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Retail
Returns fraud
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Production-Ready AI Solution
Retail · Enterprise Solution

Returns fraud

Detect abuse without punishing good customers.

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LIVE AI ENGINE
Real-Time Execution Pipeline

Returns fraud · Live Event Stream

STREAMING
STAGE 01INGEST
Enterprise Data
Lakehouse & APIs
STAGE 02ACTIVE
Breww AI Kernel
Multi-Agent Engine
STAGE 03ACTION
Automated Action
Low Latency SLA
EVENT MONITOR · RETAIL
ONLINE

[INGEST] Multi-source telemetry batch synced (2,480 items)

p99: 12.4ms
Throughput: 420 ev/s
ZERO-DRIFT SLA
The problem

What we hear

Returns policies are either too generous (fraud) or too tight (churn).

Our approach

How we solve it

Return-abuse detection at the customer + basket level with graduated friction and exception handling.

Outcomes

Measured impact

  • Lower returns loss
  • Fewer complaints from good customers
  • Cleaner store-manager guidance

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Store performance

Compare, coach, and act at the store level.

Talk to us

Bring returns fraud into production.

Share a couple of lines about your setup — a senior partner will get back within a business day.

Discuss this use case

Tell us about your organization and what problem you are solving.

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Returns fraud — Retail · DataCaffe.ai