FINANCE

Revenue Variance Investigator

On demand, an agent reconciles a chosen day's Stripe revenue against the Snowflake ledger, drills into the specific transactions causing any gap.

CategoryFinance
Enginepaperclip
Difficultyadvanced
Triggermanual
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerOperator runs agent with target date
  • ActionPull itemized Stripe transactionsStripeStripe
  • ActionQuery ledger line items from SnowflakeSnowflakeSnowflake
  • LogicIsolate unmatched entries and infer cause
  • OutputWrite root-cause memo to Google DriveGoogle DriveGoogle Drive

What it does

When a daily reconciliation comes back out of balance, this agent investigates why. It compares Stripe and ledger line items for the date in question, isolates the exact charges, refunds, or fee entries that don't reconcile, and produces a written explanation of the discrepancy with the offending transaction IDs.

When to use it

Reach for it after an automated check flags a variance and someone needs to know the cause before close — not just that a gap exists. It replaces the manual spreadsheet diffing an analyst would otherwise do.

How it works

  1. 1An operator triggers the agent with a target date.
  2. 2The agent pulls itemized Stripe balance transactions for that day.
  3. 3It queries the Snowflake ledger for the same day's line items.
  4. 4Reasoning over both sets, it identifies unmatched or mismatched entries and hypothesizes causes (late settlement, miscategorized fee, missing refund).
  5. 5It writes a root-cause memo with the variance, the implicated transactions, and a recommended fix to a Google Drive doc.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect StripeCustomers, subscriptions, payments.
  2. 2
    Connect SnowflakeWarehouses, queries, shares.
  3. 3
    Connect Google DriveDocs, sheets, slides, files.
  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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