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 metrics and return the same answers to your business questions.
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.
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.
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: Manage semantic 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.
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.
Frequently Asked Questions
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.
A semantic layer ensures consistent, trusted data across your analytics and AI workflows. It simplifies access by translating complex data into business terms that tools like Excel, Tableau, Power BI, Python, and AI agents can understand. This alignment supports explainable AI, governed agentic reasoning, and faster, more accurate insights—no matter how or where data is consumed.
AtScale connects directly to your cloud data warehouse and builds live semantic models that power both BI dashboards and AI workflows. It translates queries—from tools or agents—into optimized SQL 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.
No. AtScale complements your existing stack by integrating directly with platforms like Snowflake, 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.
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.
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.
A good place to start is with the meaning behind the data. Agents shouldn’t have to guess what “revenue” means or which definition of “customer” to use based on a table name or schema. A governed semantic layer gives them those definitions upfront. It’s also worth cleaning up table and column descriptions, since that extra context helps agents understand what they’re working with. The same access rules you already use for people should apply to agents, and their requests and queries should be logged so you can see what they did and why. All of this makes it easier for [AI agents](link: agentic AI glossary) to work with warehouse data without having to figure things out as they go.
