp list

Uses an LLM to read each Outlook invoice, classify it to a GL account and cost center, validates the code against the Postgres chart of accounts.

CategoryInvoice Processing
EngineSim + Paperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew supplier invoice email in OutlookOutlook
  • ActionLLM proposes GL account and cost centerOpenAI
  • ActionValidate code against Postgres chart of accountsPostgreSQLPostgres
  • LogicBranch: confident valid code vs. needs coding
  • OutputCreate ClickUp approval task in owning listClickUpClickUp

What it does

Replaces manual GL coding at intake. An LLM reads the invoice text and proposes a general-ledger account and cost center, the proposal is checked against the live chart of accounts in Postgres, and the invoice is filed as an approval task in the ClickUp list belonging to the responsible cost-center owner.

When to use it

Use it when AP keys GL codes by hand and invoices pile up waiting for the right department head to be assigned. It pre-codes spend and puts each invoice in front of the person who owns that budget line.

How it works

  1. 1A new supplier invoice email in Outlook triggers the run.
  2. 2An LLM reads the invoice and proposes a GL account, cost center, and confidence.
  3. 3The proposed code is validated against the Postgres chart of accounts.
  4. 4A branch routes low-confidence or invalid codes to a Needs-Coding holding list.
  5. 5Valid, confident codes resolve the owning ClickUp list for that cost center.
  6. 6A ClickUp approval task is created with the GL code, amount, and owner assigned.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect OutlookMail, calendar, contacts.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect ClickUpDocs + tasks + chats in one workspace.
  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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