The problem
For a big retailer, most deliveries are simple. A box lands on a doorstep and that's the end of it.
The hard ones are the big, heavy orders: fridges, appliances, pallets of decking. Those need someone home to receive them, a truck that can reach the door, and sometimes a code to get through a gate. If any of those isn't sorted out ahead of time, the delivery fails.
All these delivery failures are almost preventable. The only catch is volume: with thousands of high-value orders a day, there's no realistic way for a team to check them all by hand in time.
So the risky ones slip through. And one failed delivery is never just one problem; it's a wasted trip, a rescheduled slot with additional cost, and often a customer who's run out of patience and won't come back. Across thousands of orders a day, that quietly becomes one of the most expensive problems in the whole operation.
Why manual review fell behind
Catching delivery problems early came down to people, reviewing orders one at a time by hand. Ground operations agents worked through them in a spreadsheet, checking each order against the things that tend to derail a delivery: address quality, access and road suitability, gated entrances and passcodes, weather, and the customer's past delivery history.
It worked, but it couldn't keep up. A good agent got through about fifty orders a day, but the retailer ships thousands. So most orders never got that second look, and the risky ones went out unchecked resulting in customer complaints.
What we built
We start by getting close to the people who live with the problem. We shadowed reviewers, sat with live cases, and mapped how decisions actually got made on the floor. That work turned years of informal instinct into one shared definition of delivery risk — the first time it had ever existed in a single place.
Then we built the decision layer that acts on it, not one model trying to do everything, but a small ecosystem of agents, each owning one job and handing off to the next.
A scoring agent rates every high-value order for delivery risk; the same signals a seasoned reviewer used to weigh by hand: address quality, weather, access requirements, road suitability, gated communities, passcodes, and the customer's delivery history. Clear-cut cases close themselves. The rest get passed on for review with all the context already attached, so a reviewer isn't starting from scratch.
A conversation agent reaches out when it spots a likely issue. It sends the customer a personalized text, reads their replies with natural-language understanding, and works the problem in a short back-and-forth confirming details, sorting out an address, or rescheduling. Most issues resolve in a few messages. If the exchange hits a set limit without a clear resolution, it's handed to a person with the full thread already in hand.
An operations dashboard ties it together. The team gets one view of delivery risk across orders: what's been handled automatically, what's sitting in the manual queue, and where each case stands. The agents do the work; the people stay in control of it.
The result
About 73% of delivery review cases now close automatically, end to end and the team covers eight times the daily scope without hiring anyone else.
Reviewers aren't the bottleneck anymore. They're not buried under cases an AI agent could have closed. Their judgment goes where it's worth something, the genuinely hard calls, and everything else is handled correctly before anyone has to look.