Are you using Cortex Analyst in your Snowflake environment but falling short of the accurate, consistent results you expected? You’re not alone. While Cortex Analyst delivers powerful text-to-SQL capabilities, many organizations find it challenging to get reliable insights from natural language queries alone.
The good news? The AtScale semantic layer platform now integrates directly with Snowflake Cortex Analyst — a February 2025 announcement that marks a major leap forward in solving these issues.
1. Implement The AtScale Semantic Layer
This is the single most impactful change you can make. With the AtScale semantic layer, Cortex Analyst can generate SQL queries based on trusted, governed business logic. Announced on February 19, 2025, the integration bridges the gap between user intent and complex underlying data structures.
As Abhinav Vadrevu, Senior Product Manager at Snowflake, put it: “Our integration with AtScale improves the quality of the semantic layer that sits between the user’s query and the underlying data. This ultimately leads to significant improvements in text-to-SQL quality.”
The AtScale semantic layer doesn’t just help interpret queries — it ensures those queries reflect business logic while optimizing how Snowflake handles the workload.
2. Leverage Business-Defined Metrics
AtScale’s semantic models define metrics in clear business terms, giving Cortex Analyst a reliable reference point when translating natural language queries. This eliminates guesswork, reduces ambiguity, and ensures consistent answers.
Better yet, those same definitions power BI tools like Tableau, Power BI, and Excel — so teams get the same numbers, regardless of whether they’re asking a question in Cortex Analyst or building a dashboard in their BI platform of choice.
3. Optimize Query Performance With Simplified SQL
Cortex Analyst doesn’t need to build complex queries anymore. Thanks to the AtScale semantic layer, it issues straightforward selection queries against logical tables.
AtScale’s platform then dynamically transforms those inputs into optimized multi-table joins and aggregations. The result? High-speed performance and a lighter load on your Snowflake compute resources.
4. Apply Enterprise-Grade Governance To AI-Generated Insights
One of the biggest risks in using generative AI for data access is hallucinated insights — answers that look convincing but are flat-out wrong. AtScale mitigates this risk by restricting Cortex Analyst to only what’s defined in the semantic layer.
If a user asks a question beyond the model’s scope, Cortex Analyst prompts them to rephrase instead of fabricating an answer. Additional safeguards, including object- and row-level security, ensure that only approved data is accessible through AI queries.
5. Enable Seamless Integration Across Platforms
For teams already using the AtScale semantic layer, there’s no need to start from scratch. Semantic models built for traditional BI workflows now translate directly into the Cortex Analyst format.
That means a single model serves multiple use cases — BI dashboards, self-serve analytics, and natural language queries — all powered by a consistent layer of logic. Any changes made in AtScale are instantly reflected in Cortex Analyst.
What This Means For Your Organization
The results speak for themselves. Joint testing between AtScale and Snowflake showed:
- 100% accuracy across AtScale’s NLQ Benchmark
- A dramatic reduction in time spent manually validating AI-generated SQL
- A consistent source of truth across all analytics platforms
- Stronger governance and control over how data is accessed
By adopting the AtScale semantic layer alongside Cortex Analyst, you can transform natural language querying from a novelty into a reliable part of your enterprise analytics stack.
Check out this interactive demo to learn how to deploy AtScale from your Snowflake account. Or get in touch to learn more.
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