Supercharge AI development and analysis with Omni’s native Snowflake integrations
Cortex Code
Streamline end-to-end development across your data stack with Cortex Code and Omni. Load Omni skills and generate an Omni data model, materialize Semantic Views, create dashboards, and more — all from a central interface.
Semantic Views
Unlock trustworthy AI everywhere by using Snowflake semantic views alongside Omni’s built-in semantic model. Define datasets and metrics across tools to give teams a fast, flexible foundation for analyzing data.
Using Cortex AI functions in Omni
Bring AI into everyday use cases with data enrichment, sentiment analysis, categorization, and AI chat powered by Snowflake's LLM functions + Omni.
Omni and Snowflake help customers...
Deliver self-service for every user
Anyone can get insight from Snowflake data using natural language, point-and-click, Excel functions, and SQL
Eliminate data engineering bottlenecks
When new data lands in Snowflake, users can analyze metrics immediately, without data engineers modeling it first
Consolidate your data stack
Omni brings together a built-in semantic layer, fast data exploration, beautiful visualizations, and governed BI in a single platform atop Snowflake
Scale access to data without adding headcount
Support data-driven decision-making and powerful data products — with minimal administration
Speed up analytics development
Facilitate fast ad-hoc analysis, rapid iteration, and an easy path to standardization – helping your data team keep pace with the business
Build exceptional user-facing analytics
Omni makes it easy to create customized, interactive data products to meet performance SLAs – especially when supported by Snowflake’s concurrency scaling
Choosing Omni is solving for more than just BI. We've also primed ourselves to leap forward into AI because the semantic model is at the heart of the platform. That's not true for many other tools.- Mike Doll, VP of Data
Omni's built-in semantic model helps data teams quickly build enterprise data applications. Being able to validate metric definitions allows users to improve the accuracy of models to produce trustworthy results, which is essential for enterprise AI use cases.- Josh Klahr, Head of Data Warehousing at Snowflake & Harsha Kapre, Director at Snowflake Ventures