Today at Snowflake Summit, Snowflake announced Semantic Model Sharing—a new capability that enables data product teams to make their offerings AI-ready in Snowflake Marketplace by sharing LLM-optimized semantic models alongside data. Bobsled was featured as a launch partner alongside our customers Cotality and Deutsche Börse.
This announcement represents a significant step forward in making data products more accessible and valuable in the AI era. Here's what it means for data product teams and why semantic models are becoming critical infrastructure for AI-powered data consumption.
What Is Semantic Model Sharing?
Semantic Model Sharing in Snowflake enables data product teams to distribute not just raw data, but also the business context that makes that data meaningful. When customers access data products through Snowflake Marketplace, they receive both the underlying datasets and semantic models that map business terminology to database schemas.
This approach transforms how customers interact with external data products. Instead of spending weeks understanding data structure and relationships, customers can immediately begin querying data using natural language through tools like Snowflake's Cortex.
Key Benefits for Data Product Teams
Make Data Products AI-Ready
With semantic models, customers can query data using natural language directly within Cortex Agents. This eliminates the traditional barriers to data adoption and enables immediate value creation from data products.
Share Data with Business Context
Semantic models ensure that when you share data, you're also sharing the critical business context that makes it actionable. This includes field definitions, relationships between entities, and the business logic that governs how data should be interpreted.
Speed Up Onboarding and Enablement
Traditional data product onboarding involves extensive documentation, training, and custom integration work. Semantic models eliminate much of this friction by providing machine-readable context that enables immediate data exploration and analysis.
Why Semantic Models Are Critical in the AI Era
The rise of AI-powered data consumption is fundamentally changing how customers interact with data products. Several trends are driving this transformation:
AI-powered agents are becoming central to data consumption. Tools like Cortex in Snowflake enable analysts to interact with data through natural language rather than complex SQL queries. This democratizes access to data insights across organizations.
Natural language queries are the new interface. Text-to-SQL tools allow analysts to ask questions in plain English and receive accurate results. However, these tools require semantic context to generate meaningful responses.
Context drives accuracy. Semantic models provide the essential context—from field definitions to entity relationships—that AI systems need to generate accurate answers. Without this context, AI tools may produce technically correct but business-meaningless results.
How Bobsled Enables AI-Ready Data Products
Bobsled helps data companies build AI-ready data products through several key capabilities:
Data sharing to AI platforms. We enable seamless distribution to platforms like Databricks and Snowflake, where customers increasingly expect AI-native experiences.
AI-optimized data product structure. Our platform helps teams structure data products with the semantic layers and metadata that AI tools require for accurate interpretation.
Interoperable semantic layers. We build semantic models that work across different platforms and tools, ensuring customers can access data insights regardless of their preferred analytics environment.
Looking Forward
The announcement of Semantic Model Sharing represents a broader shift toward AI-native data products. As AI tools become the primary interface for data exploration and analysis, semantic models will become essential infrastructure rather than nice-to-have features.
For data product teams, this means thinking beyond just data delivery to consider how customers will actually interact with and derive value from data. The companies that invest early in building AI-ready data products—complete with rich semantic models—will have a significant advantage in the evolving data economy.
Get Started
Ready to build AI-ready data products with Bobsled? Schedule a platform tour with our solution architects to learn how we can help you leverage Snowflake's new Semantic Model Sharing capabilities.
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