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.
