On a real-time delivery platform, every second of delay costs an order — yet resolving a simple late drop-off meant an agent hopping between four systems by hand. We put the whole picture, and the fix, on one screen.
On a platform where service happens in real time, every second of delay in a reply turns into an angry customer and a cancelled order — and the centre was buckling under huge load and unusually long handle times.
The core problem was navigation. To fix a simple late delivery or a missing item, an agent had to move by hand between four different systems — the order system, the courier's GPS, the CRM, and the budget-and-credits tool. Tickets from customers and couriers arrived with no pre-sorting, so junior agents burned time just finding the order number and working out what had gone wrong with the business.
Even a trivial action — a credit for a missing dish — meant opening a calculator, checking the customer's history, confirming the credit policy, and keying it all in by hand. The exhausting grind against endless queues and old systems drove frequent churn and made a stable service level hard to hold.
One screen, the intent detected, the credit ready — inside a hard policy guardrail.
We bridged the courier's live location, the order status from the business, and the customer's history directly into the agent's handling screen — no more hopping between four tools to see one situation.
An engine reads the customer's chat or call in real time and identifies the issue at once — for example, “delivery over 30 minutes late” — so the agent starts from the answer, not the investigation.
The system produces an accurate, ready-to-approve credit for the exact situation, so a resolution that used to take a calculator and four checks becomes a glance and a click.
Hard business logic lets the AI propose or auto-approve a credit only up to a preset cash ceiling and only for customers with no fraud history. Anything beyond that requires a shift manager's sign-off.
Incoming tickets arrive classified and matched to their order and context, so junior agents stop wasting the first minutes of every contact just working out what it's about.
For anything outside the guardrail, a person decides. The AI clears the routine at speed and hands the judgement calls, intact, to the team.
More tickets cleared, by the same people, with fewer hand-offs.
Average handle time on calls and chats dropped 15% immediately, growing the centre's capacity so the same headcount could clear about one and a half times as many tickets.
First-contact resolution rose 30% — issues fixed on the first touch, with no bouncing the customer between agents or waiting for manager approvals — cutting operating costs and lifting the platform's CSAT.
The centre got faster without handing money away, because the credit guardrail auto-clears only the small, safe cases and routes everything else to a person — the same auto-up-to-a-limit, human-above-it discipline behind our bank underwriting green lane. The agent owns every judgement call; the AI just removes the four-tab scavenger hunt that used to sit in front of it.
Reef TRH