Define Once. Trust Everywhere.
Define business logic one time, in one place, and use it everywhere. Power accurate AI and analytics across your entire stack.
Trusted by 2,000+ leading enterprises around the world
AI-Ready Context
Ensure AI-generated queries stay accurate and aligned with your organization’s definitions by grounding AI outputs in governed semantic context.
Governed Metrics at Scale
Centrally define metrics, relationships, and business logic so every analysis uses consistent definitions for trusted insights at scale.
Integrated into Your Ecosystem
Built natively on your cloud data warehouse—no extracts or duplication. Use your data model or our semantic layer with governance integrations to define trusted metrics and business logic.
Intelligent Data Modeling
The foundation for governed, scalable analytics—backed by code.
Unified Metrics and Business Logic
Create reusable business definitions that serve both your end users and your AI agents. Ensure your entire organization uses the same definitions, from revenue to active customers.
AI-Ready: Give richer context to LLMs without requiring users to work directly in the warehouse.
Consistent: Leverage the same trusted metrics across all your tools.
Integrated: Deliver accurate insights across your stack—using your data model or our semantic layer.
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Give AI the Context It Needs.
Define and govern semantics once so your AI delivers accurate insights. Curate warehouse metadata, semantic layers, and usage signals to ensure AI understands your business logic, not just the raw data.
Governed Logic: Centralized definitions ensure consistent metrics everywhere.
AI-Ready Context: Curate data, semantic layers, and behavior to ground AI answers.
Trusted Answers: Reliable, accurate AI outputs aligned with your business meaning.
Read our latest blog on curating AI Context
Code-First Data Models
Define and manage reusable data models entirely in code. Build, version, and automate models that power consistent analytics and collaborative workflows across your organization.
Code-First: Create, update, and maintain models using YAML, with full version control and CI/CD workflow compatibility.
Reusable: Publish tables, metrics, joins, and relationships once and consume them across workbooks, dashboards, and AI workflows.
Governed: Enforce RBAC, approvals, and modular model design to ensure consistency, scalability, and maintainable business logic across your data ecosystem.
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Centralized Management of Your Data
Modular governance with RBAC and approvals ensures scalable, secure, and reusable data assets. Roles and RLS are inherited directly from your cloud warehouse for safe, efficient analysis.
Version Tagging: Manage multiple data model versions for easier iteration and development.
Column Level Security: Restrict sensitive data to authorized users only.
No External Caches: Warehouse-native caching reduces query times without exposing data.
Enterprise-ready semantics
From raw warehouse tables to trusted business insights. See how customers build scalable data foundations with Sigma.
"Sigma’s approach to AI is the right one. It’s not about replacing analysis. It’s about speeding up the time to insight—and keeping people in the loop."
See how Saddle Creek Logistics consolidated 600+ reports, embedded Sigma in under an hour, and built a governed, scalable BI stack on Databricks.
Today, teams at TrovaTrip look at Sigma reports to quickly and seamlessly filter and explore data to answer their own questions.
"The amount of collaboration with Sigma has been really refreshing, and it’s something that we brag about to other vendors."
"I think the biggest value driver for Sigma is that you're not using specialized Python developers to analyze billion row records anymore. You're just adding an Excel user."
Learn how Clay moved from fragmented data processes to a unified, accessible analytics system with Sigma, enabling faster, smarter business decisions across the organization.
