Baseline → Intervention → Result
The complete path from the original constraint to the measured business outcome.
Baseline
The starting point
A 15-person call center was overwhelmed by more than 1,200 daily calls, with hold times exceeding 12 minutes.
- Most calls concerned shipment status and delivery exceptions.
- Agents lacked immediate access to live transportation data.
- First-call resolution was limited to 38%.
Intervention
What NetxBytes changed
NetxBytes built a natural-language voice AI system connected to the client’s transportation management system.
- Automated live shipment-status and exception inquiries.
- Used issue type and customer tier to route escalations.
- Added analytics for resolution, escalation, and handling volume.
Result
What improved
Routine requests moved out of the agent queue, reducing wait times and increasing support capacity without proportional headcount growth.
- 85% Auto-resolved inquiries
- 60% Fewer escalations
- 90s Average hold time
- $340K Estimated annual savings
How the solution fits together
A high-level view of the system flow. Sensitive client implementation details are intentionally omitted.
Outcomes tied to the original baseline
Routine requests moved out of the agent queue, reducing wait times and increasing support capacity without proportional headcount growth.
Call-center and transportation-system logs were compared before and after launch. Savings were estimated from avoided handling volume, reduced escalation demand, and associated staffing costs.