San Francisco, CA — May 19, 2026 — Kobai today introduced new capabilities for its Lakehouse-native Semantic Intelligence Platform, including Kobai Precursor, an AI-driven semantic modeling capability, and a Genie Accelerator designed to help Databricks customers scale AI-powered analytics.
As enterprises adopt AI-driven analytics and tools such as Genie, Databricks’ AI agent that lets any employee chat with their data and get trusted answers instantly, a key challenge is scaling domain context across teams and use cases. Semantic models often require significant manual effort to define entities, relationships, and business logic, limiting adoption and consistency.
Kobai Precursor addresses this challenge by using AI to assist in the creation and evolution of semantic models. By analyzing existing Lakehouse data and metadata, Precursor accelerates the development of ontologies—capturing enterprise entities, relationships, and constraints—while keeping domain experts in control of the final model.
Kobai’s Genie Accelerator builds on this semantic foundation by enabling organizations to reuse governed enterprise context across Genie Spaces. Instead of repeatedly defining tables, synonyms, and business logic for each use case, teams can leverage a shared semantic layer, improving consistency and reducing setup time.
“Enterprises are moving quickly to adopt AI-powered analytics, but scaling domain context remains a major challenge,” said Lee Tedstone, CEO of Kobai. “With Precursor, we are using AI to accelerate how semantic models are built. With our Genie Accelerator, we help organizations reuse that context across use cases, allowing Databricks customers to move faster while maintaining consistency and governance.”
“Organizations adopting AI-powered analytics need a scalable way to create and operationalize trusted semantic models across the enterprise,” said Justin Fenton, Principal Technology Partner at Databricks. “Kobai’s AI-enabled semantic modeling and Genie Accelerator help customers accelerate the development of governed semantic models directly on their Lakehouse architecture, making it easier to deliver consistent, reliable AI and analytics at scale.”
Because Kobai operates directly on data managed in Databricks, all semantic models inherit governance from Unity Catalog and execute using Databricks compute. This allows organizations to add semantic and knowledge graph capabilities without introducing a separate system of record.
For example, in industries such as manufacturing and energy, organizations can use Kobai to connect operational data across assets, supply chains, and engineering systems. By combining AI-assisted modeling with a shared semantic foundation, teams can more quickly understand why a specific asset is delayed, what components are involved, and the potential operational or financial impact.
Kobai will demonstrate these capabilities, including AI-driven semantic model creation and Genie acceleration, at the upcoming Data + AI Summit in San Francisco.
Attendees can learn more and see live demonstrations at the Kobai booth in the Expo Hall.
About Kobai
Kobai provides a Lakehouse-native Semantic Intelligence Platform built on the Databricks platform. Kobai enables organizations to model enterprise entities, relationships, and constraints directly over their data, creating governed semantic models and knowledge graph capabilities without introducing additional data platforms.
Learn more at www.kobai.io