Introduction

A private equity backed U.S. home services provider partnered with OmniReach to validate whether sales conversation intelligence could operate reliably, cost efficiently, and at scale across field environments with limited or no connectivity. The objective was to reduce cloud dependency, improve data reliability, and enable faster decision-making without long term vendor lock-in.

The Challenge

Field teams operated in low connectivity environments, resulting in incomplete or failed conversation captures. A cloud first processing model led to rising compute and storage costs, delayed insights, and unreliable preprocessing. Large raw audio files increased bandwidth usage, while dependence on commercial SDKs introduced escalating costs and long term vendor risk.

The Solution

OmniReach designed and validated an edge driven conversation intelligence framework that shifted critical processing to on device inference. The solution followed a privacy first, open source approach with smart segmentation to remove blank and non conversational data before cloud transfer. The architecture was optimized for constrained networks and validated for scalability through benchmarking against leading commercial solutions.

The Impact

The solution reduced cloud compute and storage costs by approximately 50% and delivered 2× faster time to insight through edge preprocessing. Conversation capture achieved near 100% reliability, even in offline conditions, improving field workflows and executive confidence while establishing a scalable, privacy aligned foundation for growth.

Summing up

This case proves that enterprise grade AI does not need to be cloud heavy, expensive, or vendor locked.
OmniReach helps enterprises redesign how intelligence operates reliably, cost efficiently, and at scale.