The data company Snowflake has launched Snowflake Intelligence, an enterprise AI platform designed to overcome the limitations of current systems. Sean Michael Kerner reports for VentureBeat that a key feature, Agentic Document Analytics, allows businesses to query and aggregate information from thousands of documents simultaneously. This capability moves beyond the simple retrieval and summarization offered by most existing Retrieval Augmented Generation (RAG) systems.
Traditional RAG architecture struggles with analytical tasks that require synthesizing information from large document sets. Jeff Hollan, a manager at Snowflake, explained that RAG is like a librarian who can find an answer on a specific page but cannot analyze the content of the entire library. This often forces companies to maintain separate systems for structured business data and unstructured documents, creating data silos.
Snowflake’s approach treats documents as queryable data sources. The system uses AI to extract and structure content, enabling complex analysis. This allows companies to combine insights from documents like PDFs or customer support tickets with structured data such as sales records.
For example, a business could ask for a count of weekly product mentions in support tickets over the last six months, a query that is difficult for standard RAG systems. This shifts the paradigm from simply searching for existing answers to actively analyzing unstructured data to find new insights. By integrating this function into its core platform, Snowflake aims to help companies make complex document analysis more accessible to regular business users.
