From overloaded inboxes to under 30 minutes — how Omest scaled customer care without scaling headcount

The challenge

Support became the bottleneck

Growth meant more lanes, more partners, and more questions in the inbox. Response times stretched past six hours. Agents re-read the same threads, hunted for shipment context in different tools, and still risked inconsistent answers. Scaling the team linearly was not the plan — scaling quality was.

Our approach

Embedded, not distant

We did not whiteboard from afar. A product engineer worked on-site inside Omest — shadowing agents, mapping real exceptions, and pressure-testing ideas against live operations. The result: a system shaped by how logistics actually breaks, not how software imagines it.

The solution

AI that fits the cargo, not the other way around

We delivered a custom AI-assisted communication layer on top of Omest’s reality: automatic classification of inbound mail, intelligent draft replies grounded in shipment and tracking data, optional auto-send paths where policy allows, and a tight bridge to Desk365 so nothing lives in a silo. Built multi-channel-ready — because tomorrow’s message might not be an email.

In the product capture on this page, you see the real Admin Dashboard — the pulse view for traffic, the snapshot of open vs resolved tickets, and the red-line visibility into Desk365 sync failures that leadership demanded. That is the transparency layer: when AI accelerates replies, humans still keep score on reliability.

• Intent, priority, and language signals — routed before a human opens the thread
• Draft responses that mirror Omest tone and pull the right reference numbers
• Operational context from internal systems — fewer “let me check” loops
• Audit-friendly flow — humans stay in control where it matters

Results

Speed customers feel. Calm teams notice.

First-response windows dropped from hours to under 30 minutes. Repeat questions stopped eating the day. Agents shifted from copy-paste triage to judgment calls and relationship work — with measurably steadier reply quality as the models learned Omest’s world.