The Missing Link: Using Semantic Layers to Enhance Cortex Analyst Accuracy

Natural language querying (NLQ) is revolutionizing how organizations access data, with Snowflake Cortex Analyst leading the way. Business users can ask questions like “What were our top-selling products last quarter?” and receive SQL-generated answers instantly. But despite Cortex Analyst’s impressive 90%+ SQL accuracy rate, many organizations still struggle with trust, consistency, and business alignment.

Why? Because technical accuracy isn’t the same as business accuracy.

That’s where the AtScale semantic layer makes all the difference. By embedding business logic, structure, and governance directly into the Cortex experience, AtScale turns healthy skepticism into reliable self-service analytics — empowering business users to ask questions and trust the answers.

Why AI Alone Isn’t Enough

Even the most advanced AI models struggle when they lack context. Without a clear understanding of business logic, metric definitions, and governance rules, Cortex Analyst may return technically valid SQL — but the answers can still be wrong in practice.

Here’s where things break down:

• Misinterpreted terms: AI doesn’t always understand that “forecast accuracy” or “active users” might have company-specific definitions.
• Complex relationships: Without a semantic model, Cortex can struggle with multi-table joins and complex schemas.
• Inconsistent metrics: Answers from Cortex may not match what users see in Tableau, Power BI, or Excel.
• Governance blind spots: AI could surface sensitive fields or invent non-existent metrics.

The result? Users lose confidence fast, even when the technology is technically “working.”

How AtScale Bridges the Gap

AtScale solves these problems by inserting a powerful, governed semantic layer between Cortex Analyst and your Snowflake data. This layer translates business concepts into SQL-friendly logic, ensures consistency across tools, and protects sensitive data — all while improving NLQ reliability.

1. Boosted Accuracy with Real Business Logic

AtScale’s semantic layer defines KPIs, metrics, and relationships clearly, so Cortex Analyst doesn’t have to guess. Instead of trying to reverse-engineer how your company defines “churn rate” or “bookings,” AtScale tells it directly.

• Business terms are translated into standardized SQL expressions.
• Key metrics are consistent with what’s already used in dashboards and reports.
• The semantic model guides Cortex to the right fields, tables, and logic.

In benchmark testing, Cortex Analyst reached 100% accuracy across AtScale’s NLQ Benchmark — up from an industry average of 54% with semantic context and just 16% with raw SQL access.

2. Consistent Answers Across Every Tool

Trust builds when the numbers match. With AtScale, the results you get in Cortex Analyst mirror what you already see in Tableau, Power BI, Excel, or any other BI platform.

• No more conflicting reports between teams.
• No more duplicate metric definitions scattered across tools.
• Everyone — from executives to analysts — works off the same logic and language.

This consistency is key to driving adoption and reducing second-guessing.

3. Simpler Query Generation

With AtScale, Cortex Analyst doesn’t need to write complicated SQL with joins, filters, or aggregations. Instead, it just queries logical tables exposed by the semantic layer.

AtScale handles the heavy lifting behind the scenes:

• Translates basic AI queries into optimized, multi-table SQL.
• Applies performance-enhancing features like intelligent aggregation.
• Ensures queries are fast, efficient, and easy to troubleshoot.

The result? AI-generated insights that are not only accurate but also lightning-fast.

4. Built-in Governance and Guardrails

AI should never guess when it comes to data security or compliance. AtScale enforces clear boundaries:

• Row-level and object-level security ensure that users only access data they’re allowed to see.
• AI cannot invent or hallucinate metrics that haven’t been defined in the model.
• If a user’s question falls outside the defined semantic scope, AtScale prompts with guidance rather than generating a misleading result.

Why Business Users Trust AtScale + Cortex

Many organizations are discovering that the path to trusted AI analytics isn’t about better prompts — it’s about better context. With AtScale, business users no longer need to know SQL or question whether AI is “making things up.”

Instead, they gain:

• Confidence in the numbers, because every metric follows a defined standard.
• Clarity on how answers are generated, with visibility into the SQL logic behind results.
• Consistency across platforms, so dashboards and AI outputs always align.
• Control over data access, with governance built into every layer.
  

Real-World Results

Organizations using AtScale and Cortex Analyst together are already seeing meaningful improvements:

• Fewer hours spent manually validating AI-generated SQL.
• Faster time to insight, especially for non-technical users.
• Three times higher accuracy in NLQ responses when using a semantic layer.
• Cross-platform consistency, eliminating confusion between dashboards and AI outputs.

According to Snowflake Senior Product Manager Abhinav Vadrevu,

“By combining Cortex Analyst with AtScale’s semantic layer, users can get correct answers in seconds.”

Best Practices to Build Trust in AI Analytics

Implementing AI tools is only the first step. Here are some proven practices to help your team move from skepticism to full-scale adoption:

Start with a Strong Semantic Foundation

Begin by implementing AtScale’s semantic layer to define your business terms, KPIs, and relationships. The clearer the definitions, the more accurate the AI responses will be.

Demonstrate Cross-Tool Consistency

Show users how Cortex outputs match what they already see in Tableau, Power BI, or Excel. Trust builds quickly when numbers align across tools.

Expand Gradually

Start with high-confidence, low-risk use cases. Common metrics like revenue, customer count, or churn are great entry points. As trust grows, expand into more complex scenarios.

Be Transparent

Allow users to see how their natural language questions are interpreted. With AtScale, you can show the exact metric used, which fosters understanding and buy-in.

Ready to Scale AI-Powered Analytics?

Snowflake Cortex Analyst is a powerful leap forward in natural language querying — but it’s the AtScale semantic layer that turns promise into practice. With clear definitions, consistent results, and built-in governance, your teams can finally trust what AI has to say.

As of April 2025, the full AtScale and Cortex Analyst integration is production-ready. Now’s the time to see it in action, align your business teams, and unlock truly self-service, AI-powered insights.

Want to learn more? Watch the integration in action or connect with us to explore a pilot.