AI-Ready Semantic Models
Create semantic scaffolding for LLMs and AI agents that interpret business data autonomously.
Ask in Natural Language
Use NLQ tools like Databricks Genie and Snowflake Cortex to retrieve governed answers in plain English.
Consistentcy for Agents & Humans
Ensure both dashboards and AI systems use the same trusted metrics and business logic.
Deep & Wide Support for All Tools
Ensure every BI dashboard, LLM, or AI agent shares a unified understanding of metrics and dimensions.
Cross-Platform Semantic Interoperability
Support Snowflake, Databricks, BigQuery, Redshift, and more through a single semantic layer.
Self-Service Analytics
Empower business users with governed, self-serve access in tools like Tableau, Excel, Power BI, and Looker.
Centralized Metric Governance
Maintain and propagate business definitions from a single semantic model to every downstream tool.
Multidimensional Modeling
Support complex business logic like 53-week calendars, custom fiscal periods, and currency conversions.
Hybrid Modeling Experience
Build and maintain semantic models with visual interfaces or YAML-based code, tailored for every user.
Enterprise-Ready AI Agents
Context-Driven AI Agents: Empower agentic AI workflows and LLMs with precise, business‑contextual data, no hallucinations.
Natural Language Queries Engineered: Let users ask questions in simple language, while the semantic layer translates them into optimized queries over live data.
Semantic-First AI Assistant Integration: Train chatbots and virtual analysts on consistent, governed metrics to deliver reliable, auditable answers.
Performance Optimization
Smart Query Routing & Caching: Use intelligent pushdown, aggregate awareness, and caching to deliver sub-second results directly from your cloud warehouse.
Cloud-Efficient Scale: Automatically optimize performance and costs with engine-level optimization, consumption‑based pricing, and query planning tuned for modern data platforms.
Agentic Workload Readiness: Support latency-sensitive AI agent interactions and human‑driven BI workflows with ultra-fast response times.
Streamlined Governance
Semantic Governance for Humans & AI: Apply centralized role-based access control (RBAC) and metric versioning across all user types, analysts, bots, and agents.
End-to-End Observability: Integrate with OpenTelemetry, data catalog tools, and metadata platforms to track query lineage, usage, and semantic accuracy in AI-driven workflows.
Auditability by Design: Ensure full traceability of every metric call, from BI dashboard to autonomous AI agent.
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 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.
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.
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.
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.
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.
