Snowflake's New Intelligence: What It Means for Data Aggregation

Snowflake's AI Gambit: From Data Warehouse to Document Detective

Snowflake's BUILD 2025 conference saw the unveiling of Snowflake Intelligence, an enterprise intelligence agent platform. The core promise? To unify structured and unstructured data analysis. The headliner is Agentic Document Analytics, capable of analyzing thousands of documents simultaneously.

This is positioned as a leap beyond traditional Retrieval Augmented Generation (RAG) systems. Jeff Hollan, head of Cortex AI Agents at Snowflake, dismissed RAG as merely a "librarian," capable only of pointing to specific pages. Snowflake, by contrast, aims to treat documents as queryable data sources, using AI to extract, structure, and index content for SQL-like analytical operations. Think of it as turning your document repository into a giant, searchable spreadsheet.

The claim is ambitious. Traditional RAG systems, as well as solutions from Databricks (with their vector databases) and even OpenAI's Assistants API, struggle with analytical queries across large document sets. They're designed for retrieval and summarization, not aggregation and complex analysis. Snowflake's selling point is its ability to execute queries like "Show me a count of weekly mentions by product area in my customer support tickets for the last six months."

Beyond the Hype: Is Snowflake Reinventing the Wheel?

Christian Kleinerman, EVP of product at Snowflake, rightly pointed out that the real value of AI lies in its connection to enterprise data. The problem? Traditional RAG often requires separate analytics pipelines for structured and unstructured data, creating data silos and governance headaches. Snowflake's unified approach aims to solve this.

But is it truly novel? Several players are already vying for dominance in the unstructured data analytics space. OpenAI's Assistants API and Anthropic's Claude offer document analysis, albeit constrained by context window sizes. Vector database providers like Pinecone and Weaviate are pushing the boundaries of what's possible with unstructured data. The question is, does Snowflake's approach offer a significant advantage, or is it simply another iteration in a rapidly evolving landscape?

One key differentiator appears to be Snowflake's focus on SQL-like analytical operations. Cortex AISQL handles document parsing and extraction, feeding the data into Snowflake's existing infrastructure. This allows users to leverage their existing SQL skills and tools to analyze unstructured data. Interactive Tables and Warehouses further promise sub-second query performance on large datasets within Snowflake. (This is the part of the announcement that I find genuinely interesting, the speed claim.)

The system reportedly works with documents across multiple sources, including PDFs in SharePoint, Slack conversations, Microsoft Teams data, and Salesforce records. This is crucial. Data silos are the bane of any data analyst's existence. The ability to seamlessly analyze data across these disparate sources could be a game-changer.

But here's the rub. The announcement lacks concrete performance benchmarks. Sub-second query performance is a vague claim. What's the dataset size? What's the query complexity? Without these details, it's impossible to assess the true potential of Snowflake Intelligence. And this is the part of the report that I find genuinely puzzling...

The BTIG price target increase—from $276 to $312—following the announcement suggests that Wall Street is buying into the vision. (The acquisition cost was substantial (reported at $2.1 billion).) But price targets are often based on sentiment and future projections, not necessarily on concrete data. Time will tell if Snowflake can deliver on its promises. Here Are Wednesday's Top Wall Street Analyst Research Calls: AT&T, Beyond Meat, Carvana, Fortinet, Snowflake, Waste Managment and More - 24/7 Wall St.

A Solution in Search of a Problem?