Kobai published a research piece examining three areas in financial services: collateral pledged across a corporate group, aggregate exposure across subsidiaries, and beneficial ownership chains that AML monitoring cannot fully trace.

At Kobai, we focus on a problem most modern data platforms haven’t solved yet: understanding data across systems. Enterprises have invested heavily in platforms like Databricks and Snowflake. The lakehouse is in place. Data is governed. AI initiatives are underway. But when organisations try to connect data across engineering, operations, supply chain, and commercial systems, the challenge becomes semantic — not technical. Different systems describe the same thing in different ways. Context is lost. AI struggles to reason reliably. That’s where Kobai comes in. Kobai is a Semantic Intelligence Layer that sits directly on your lakehouse — bringing meaning, structure, and connectivity to your data without moving it. We translate complex relationships into a governed, queryable knowledge layer, enabling both business users and AI systems to work with data in context. What this enables: Cross-domain data alignment without duplication Faster, more reliable analytics across systems AI use cases grounded in real business context (GraphRAG, agents) A consistent, governed semantic layer built on your existing data platform Built to work natively with Databricks and Snowflake, Kobai allows organisations to move from fragmented data to connected intelligence — without introducing new silos or infrastructure. The lakehouse solved data at scale. Kobai solves data with meaning.