Introduction
In today’s evolving data landscape, the gap between business users and data engineers remains one of the biggest barriers to true data democratization. Traditional BI dashboards, SQL queries, and static reports still demand technical expertise, creating dependency bottlenecks that slow down time-to-insight.
Snowflake Intelligence changes this dynamic. It is an AI-native capability designed to let anyone in the organization talk to data. By blending generative AI, governed data access, and context-aware intelligence, Snowflake conversational analytics transforms your enterprise data platform into an interactive assistant that can answer questions, generating SQL, summarize trends, and even suggest business optimizations, all in natural language.
So, What is Snowflake Intelligence?
Snowflake Intelligence is a new agentic experience, available through a dedicated portal (ai.snowflake.com), built to help business users interact directly and securely with organizational data.
When users ask a question, Snowflake Intelligence:
- Understands the prompt using Gen AI and Cortex
- Retrieves relevant data (structured, semi-structured, or unstructured)
- Dynamically generates and executes SQL queries
- Returns meaningful, contextualized insights instantly
All activity happens within Snowflake, ensuring compliance, governance and data security. In essence, when people ask what is Snowflake Intelligence, it’s the bridge between natural human curiosity and governed, AI-powered data exploration, forming the foundation of true Snowflake conversational analytics.
Architecture: How ‘Talk-to-Data’ Works
At the heart of Snowflake Intelligence are three modular Cortex components—specialized building blocks that handle different types of data and functions. Working together, they create the “Talk-to-Data” experience, turning natural language questions into precise, actionable insights in real time.
The core architecture comprises three modular Cortex components that together enable the “Talk-to-Data” experience:
| Component | Purpose | Data Type |
| Cortex Analyst | A fully managed, LLM-powered Snowflake Cortex feature that helps you build applications capable of reliably answering business questions from structured data. Business users can ask questions in natural language and receive direct answers without writing SQL. | Structured |
| Cortex Search | A fully managed, low-latency, hybrid (vector and keyword) search engine for unstructured data that enables high-quality “fuzzy” search and Retrieval Augmented Generation (RAG) applications. | Unstructured |
| Custom Tools / Procedures | An extensibility layer for APIs, stored procedures, or workflows (e.g., sending an email or triggering an action). | Hybrid |
These components are orchestrated under an Intelligence Agent, which acts as the system’s brain. It routes user prompts to the right service, generates SQL, interprets results, and maintains the conversational context.
Real-Time Example: Insurance Agent Prototype
An insurance intelligence agent was developed to showcase how Snowflake Intelligence can be applied to real-world use cases across structured and unstructured data.
Step 1—Ask the Question
“What are the various coverages provided by this insurance company?”
Snowflake Intelligence parses the question and maps it to the underlying schema through Cortex agents. It then routes the prompt to the relevant dataset. Since this query involves structured data, it directs it to the Cortex Analyst. From there, it dynamically generates an SQL query to extract coverage information from multiple tables, including customers, policies, claims, and payments.
Step 2—Interpret and Visualize
Within seconds, Snowflake displays tabular and visual results showing coverage types, policy counts, financial performance, and adoption rates. All of this is generated in real time.
Step 3—Continue the Conversation
“Summarize how the coverage types are performing and suggest how we can improve.”
Snowflake analyzes the aggregated metrics, identifies underperforming coverage segments, and recommends improvement strategies, such as focusing on less adopted coverage types or optimizing claims management.
Step 4—Automate and Act
To extend functionality, a custom tool (send_email()) was added as a stored procedure. The output from the analysis was automatically emailed to business users at their configured addresses. This allowed them to take immediate action based on the insights received.
Behind this seamless conversational experience lies a thoughtful technical design. Each interaction, query, and recommendation is powered by well-structured data models and configurations that make the system both intelligent and reliable.
Under the Hood: Data Modeling and Configuration
To bring the “Talk-to-Data” capability to life, Snowflake™ Intelligence relies on four core layers.
1.Structured Layer (Cortex Analyst)
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- Built on a semantic model defining relationships across 11 tables, including policies, customers, and claims.
- Relationships such as POLICY.CUSTOMER_ID = CUSTOMER.CUSTOMER_ID enable natural language queries to resolve relational joins automatically.
2. Unstructured Layer (Cortex Search)
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- Built on textual data such as transcripts, documents, or support tickets.
- Enables semantic search. For example, a user can ask “Show me support tickets mentioning delayed claims”, and Snowflake retrieves relevant snippets using embeddings.
3.Custom Tools
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- Implemented through stored procedures or external API hooks.
- Example: Sending automated recommendations through email or Slack based on analysis results.
4.Agent Orchestration
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- Manages prompt routing, LLM orchestration, and access control (RBAC).
- Supports models such as Claude and GPT for enhanced conversational accuracy.
Governance and Security
While the architecture enables agility and intelligence, governance ensures it all happens securely.
Unlike external conversational AI tools, Snowflake Intelligence operates natively within your governed Snowflake environment. This design ensures strong security and compliance at every step.
- No external LLM hosting: Eliminates risks of data exfiltration.
- Native security controls: Role-based access control (RBAC) and masking policies warrant users to access only authorized data.
- Comprehensive compliance: A complete audit trail of prompts, queries, and outputs simplifies regulatory reviews.
This makes Snowflake Intelligence especially suited for regulated industries such as insurance, banking, and healthcare, where trust and compliance are non-negotiable.
Business Impact: From Dashboards to Dialogue
| Traditional BI | Snowflake Intelligence |
| Predefined dashboards | Dynamic, conversational interface |
| SQL or visualization tools required | Natural language queries |
| Slow insight cycle | Real-time, contextual insights |
| Limited interactivity | Continuous, intelligent dialogue |
| Manual follow-ups | Automated actions through custom tools |
For example, instead of refreshing a traditional dashboard, a business analyst can simply ask:
“Show me the top-performing coverage types last quarter and how they compare with current policy renewals.”
Within seconds, Snowflake Intelligence returns numeric insights and actionable recommendations, creating a truly conversational analytics loop.
Integration Possibilities
The power of Snowflake Intelligence doesn’t stop within its own interface. Its modular design allows it to extend into the tools and systems your teams already use, making insights accessible anywhere work happens.
- Collaboration Tools (MS Teams/Slack): Embed Snowflake Agent endpoints directly into daily workflows to enable real-time “talk-to-data” functionality.
- Business Systems (APIs/REST): Expose Intelligence endpoints via standard APIs and REST services for easy integration with external applications.
- Advanced AI Workflows (Cortex + Snowflake ML): Combine multiple AI capabilities to create sophisticated solutions. For example, running sentiment analysis on customer support data before surfacing those insights conversationally.
The Future of Conversational Analytics
As these integrations deepen, analytics itself is undergoing a quiet transformation. With Snowflake Intelligence, the analytics paradigm is shifting:
- From data access → to data dialogue
- From queries → to conversations
- From dashboards → to recommendations
The convergence of AI, machine learning, and data engineering is redefining how organizations extract value from data. Together, these capabilities make self-service analytics more intelligent, contextual, and conversational than ever before.
Conclusion
Snowflake Intelligence marks a shift from viewing data as static assets to experiencing it as an active conversation partner.
By embedding intelligence, governance, and accessibility within a single native environment, it redefines how decisions are made, faster, safer, and with greater context.
The real takeaway? Snowflake conversational analytics is no longer a futuristic concept. It’s here, enabling organizations to turn every question into insight and each insight into action. In doing so, Snowflake Intelligence lays the foundation for a future where analytics become effortless, conversational, and deeply connected to everyday business outcomes.