We Know Your Industry Because We've Delivered In It

Thrivent’s digital platforms were hampered by disjointed, siloed legacy systems and slow third-party data hosts, resulting in inefficient data access and a laggy user experience for their banking and investment customers.

We developed the Thrivent Enterprise Integration Datastore using Kafka for real-time streaming and DynamoDB for high-speed retrieval. By implementing a GraphQL layer and a robust AWS-based data pipeline, we consolidated multiple systems of record into a single, synchronized source of truth that powers responsive applications while reducing third-party API costs.

Multiple systems of record consolidated into a single source of truth, reducing third-party API costs and eliminating data lag.

Medtronic relied on manual, paper-based data entry and whiteboards to track production metrics, leading to reporting delays and a lack of real-time visibility that hindered their ability to optimize production lines and manage equipment downtime.

We replaced manual processes with automated real-time data pipelines using Kafka and Confluent. By capturing IoT sensor data via MQTT and integrating it with Manufacturing Execution Systems (MES), we built Java-based stream applications that provide actionable insights and predictive maintenance alerts. This digital transformation standardized data across factories and enabled instant visibility into production health via Power BI.

Manual paper-based production tracking replaced with real-time automated pipelines across factories.

Collision Auto struggled with fragmented data across multiple ERP systems, making it impossible for leadership to get a unified view of revenue, inventory, and sales performance across their West Coast distribution network.

We modernized their infrastructure by building a centralized data lake on AWS S3 and a cloud data warehouse on Snowflake. By integrating disparate ERP data through custom APIs and surfacing insights via Power BI, we enabled dynamic pricing strategies and a scalable foundation for future data science initiatives.

Unified data view across multi-ERP operations, enabling dynamic pricing and a scalable data foundation.

Coban needed to speed up the delivery of mission-critical features for in-vehicle law enforcement systems, but their hardware-dependent development process was bottlenecked by manual workflows and a lack of automated on-device testing.

We architected a custom DevOps pipeline on Azure, utilizing isolated VPNs to bridge cloud-based builds with a physical hardware lab. By implementing automated deployment agents and advanced branching strategies, we enabled features to be built in the cloud and tested on-device in isolation, slashing build times by 50% and significantly increasing the speed of innovation for real-time video streaming capabilities.

Build times cut by 50%, cloud-to-hardware deployment automated end to end.