Natural language querying (NLQ) has the potential to make data accessible to everyone — not just analysts and data engineers. But in practice, tools like Snowflake Cortex Analyst often deliver inconsistent or inaccurate results. That inconsistency erodes trust, slows adoption, and forces users back into manual SQL verification.

The good news is that the AtScale semantic layer platform integrates with Cortex Analyst to provide the required structure, context, and governance to deliver NLQ results you expect: accurate, reliable, and trustworthy.

Where Cortex Analyst Falls Short

Despite its strengths, Cortex Analyst still faces common challenges:

• Struggles with semi-structured data like ARRAY, VARIANT, and OBJECT
  
• Misinterprets low-cardinality categorical fields
  
• Errors when handling complex JOIN logic
  
• Inconsistent results depending on how questions are phrased
  

These gaps can lead to false positives or incorrect outputs — even when the SQL it generates is syntactically valid.

AtScale: Turning Natural Language into Trusted Insights

AtScale addresses these NLQ weaknesses by inserting a business-aware semantic layer between Cortex and Snowflake.

1. Better Accuracy Through Business Context

With AtScale’s semantic models in place:

• Cortex Analyst has a clear understanding of business logic and KPI definitions
  
• Ambiguous language is mapped to precise SQL logic
  

In a joint evaluation, AtScale + Cortex Analyst achieved 100% accuracy against AtScale’s NLQ Benchmark. ‘

2. Consistency Across Every Tool

With AtScale as your single source of truth:

• Natural language queries return the same results as those from Tableau, Power BI, or Excel
  
• Business users gain confidence in answers, regardless of how they access data
  

3. Simpler Query Generation for Cortex Analyst

AtScale makes it easier for Cortex to succeed:

• Cortex generates simple selection queries
  
• AtScale’s engine transforms those into optimized, multi-table SQL queries
  
• Users get accurate results without needing to manually define joins or filters
  

4. Guardrails and Governance Built-In

AtScale helps prevent AI overreach:

• Object- and row-level security protects sensitive data
  
• AI can only query within the defined semantic model
  
• If a question falls outside the scope, Cortex prompts for clarification instead of guessing
  

The Business Impact

Snowflake Senior Product Manager Abhinav Vadrevu told BusinessWire,

“Our integration with AtScale improves the quality of the semantic layer… Users can get correct answers in seconds.”

Organizations implementing AtScale with Cortex Analyst report:

• Reduced SQL validation overhead
  
• Faster time to insight for business users
  
• Improved trust in AI-generated outputs
  
• Metric consistency across all analytics tools
  

Building Trust with a Strong Foundation

To get the most from Cortex Analyst + AtScale:

1. Deploy AtScale’s semantic layer (available as a Snowflake Native App)
   
2. Define clear, governed business metrics in your semantic model
   
3. Educate users on effective natural language queries
   
4. Monitor usage patterns and continuously refine your model
   

Unlock the Power of Your NLQ

NLQ shouldn’t be a black box. With AtScale, it becomes a transparent, governed, and accurate way for users to get answers — without writing SQL.

As of April 2025, AtScale’s full integration with Snowflake Cortex Analyst is available and production-ready. If your goal is trusted, business-aligned AI insights, now’s the time to put AtScale’s semantic layer to work. Request a demo to watch AtScale and Cortex Analyst in action. Or reach out for more information.