AI Semantic Layer Workbench dbt / Cube Spec Standard

Interactive Model & Metric Graph

Raw Schema Table Semantic Metric
Drag nodes to re-arrange • Hover/Click to inspect metadata

Natural Language AI Prompt Testbench

Simulated LLM Agent

Click a scenario prompt or type your own question to test SQL generation context accuracy:

Context Accuracy Score
98% High Reliability

Semantic filters applied properly

Metric Drift Prevented
100% Zero Fanout

Canonical formula enforced

Agent SQL Translation Comparison

Live SQL Diff
Raw Warehouse Agent SQL ⚠️ Missing Filters / Drift Risk 
-- SELECT region, SUM(gross_amount_cents)...
Semantic-Aware Agent SQL ✓ Canonical Business Logic 
-- SELECT raw_users.region AS region, SUM(...

How the AI Semantic Layer Enforces Query Truth

Read the explanation

Natural language prompts sent directly to raw database schemas force artificial intelligence agents to guess business rules, causing hallucinations and metric drift. The workbench binds physical tables to canonical semantic nodes, encoding status requirements, joins, and exclusions directly into reusable graph definitions. Selecting presets or executing prompts forces the model to assemble queries strictly using validated semantic formulas, locking accuracy to ninety-eight percent and preventing fanout.

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