What if your operational intelligence and your analytics capabilities were unified and truly interoperable, driving the same insights?

Healthcare's data landscape is uniquely complex. Patient information is spread across HL7v2 messages, C-CDA documents, X12 transactions, and proprietary formats, each system encoding the same clinical concepts differently. A single diagnosis may appear under multiple codes across multiple vocabularies. A single patient may exist as several records across several systems.

The traditional approach to unifying this data involves standing up a FHIR server for interoperability, a separate data warehouse for analytics, and a web of ETL pipelines connecting the two. Each system maintains its own access controls, audit trails, and compliance posture.

This duplication is costly. The same clinical data is replicated across the FHIR server, the warehouse, and multiple staging layers — each adding storage, compute, and operational overhead. Meanwhile, the FHIR server itself often becomes a bottleneck. Most implementations were designed for transactional use cases — document exchange, point lookups, regulatory APIs — not for the access patterns of modern analytics, ML pipelines, or AI agents that need to scan millions of resources efficiently.