FINANCE

SaaS Spend Anomaly Investigation Agent

When a Stripe charge exceeds a vendor's historical baseline, an agent investigates the spike using Snowflake history and web research.

CategoryFinance
Enginepaperclip
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerStripe charge exceeds vendor baselineStripeStripe
  • ActionPull 12-month vendor charge historySnowflakeSnowflake
  • ActionResearch vendor pricing changes on the webPerplexityPerplexity
  • LogicClassify spike cause from evidence
  • OutputOpen Linear ticket with findings and recommendationLinearLinear

What it does

Turns a spend spike into a worked investigation, not just an alert. When a Stripe charge comes in materially above a vendor's trailing average, an agent gathers the vendor's billing history from Snowflake, researches whether the vendor recently changed pricing, and writes up the most likely cause with a recommendation.

When to use it

Use it when a raw threshold alert is not enough and you want the first pass of analysis done before a human looks. It is ideal for finance teams who want every unusual software charge triaged with context, separating real price increases from seat growth or duplicate billing.

How it works

  1. 1A Stripe webhook fires when a charge exceeds the vendor's baseline.
  2. 2The agent queries Snowflake for the vendor's trailing 12-month charge history.
  3. 3It runs web research to check for announced price changes or plan migrations.
  4. 4The agent reasons over the evidence to classify the spike as price increase, seat growth, duplicate, or unknown.
  5. 5It drafts a recommended action and opens a Linear ticket assigned to the budget owner with its findings attached.

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 PerplexitySearch-grounded answers with citations.
  4. 4
    Connect LinearIssues, projects, cycles, triage.
  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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