Snowflake Intelligence: Start Talking to Your Data

Why Conversational Data Access Changes the Equation

Conversational BI takes data democratization a step further by eliminating the need for dashboard design in complex scenarios. It makes information directly accessible to users and extends the analytical toolkit beyond what static visualizations offer. When you combine conversational data analytics with knowledge-based chatbots, you create new pathways for discovering insights and making information comprehensible to audiences both inside and outside of your organization.

The core advantage of using Snowflake Intelligence as your corporate AI and data service is consolidation and unification. You manage organizational data and conversational AI on one platform, exposing it where needed with consistent capabilities and predictable responses. This consolidates the user experience around data conversations. You eliminate issues with semantic and context translation, and you stop duplicating efforts across teams handling chatbots, data catalogs, semantics, and data visualizations.

Snowflake provides insights from both structured and unstructured data. However, some limitations exist. Features like video file textualization cannot be analyzed directly within Snowflake and require integration with external services.

Inside Snowflake Cortex

Snowflake Cortex delivers reliable responses to user requests with high accuracy rates. Its built-in Deep Research Agent for Analytics goes beyond simple retrieval to analyze data, investigate complex business questions, uncover trends, and explain causation, not just correlation.

Snowflake's Knowledge Extensions functionality enables you to enrich existing AI capabilities with custom-trained models and knowledge bases from various vendors or monetize your own knowledge assets. For organizations running client-facing AI services that interact with data, it is essential to consider costs and add custom AI services for evaluation.

How To Prepare Your Snowflake for Data Conversations

If you already use Snowflake or are considering it as the foundation for your AI data strategy, it adds minimal operational overhead while delivering high-quality results. Beyond the standard data architecture (medallion on data warehouse), AI architecture introduces requirements that were either unnecessary or absent in traditional and modern data stacks.

Creating A Semantic Model for Structured Data

This step provides information about your data to AI and BI analytics services. A basic semantic model explains the data model by describing its facts, dimensions, descriptions, fact additivity, granularity, table relationships, data types, and sample values. An advanced semantic model adds context, including a search index of data with value lists that help agents filter, classify, correct typos in requests, and identify irrelevant queries. A semantic model can also include a knowledge base that serves as a thesaurus and terminology guide for the agent, as well as verified requests and guardrails for desired and undesired model outputs.

Creating Agents That Orchestrate AI and Traditional Tools

Agents use orchestration rules to perform multi-step responses. For example, an agent accepts a user request, translates it into a context that a natural language query generator understands, generates, and executes a query, identifies a suitable visualization option, presents the data in a convenient format, and sends the generated report via email after conversing with the user. Despite its importance, this is a relatively small task that requires understanding context design.

Creating A Knowledge Base and Search Index for Unstructured Data

Use this for standalone knowledge retrieval of conversations, to enhance data analytics, or for both conversational and analytical contexts. This step may seem less important than the previous ones, but it drives the success or failure of your AI initiative. If you don't prepare and explain your data, you make your chatbots useless for the people who need them.

Maintaining Data Quality

While data quality management is a standard practice, your AI data team should pay particular attention to data shared through conversational AI. This data reaches consumers, not data professionals who build dashboards. All verifications and cleansing must happen before data exposure. Evaluate your data continuously using ML services. Quality of data is arguably the most important component of success for Data Ai project,

All these activities require proper design and planning to meet the needs of business users while ensuring efficient implementation and maintenance.

How To Reach Your Users

Let's examine the Snowflake Intelligence architecture, starting with the frontend. 

Table 1: Snowflake Intelligence Architecture Overview

Snowflake offers several options for conversational frontend implementation:

• Snowflake Intelligence UI, which integrates tightly with Snowflake Data Agents. If you want the full capabilities of Snowflake AI while keeping implementation simple, consider Snowflake Intelligence as your primary conversational service for data. This provides all Snowflake agent capabilities for end business users, including data visualization. It requires zero integration effort and comes with built-in Snowflake roles and permissions.
• Integration with custom applications or existing chat services (Teams, Microsoft 365 Copilot, etc.). This is the preferred option if your primary stack is Microsoft or if you've chosen Teams as your conversational service. This requires additional integration effort and doesn't support the visualizations that come with Snowflake Intelligence UI.
• Custom client integrated with Agentic API. This serves scenarios requiring extended custom capabilities not provided by standard services.
• MCP servers and direct use of the Cortex services API. This alternative option allows creating agents outside Snowflake while utilizing Cortex Search and semantic models.

Core Components

• Cortex Data Agent handles top-level logic for conversational tasks with data customers on Snowflake, including orchestration and multimodal data analytics. You can create multiple agents that serve different purposes with distinct toolsets.
• Cortex Analyst, Cortex Search, and Cortex Knowledge Extensions provide search and conversational capabilities for specific scopes, including structured data associated with semantic models, search indexes, RAG with staged files, and extended AI capabilities from knowledge extension vendors, which are performed on customer or shared data. Cortex Knowledge Extensions are pluggable components for agentic AI, enriching your system with new capabilities. You can add knowledge bases and models trained for specific purposes by different vendors.
• ML and non-AI functionality can be integrated into your agentic toolset through various Snowflake functions that perform tasks such as ML pipelines, notifications, data sharing, and triggering external services.
• Data layer stores analytical data in tables, vectorized unstructured data, and staged data for use with RAG patterns.

What This Means for Your Organization

Snowflake has built the components for an AI data platform that retrieves, transforms, enriches, cleans, explains, and prepares insights from data. Now, it offers sophisticated interactive conversation and data visualization capabilities that are deeply integrated into the platform, with low implementation complexity and high reliability.

While visualization capabilities may not match those specialized BI tools, they suffice for most conversational needs. Combined with high-quality responses, solid user experience, and simplicity, this positions Snowflake Intelligence as a significant contender in conversational BI and multimodal analytics. With this service, data and analytics teams can focus less on technology and more on the value of data.

Want to learn more? See how we can help you implement Snowflake Intelligence.