How Omilia Cuts Deployment Times 33% and Streamlines Data Ops with Snowflake

Omilia builds and operates an end-to-end conversational AI platform that helps organizations shift from fragmented, legacy interactive voice response systems to AI-first contact centers. The company’s AI models process massive volumes of interaction data across a growing roster of global enterprise clients in banking, insurance, and telecommunications. As Omilia’s deployment footprint expanded, the team needed a data platform that could match its pace — one that provided near real-time analytics, simplified governance for regulated industries, and freed engineers from infrastructure maintenance.

Before adopting Snowflake, Omilia’s DevOps and data science teams operated across multiple fragmented environments for reporting, data ingestion, and experimentation. Dashboard updates required manual data assembly. Detecting time-sensitive events — such as an unexpected spike in inbound contact center calls caused by a customer’s website outage — wasn’t achievable in near real-time. Deploying new data environments was slower than the product roadmap demanded, and infrastructure maintenance consumed engineering hours that should have gone toward building new capabilities.

Omilia migrated to Snowflake’s AI Data Cloud running on Amazon Web Services. Snowflake’s fully managed platform consolidated data sources across CRM, delivery, and PMO systems, while its flexible compute model separated storage from compute to eliminate costs from idle warehouses. Distinct environments for reporting, ingestion, and ML experimentation — each independently scalable — gave teams the isolation they needed without the overhead of managing separate infrastructure. Snowflake Horizon Catalog provided governance and compliance controls that met the strict data protection requirements of Omilia’s enterprise clients in regulated sectors.

The results were measurable across operations. Deployment times for new environments dropped 33%, and hundreds of DevOps hours per month that previously went to infrastructure maintenance are now available for product development. Real-time event detection — alerting a customer the moment a website outage is driving a call spike, so they can immediately prepare AI models for the traffic surge — is now live in production. Analytics dashboards update automatically, shifting the team’s time from assembling data to analyzing it.

Looking ahead, Omilia plans to expand automation and deepen the integration between its AI stack and Snowflake’s AI Data Cloud. The platform has become foundational to how Omilia delivers intelligent, data-driven experiences to its customers — and to how its teams innovate at the pace its global enterprise clients expect.