CHATBOTS

Approval-route predictor for new expenses

When a new expense record is created in Airtable, the workflow predicts its full approval path, auto-approves clearly in-policy items.

CategoryChatbots
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew expense record created in AirtableAirtableAirtable
  • ActionPredict approval route with the modelOpenAI
  • LogicBranch: auto-approve / single / dual approval
  • ActionWrite status and predicted path to AirtableAirtableAirtable
  • OutputRoute review cases to the approver in SlackSlack

What it does

The moment an expense is created, it predicts what will happen to it: which approval tier it hits, whether it auto-clears, or which manager and finance approver it must pass through. Clean, under-cap items are marked auto-approved; anything ambiguous is routed to the correct human with a plain-English reason, so nothing sits in an unassigned queue.

When to use it

Use it when approval routing is manual and inconsistent — items bounce between approvers or stall because no one knows who owns them. This makes the routing decision deterministic and immediate.

How it works

  1. 1A new record in the Airtable expenses table triggers the run.
  2. 2The model evaluates the expense against policy thresholds and the approval matrix to predict its route.
  3. 3A logic branch separates auto-approve, single-approver, and dual-approval outcomes.
  4. 4Auto-approved items have their status and predicted path written back to Airtable.
  5. 5Items needing review are sent to the assigned approver in Slack with the amount, category, and the specific rule that triggered review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AirtableBases, tables, views, automations.
  2. 2
    Connect OpenAIModels, embeddings, files.
  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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