TheUniversal Semantic Layer for Enterprise Analytics and AI

Define business metrics and logic once, and expose them consistently to dashboards, analytical applications, LLMs, and autonomous agents.

AtScale provides a universal semantic layer that defines metrics, relationships, and business logic once and exposes them consistently to BI tools, analytical applications, LLMs, and autonomous agents. This ensures that analytics remain accurate, governed, and repeatable as execution shifts from human-driven workflows to agent-driven systems.

What Is a Semantic Layer?

Semantic Models for LLMs & Agents

Expose governed metrics and logic through MCP so AI systems execute analytics deterministically.

Conversational Analytics

Natural language queries resolve against defined semantic objects.

Consistency Across AI & Humans

Dashboards, applications, and AI agents operate on the same governed metrics.

Shared Semantic Definitions

Use the same metric and dimension definitions across BI tools, applications, LLMs, and agents without rewriting logic per platform.

Cross-Platform Semantic Interoperability

Expose semantic models consistently across Snowflake, Databricks, BigQuery, Redshift, and other cloud data platforms.

Standardized Semantic Access

Expose semantic models through consistent interfaces so tools, applications, and AI systems can access the same definitions programmatically.

Deterministic Semantic Execution

AI analytics execute through defined logic, not probabilistic inference.

Versioned Model Control

Know exactly which metric definitions were active at the time of analysis.

Auditable AI Workflows

Reconstruct inbound prompts and executed queries for compliance and review

Enterprise-Ready AI Semantics

Semantic Models for AI Agents: Expose governed metrics and logic through MCP so AI systems execute analytics deterministically.

Conversational AI for Analytics:Natural language queries resolve against defined semantic objects.

Consistency Across AI and Humans: Dashboards, applications, and AI agents operate on the same governed met.

Learn More

Cost Control & Performance for AI Workloads

Semantic Aggregation: AI and BI queries resolve against optimized aggregates instead of scanning raw fact tables — delivering sub-second performance at enterprise scale.

Predictable Performance at Massive Scale: Serve interactive analytics on fact tables with hundreds of billions of rows without multi-minute latency that breaks conversational and agentic workflows.

Economically Scalable AI Prevent repeated full-table scans that drive runaway warehouse costs. Support thousands of daily AI interactions without degrading trust, performance, or economics.

Learn More

Optimize Performance with a Universal Semantic Layer

Streamline Data Governance with a Universal Semantic Layer

Governance for Explainable AI Analytics

Deterministic Semantic Governance: Execute analytics through governed semantic models so AI-driven results are based on deterministic logic, not inferred behavior.

Versioned Model Control: Track semantic model and metric versions so the exact logic in effect at the time of analysis is known and reviewable.

Auditable Analytics Execution: Store inbound requests and executed queries to reconstruct, review, and justify AI-generated analysis when required.

Learn More

Unified Semantic Modeling

Avoid Metric Sprawl: Ingest models from dbt, Power BI, LookML, and more—unified under a single semantic layer.

Governed Metrics: Define KPIs once in open-source SML with Git-based CI/CD for versioning and control.

CI/CD + Version Intelligence: Managesemantic models with Git workflows, CI/CD pipelines, and full traceability.

Agent-Powered Collaboration: Enable real-time teamwork with intelligent agents that streamline modeling and validation.

Hybrid Modeling Experience: Support code-first and no-code workflows—AI copilots assist users across all skill levels.

Learn More

Support Multi-Persona Modeling with a Universal Semantic Layer

Flexible Deployment Options and Pricing with a Universal Semantic Layer

Flexible Deployment & Pricing Options

Deploy Anywhere, Scale Seamlessly: Kubernetes‑based deployment in public clouds, private clouds, or hybrid environments, and available natively on Snowflake & GCP marketplaces.

AI-Ready Infrastructure: Provisioned for high‑throughput agentic AI workloads and real‑time BI access, in under 5 minutes.

Transparent Consumption-Based Pricing: Pay only for compute and queries, no user‑based licensing, ideal for unpredictable AI volume usage.

Learn More

Frequently Asked Questions

What is a universal semantic layer?

A universal semantic layer is a centralized business logic layer that sits between your data and any analytics tool or AI application. It defines metrics, hierarchies, and relationships once—so both humans and intelligent agents can access consistent, governed data without needing to move or transform it. This foundation enables interoperability across BI dashboards, AI copilots, and LLM-powered agents.

Why do I need a semantic layer?

Asemantic layer ensures consistent, trusted data across your analytics and AI workflows. It simplifies access by translating complex data into business terms that tools likeExcel,Tableau,Power BI, Python, andAI agents can understand. This alignment supports explainable AI, governed agentic reasoning, and faster, more accurate insights—no matter how or where data is consumed.

How does AtScale’s semantic layer work?

AtScale connects directly to yourcloud data warehouse and builds live semantic models that power both BI dashboards and AI workflows. It translates queries—from tools or agents—into optimizedSQL that runs at scale, using real-time data without requiring movement or duplication. This allows both human and agentic consumers to interact with governed, up-to-date business logic.

What are the benefits of using AtScale’s semantic layer?

AtScale delivers faster query performance, reduced compute costs, and a single source of truth for metrics. It empowers both business users and AI agents to access consistent, explainable data—accelerating analytics and decision-making across teams. By unifying semantic logic across your stack, AtScale makes your data ecosystem AI-ready by design.

Does AtScale replace my existing BI tools or data warehouse?

No. AtScale complements your existing stack by integrating directly with platforms likeSnowflake,Databricks,Google BigQuery, and tools like Tableau, Power BI, Excel, and Python. It acts as the semantic layer between your warehouse and all consuming applications—BI, data science, and AI agents alike.

Can AtScale’s semantic layer be used for both BI and AI/ML?

Yes. AtScale powers traditional BI dashboards and feeds consistent features and business definitions to AI/ML pipelines. This enables generative AI tools,LLM agents, and machine learning models to reason over governed data the same way a BI dashboard would—ensuring semantic integrity across all use cases.

How is AtScale different from other modeling tools?

Unlike tools that require transforming or duplicating data, AtScale models live data directly from your warehouse. It provides real-time, governed access to business logic for both BI tools and AI systems—removing the friction between data modeling and intelligent decision-making. This makes it uniquely suited for agentic AI workflows.

Is AtScale installed on-premises or in the cloud?

AtScale is a flexible, cloud-native platform that can be deployed in the cloud, on-premises, or in hybrid environments. Most customers deploy AtScale alongside platforms like Snowflake, Databricks, or Google BigQuery—but it also supports on-premise use cases for compliance-heavy environments, without compromising support for agent-based analytics.