AI discovery scans your data and proposes metrics and semantic models. Define dimensions and measures properly. Build agents and skills with confidence—trust scores, lineage tracking, and user feedback measure accuracy so you know the answers are right.
Why it matters: AI that queries your data is only as good as the structure underneath. Auto-discovery gets you started. Semantic modeling gets it right. Trust scoring and feedback let users know when answers are accurate—and help you improve when they're not.
Understand your data, model it properly, and build agents your users can trust
Connect your data source and run an AI discovery scan. The system profiles tables, infers dimensions and measures, detects relationships, and proposes metrics and semantic models—so you understand your data before you build.
Review and refine the proposed models. Define dimensions (categorical, time), measures (sum, avg, count), and relationships. Proper modeling ensures AI queries return accurate, consistent answers aligned with your business definitions.
Create agents that query your semantic models in natural language. Deploy skills so users can ask anything—with answers backed by properly modeled data and transparent lineage.
Every answer gets a trust score, confidence breakdown, and lineage. Users rate accuracy. Track feedback over time, identify issue patterns, and improve your models—so users know they can rely on the results.
From discovery to semantic modeling to trustworthy AI agents
Auto-scan your data warehouse. AI profiles tables, infers dimensions and measures, detects relationships, and proposes metrics and semantic models—so you start with structure.
Define dimensions, measures, and relationships the right way. Proper semantic models ensure AI understands your business context and returns accurate, consistent answers.
Every AI answer gets a trust score, confidence breakdown, and lineage. Users rate accuracy. Track feedback, identify patterns, and improve—so users know they can rely on results.
Build agents that query your semantic models in natural language. Deploy skills backed by properly modeled data—with transparency and trust scores.
Define metric blocks with grain dimensions, time windows, and aggregations. Build collections that join related metrics for richer analysis.
Ask anything in plain English. Query metrics, slice by dimensions, get insights—powered by semantic models that understand your business.
Runs in your Snowflake environment. Create semantic views, Cortex agents, and aggregate tables—all in your own data cloud.
Link metrics to OKRs and track progress. Get insights on goal achievement with properly modeled, trustworthy data.
Import existing metric definitions from GitHub. YAML-based definitions with validation—complements AI discovery for hybrid workflows.
Start with AI discovery, model your data the right way, and build agents your users can trust.