Traversaal/Labs/Case studies/Retail delivery
Case study · Retail · 2025

Closing 73% of delivery review cases automatically with no new headcount.

We built a decision layer for one of the Fortune 500 retailers that scores every high-value order for risk, resolves most issues with the customer directly, and routes only the hard cases to a person.

Overview

For a top US retailer chain, a damaged appliance isn't a mere forty-dollar return. It means delayed installations, wasted contractor time, and frustrated customers who walk away. We built a decision layer that handles roughly 73% of the orders flagged for review end to end, runs about fifteen times faster per order than the previous workflow, and lets the team cover eight times as many orders without adding headcount.

A thin layer that sits between the OMS and the human queue. The agent decides; humans see only what genuinely needs them.

Challenge  | Three things made catching problems early without adding headcount hard.

High-value orders carry consequences that small parcels don't. The retailer wanted to catch problems earlier without putting more people on the queue.

  • Signal was scattered. Order status, carrier updates, customer messages, and address data all lived in separate systems. Nothing pulled them together for an operator looking at a single order.
  • Judgment lived in people's heads. The team had years of informal knowledge about which combinations of item, route, and customer history warranted a second look. None of it was written down or applied consistently.
  • The existing process couldn't be paused. Whatever shipped had to run alongside the live workflow without disrupting active orders.

The work  | An agentic decision layer built with the team, not handed to them.

We shadowed reviewers, walked through how decisions actually got made, and used that work to draft a single shared definition of risk the system could act on. The agent scores each order itself, runs its own conversation with the customer when something looks off, and escalates to a person only when the case genuinely needs one.

Agentic risk & routing

Risk scoring & action classification for every incoming order.

Address & satellite checks

Validation w/ map and satellite imagery before dispatch.

Weather-aware risk

Forecast checks tied to delivery zones and item type.

The biggest change isn't the speed. It's that the people on the floor can finally focus on the orders that actually need them.
Director of OperationsFortune 500 US home improvement retailer

Outcomes  | Outcomes the ops team can feel on the floor.

~73%
of curated review cases handled automatically end to end
15×
faster per-order review than the manual workflow
daily review scope with no added headcount

Every engagement ends with full handoff. Code, infra config, runbooks - the system is yours before we leave.

About the client

A national chain shipping thousands of high-value orders a day. We built a decision layer for one of the Fortune 500 retailers that scores every high-value order for risk, resolves most issues with the customer directly, and routes only the hard cases to a person.

Industry
Retail
Year
2025
Engagement
12 weeks
Services
Agentic AI · Risk scoring · SMS automation · Ops dashboard
Status
● Production
Next engagement

Have a similar delivery problem?

Two or three clients at a time. 30-minute call, no pitch deck.

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