DATA OPS

RMA Defect-Pattern Watch and Alert

On a schedule, scans recent return authorizations for spikes in a single SKU or reported defect, and alerts quality and ops in Slack when a product crosses a return-rate threshold.

CategoryData Ops
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled daily run
  • ActionAggregate recent RMAs by SKU and defect in PostgresPostgreSQLPostgres
  • LogicCompute return rates and flag SKUs over threshold
  • LogicBuild defect summary for flagged SKUs
  • OutputPost quality alert to Slack channelSlack

What it does

Turns your stream of RMAs into an early warning system for product quality problems. It periodically aggregates recent return authorizations by SKU and defect reason, compares each product's return rate against a baseline threshold, and raises an alert when one spikes — catching a bad batch before it becomes a recall.

When to use it

Use this when individual RMAs look normal but a pattern across them signals a manufacturing or supplier defect. Valuable for quality, ops, and product teams who need to spot a failing SKU within days rather than at quarterly review.

How it works

  1. 1A scheduled trigger runs on a regular cadence, such as every morning.
  2. 2The bot queries Postgres for RMA records in the recent window grouped by SKU and defect reason.
  3. 3A logic step computes return rate per SKU and flags any that exceed the configured threshold.
  4. 4If any SKU crosses the line, the bot builds a summary with counts, top defect reasons, and trend.
  5. 5It posts the alert to a Slack quality channel; if nothing crosses the threshold, the run ends silently.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect SlackChannels, DMs, threads, mentions.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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