Case Study

ML-Driven Routing Engine Saving $2.4M/Year in Telecom Costs

TelecomNTT
The problem

Challenge

Telecom routing costs for toll-free numbers were bleeding millions annually. Routing decisions were static and never revisited, even as carrier rates and call patterns shifted daily.

What I did

Approach

Built an ML-driven .NET routing engine that analyzed the previous day’s call data, identified optimal routing changes for toll-free numbers, and executed those changes automatically via carrier APIs. The system re-evaluated performance the next day and continuously refined its decisions — a self-improving optimization loop running without manual intervention.

Outcome

Result

$2.4M per year in telecom cost savings. Fully automated — no human in the loop for daily routing decisions. Continuous optimization that got smarter over time.

Involved
.NETMachine LearningAPI AutomationTelecomData Analytics
The service

Work of this kind

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