FINANCE

Front Dispute Classification Warehouse Logger

On every resolved billing dispute, writes a normalized row (root cause, resolution path, amount, time-to-resolve) to Postgres for trend reporting.

CategoryFinance
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerBilling conversation resolved in FrontFront
  • ActionReconcile resolution against StripeStripeStripe
  • LogicNormalize root cause and outcome code
  • ActionInsert row into disputes tablePostgreSQLPostgres
  • OutputConfirm logged via internal noteFront

What it does

This workflow turns resolved billing disputes into clean analytics data. As each Front billing conversation closes, it reconciles the resolution against Stripe (refund, credit, or no-action), normalizes the root cause and outcome, and appends a structured row to a Postgres disputes table. Over time you get a queryable record of why customers dispute charges and how often each path is used.

When to use it

Use it when finance or support leadership needs dispute trends — top root causes, refund-versus-credit ratios, average time-to-resolve — instead of guessing from anecdotes.

How it works

  1. 1A Front billing conversation moving to resolved triggers the run.
  2. 2Stripe is checked for any refund or balance transaction tied to that customer in the dispute window.
  3. 3Branch logic maps the resolution into a normalized outcome and root-cause code.
  4. 4A row is inserted into the Postgres disputes table with customer, amount, cause, outcome, and timestamps.
  5. 5The Front conversation receives a short internal note confirming the dispute was logged.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  2. 2
    Connect StripeCustomers, subscriptions, payments.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  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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