AtScale provides a universal semantic layer designed to bridge business logic with data stacks, enabling consistent and governed metrics for BI tools, AI models, and autonomous systems. It serves as a foundation for scaling human analysis and agentic AI, creating a single source of semantic truth. The platform supports AI-ready semantic models for LLMs and AI agents, allowing natural language queries and ensuring consistency between dashboards and AI systems. It offers enterprise-ready AI agents with context-driven capabilities to prevent hallucinations and translate user questions into optimized queries. Performance optimization is achieved through smart query routing, caching, and cloud-efficient scaling. Streamlined governance is provided via centralized role-based access control and metric versioning, with end-to-end observability and auditability by design. AtScale unifies semantic modeling from various sources like dbt and Power BI, defining KPIs once in open-source SML with Git-based CI/CD. It supports hybrid modeling experiences with AI copilots and offers flexible, cloud-native deployment options with transparent consumption-based pricing.

Features & Benefits

• AI-Ready Semantic Models: Creates semantic scaffolding for LLMs and AI agents, enabling autonomous interpretation of business data and context-driven AI workflows.
• Performance Optimization: Delivers sub-second results through intelligent pushdown, aggregate awareness, caching, and engine-level optimization for modern data platforms.
• Streamlined Governance: Applies centralized role-based access control (RBAC) and metric versioning across all user types, with end-to-end observability and auditability.
• Unified Semantic Modeling: Ingests models from various sources (dbt, Power BI, LookML) into a single layer, defining governed metrics with Git workflows and CI/CD.