ERGO Hestia, one of Poland's leading insurance companies, operates a large-scale pricing platform supporting over 100 models and 1,000 variables. As one of Databricks' largest Polish users, ERGO Hestia has built substantial capability in state-of-the-art millisecond pricing and industry-leading execution speed.

However, because the team is constantly pursuing the next great innovation in insurance technology, they recognized an opportunity to further maximize revenue by introducing real-time B2C capabilities. While the existing architecture was highly functional, the shift toward continuous model updates and instant customer responsiveness revealed a new challenge: sustaining innovation velocity as complexity scaled. The team had mastered the art of competitive pricing and was now ready to pioneer the next generation of real-time pricing delivery.

Building on a close and productive partnership, ERGO Hestia and Databricks jointly discussed how to enhance the previous architecture for the next generation of real time pricing. This great collaboration led to the evolution of the platform using Lakebase to provide an Online Feature Store alongside Mosaic AI Model Serving Endpoints to keep all data and logic within the Databricks ecosystem. This architecture keeps both data and model serving inside the lakehouse, eliminating external systems and reducing model deployment time By unifying governance via Unity Catalog the team integrates data and model management to ensure full traceability and long term retention of historical training sets and model versions. This architecture provides Pricing experts with a reliable audit trail to ensure every decision remains fully traceable and verifiable while maintaining peak model performance. Ultimately this transformation positions the team to accelerate innovation velocity and respond rapidly to market conditions while continuously advancing their state of the art pricing models.

The previous architecture followed a logical pattern where Databricks ingested and transformed pricing data through its medallion architecture, then exported processed datasets to an external Azure PostgreSQL database. An intermediate adapter layer handled caching and exposed data to the pricing engine. This worked well when throughput was moderate. Yet as data volume grew and model iteration accelerated, the multi-hop pattern by moving data out of the lakehouse, through an external database, through a caching layer, to the application, began to constrain performance and agility.

For an organization managing 100+ models across 1,000+ variables in a regulated industry, this fragmentation created both operational friction and governance risk.

ERGO Hestia's transformation was built on three core technical pillars that unified all operations within the lakehouse.