CHATBOTS
Weekly policy-change impact digest
On a schedule, the workflow re-simulates recent draft expenses against the current policy and posts a digest of which pending submissions would now fail and why.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule fires
- ActionRead pending expenses and policy from AirtableAirtable
- ActionRe-simulate each record with the modelOpenAI
- LogicKeep at-risk records and group by employee
- OutputPost the impact digest to finance SlackSlack
What it does
Runs the what-if simulator in reverse: instead of one employee checking one purchase, it sweeps all draft and pending expenses on a schedule and re-scores them against the latest policy. It surfaces the submissions that would be rejected or trimmed, grouped by employee and reason, so finance can nudge people before the approval deadline.
When to use it
Run it weekly ahead of payroll or expense-cutoff dates, especially after a policy update, when employees may have drafted expenses under the old rules.
How it works
- 1A schedule fires the run (e.g., every Monday morning).
- 2Pending and draft expense records are read from Airtable along with the current policy ruleset.
- 3The model re-simulates each record and labels it pass, trim, or reject with a reason.
- 4A logic step keeps only the at-risk records and groups them by owner.
- 5The digest is posted to the finance Slack channel with per-employee callouts and the dollar impact of each issue.
Set it up
What you configure once, before turning it on.
- 1Connect AirtableBases, tables, views, automations.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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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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