One layer, five components
Five components that get AI to the right number, prove it, and keep it governed. Any AI, any compute layer, one definition underneath all of it.
The promise of AI is gated by two things: accuracy and efficiency
Ask a model for a number and it writes SQL from whatever it can see. Ask again tomorrow and it writes different SQL.
Every attempt is a guess, and every guess is another scan of your warehouse. Wrong answers and a rising bill, from the same cause.
AtScale closes both. Ava resolves the prompt to the right metric. ACE computes that metric in your warehouse and keeps the aggregate, so the next question costs less than the last one did. Canary scores the answers against known-correct results. Each pass is more accurate and cheaper than the pass before it.
If it isn’t AtScale, it’s just a guess.
One hundred percent accuracy is reachable, and it takes a ratchet. Every job here raises accuracy and lowers the compute it takes to get there.
Snowflake
Databricks
Google BigQuery
Microsoft Fabric
Data Warehouse
Ontologies Catalog
Spark
Ontologies
Data Catalogs
Metadata
Claude
Cursor
Chat GPT
Copilot
Codex
Power BI
Tableau
Excel
01
Turns metrics into SQL. Computes in place
02
Build and maintain semantic models using AI.
New
03
Navigate prompts to one metric. Ask when unsure.
New
04
Inherited access control. Enforced once.
New
05
Scores answers against a gold standard.
AI
BI
Semantic model
Prompt
Clarify
Answer
Serve
The AI Computation Engine. Computes metrics and dimensions in your warehouse, then serves the same number to everything that asks.
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Reads a question in plain language and resolves it to one metric definition before anything runs.
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Builds and maintains the semantic model with AI, so definitions stop drifting.
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Coming
Tests answers against a gold standard and shows you which ones failed.
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Coming
Warehouse permissions carry through, enforced in one place. Definitions ship like code: versioned, reviewed, promoted, rolled back when a change breaks something.
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
Surface bottlenecks, shortages, and pricing volatility across suppliers and regions
The full platform, on any warehouse. Snowflake, Databricks, BigQuery, Redshift, Azure.
Runs natively inside Snowflake, and it’s the engine inside Semantic Views. Links out to the sub-site.
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