Snowflake Launches Intelligence Platform to Transform Enterprise AI Beyond Traditional RAG Systems

Snowflake builds new intelligence that goes beyond RAG to query and aggregate thousands of documents at once

Enterprise AI has a data problem. Despite billions in investment and increasingly capable language models, most organizations still can't answer basic analytical questions about their document repositories. The culprit isn't model quality but architecture: Traditional retrieval augmented generation (RAG) systems were designed to retrieve and summarize, not analyze and aggregate across large document sets. Snowflake is tackling this limitation head-on with a comprehensive platform strategy announced at its BUILD 2025 conference. The company unveiled Snowflake Intelligence, an enterprise intelligence agent platform designed to unify structured and unstructured data analysis, along with infrastructure improvements spanning data integration with Openflow, database consolidation with Snowflake Postgres and real-time analytics with interactive tables. The goal: Eliminate the data silos and architectural bottlenecks that prevent enterprises from operationalizing AI at scale. A key innovation is Agentic Document Analytics, a new capability within Snowflake Intelligence that can analyze thousands of documents simultaneously. This moves enterprises from basic lookups like "What is our password reset policy?" to complex analytical queries like "Show me a count of weekly mentions by product area in my customer support tickets for the last six months." The RAG bottleneck: Why sampling fails for analytics Traditional RAG systems work by embedding documents into vector representations, storing them in a vector database and retrieving the most semantically similar documents when a user asks a question. "For RAG to work, it requires that all of the answers that you are searching for already exist in some published way today," Jeff Hollan, head of Cortex AI Agents at Snowflake explained to VentureBeat during a press briefing. "The pattern I think about with RAG is it's like a librarian, you get a question and it tells you, 'This book has the answer on this specific page.'" However, this architecture fundamentally breaks when organizations need to perform aggregate analysis. If, for example, an enterprise has 100,000 reports and wants to identify all of the reports that talk about a specific business entity and sum up all the revenue discussed in those reports, that's a non-trivial task. "That's a much more complex thing than just traditional RAG," Hollan said. This limitation has typically forced enterprises to maintain separate analytics pipelines for structured data in data warehouses and unstructured data in vector databases or document stores. The result is data silos and governance challenges for enterprises. How Agentic Document Analytics works differently Snowflake's approach unifies structured and unstructured data analysis within its platform by treating documents as queryable data sources rather than retrieval targets. The system uses AI to extract, structure and index document content in ways that enable SQL-like analytical operations across thousands of documents. The capability leverages Snowflake's existing architecture. Cortex AISQL handles document parsing and extraction. Interactive Tables and Warehouses deliver sub-second query performance on large datasets. By processing documents within the same governed data platform that houses structured data, enterprises can join document insights with transactional data, customer records and other business information. "The value of AI, the power of AI, the productivity and disruptive potential of AI, is created and enabled by connecting with enterprise data," said Christian Kleinerman, EVP of product at Snowflake. The company's architecture keeps all data processing within its security boundary, addressing governance concerns that have slowed enterprise AI adoption. The system works with documents across multiple sources. These include PDFs in SharePoint, Slack conversations, Microsoft Teams data and Salesforce records through Snowflake's zero-copy integration capabilities. This eliminates the need to extract and move data into separate AI processing systems. Comparison with current market approaches The announcement positions Snowflake differently from both traditional data warehouse vendors and AI-native startups. Companies like Databricks have focused on bringing AI capabilities to lakehouses, but typically still rely on vector databases and traditional RAG patterns for unstructured data. OpenAI's Assistants API and Anthropic's Claude both offer document analysis, but are limited by context window sizes. Vector database providers like Pinecone and Weaviate have built businesses around RAG use cases but sometimes face challenges when customers need analytical queries rather than retrieval-based ones. These systems excel at finding relevant documents but cannot easily aggregate information across large document sets. Among the key high-value use cases that were previously difficult with RAG-only architectures that Snowflow highlights for its approach is customer support analysis. Instead of manually reviewing support tickets, organizations can query patterns across thousands of interactions. Questions like "What are the top 10 product issues mentioned in support tickets this quarter, broken down by customer segment?" become answerable in seconds. What this means for enterprise AI strategy For enterprises building AI strategies, Agentic Document Analytics represents a shift from the "search and retrieve" paradigm of RAG to a "query and analyze" paradigm more familiar from business intelligence tools. Rather than deploying separate vector databases and RAG systems for each use case, enterprises can consolidate document analytics into their existing data platform. This reduces infrastructure complexity while extending business intelligence practices to unstructured data. The capability also democratizes access. Making document analysis queryable through natural language means insights that previously required data science teams become available to business users. For enterprises looking to lead in AI, the competitive advantage comes not from having better language models, but from analyzing proprietary unstructured data at scale alongside structured business data. Organizations that can query their entire document corpus as easily as they query their data warehouse will gain insights competitors cannot easily replicate. "AI is a reality today," Kleinerman said. "We have lots of organizations already getting value out of AI, and if anyone is still waiting it out or sitting on the sidelines, our call to action is to start building now."

Snowflake Unveils New AI Tools to Help Enterprises Build and Deploy Agentic Apps Faster | AIM

AI data cloud company Snowflake announced a series of product enhancements to help organisations deploy enterprise-grade agentic AI applications faster and more securely. The company launched Snowflake Intelligence, now generally available to its global customer base of more than 12,000 organisations. The enterprise intelligence agent allows users to access and analyse data through natural language and connect structured, unstructured and semi-structured data from various sources within Snowflake's secure platform. "For more than a decade, Snowflake has served as a cornerstone of global enterprises' data strategies. Our next evolution is about bringing AI to this data, allowing every customer to unlock intelligence that is uniquely their own," Christian Kleinerman, EVP of product at Snowflake, said. "Our latest enhancements democratise the power of AI so every employee can make smarter and faster decisions." Over 1,000 customers, including Cisco, Toyota Motor Europe, TS Imagine and the USA Bobsled/Skeleton Team, have used Snowflake Intelligence in the past three months to deploy more than 15,000 AI agents across their businesses. Powered by AI models from partners like Anthropic, Snowflake Intelligence converts complex queries into conversational insights. The platform includes the Agent GPA (goal, plan, action) framework to improve the accuracy and trustworthiness of AI outputs. Snowflake also announced updates to Horizon Catalog and Openflow to help enterprises connect data across clouds and regions without vendor lock-in. Horizon Catalog now supports open APIs from Apache Polaris (Incubating) and Apache Iceberg REST Catalog, enabling unified governance and access management. Other updates include Interactive Tables and Warehouses (in private preview) for real-time data experiences, near real-time streaming analytics and integration with Oracle for continuous data streaming. Following its acquisition of Crunchy Data, Snowflake introduced Snowflake Postgres and open-sourced pg_lake, strengthening Postgres integration within its platform. Snowflake also unveiled new developer tools, including Cortex Code (in private preview), Cortex AISQL (now generally available), Workspaces with Git and VS Code integration, dbt Projects on Snowflake and Snowpark Connect for Apache Spark. These tools aim to help developers build, test, and deploy AI applications faster within a single, governed environment. Snowflake said the innovations will enable enterprises to unify data, governance, and AI development, accelerating the creation of trusted, scalable and agentic AI applications.

AI analytical agent caps a wave of new and enhanced Snowflake products - SiliconANGLE

AI analytical agent caps a wave of new and enhanced Snowflake products Snowflake Inc. today announced the general availability of Snowflake Intelligence, the centerpiece of the company's latest wave of artificial intelligence-driven products aimed at allowing employees across skill levels to derive insights from enterprise data. Announced in June at Snowflake Summit 2025, Snowflake Intelligence enables users to ask complex business questions in natural language, leveraging both structured and unstructured data from Snowflake and third-party data sources, such as Salesforce Inc.'s Data 360. Snowflake said the platform is already in use by more than 1,000 customers in the test phase and has been used to deploy more than 15,000 AI agents, a type of software that can interact with its environment, make decisions and perform tasks. Users can query data using a natural language interface without needing to write code. The product automatically interprets business semantics, generates and executes SQL queries. Intelligence integrates with Snowflake's Horizon catalog to ensure access control across clouds, regions and formats. Snowflake said its underlying model integrates AI services from partners such as Anthropic PBC and OpenAI LLC, and can synthesize multimodal data, such as logistics records and internal communications, to identify trends, root causes and recommend actions. Responses are supported by verified queries and semantic views, which are marked by a "green shield" icon indicating certified data sources were used. Intelligence is positioned as "a strategic component of how [organizations] understand what's happening," said Jeff Hollan, the company's head of Cortex AI Agents. "It's not just to get basic information but to understand why a trend is happening." To enhance reliability, Snowflake's AI researchers introduced a new evaluation framework called Agent Goal, Plan, Action that reportedly catches up to 95% of errors during testing. The company also claims that text-to-SQL performance is now up to three times faster than before. Hollan said agentic capabilities extend beyond answering queries to performing actions. "My agent understands things like how to integrate with my other systems," he said, demonstrating a scenario in which a company's logistics provider is automatically changed and a team is notified via Slack. Existing data governance rules are automatically enforced, and users are only shown data they are authorized to access, said Christian Kleinerman, executive vice president of product. "We focus every day on making sure Snowflake is trusted and that our customers can entrust us with their data," he said. The company is also using its Build conference this week to announce a set of developer-focused enhancements designed to support enterprise-grade AI development. They include the general availability of Cortex Agents, which allow developers to define custom data agents, and a Model Context Protocol Server, which facilitates secure communication between Snowflake and external AI tools. Developers can build and test AI pipelines within Snowflake using Dynamic Tables and Cortex AISQL. A forthcoming feature, AI Redact, will help identify and remove sensitive information from unstructured data. The company is also introducing Cortex Code, an AI assistant that integrates directly into the Snowflake interface to help with tasks such as query optimization and system navigation. Cortex Code helps users understand their Snowflake usage, optimize complex queries and fine-tune results to minimize costs. To support version control and collaboration, Workspaces now include Git and Visual Studio Code integrations. Snowflake is also extending support for open-source tools, such as data build tool, enabling teams to manage analytics workflows within the Snowflake environment. The new dbt Projects on Snowflake, now generally available, enables developers to use dbt to test, deploy and monitor their data transformations directly within their Snowflake environment. Snowflake is expanding its support for data lakehouses - which combine the features of a data lake and a data warehouse -- with updates to the Horizon Catalog and the Snowflake OpenFlow data integration service. These enable organizations to ingest, govern and share data across systems using open-source elements, such as Apache Iceberg and the Apache Polaris Catalog. The goal is to enable AI agents to securely access all enterprise data, regardless of format or location, Kleinerman said. "Organizations struggle with AI readiness due to fragmented governance and siloed data systems," he said. "This couldn't be more timely." Interactive Tables and Warehouses, now generally available, provide sub-second query response times to power real-time dashboards and applications. A new near-real-time streaming analytics feature is in private preview. "Think of [Interactive Tables and Warehouses] as being either for applications or dashboards that need very low latency," Kleinerman said. "At the end of the day, we want to provide subsecond analytics." In another significant step into the open-source ecosystem, Snowflake has also introduced a fully managed version of the PostgreSQL database engine, allowing organizations to run transactional workloads alongside analytical tasks on a single platform. Postgres developers can use a new extension called pg_lake to read and write directly to Iceberg tables from Postgres, eliminating the need for an extract, transform, load process. Snowflake developed pg_lake and has released it to open source. "We have a very long list of customers asking for access to the preview and interested in getting the Postgres capabilities," Kleinerman said. The pg_lake extension is for anyone who wants to turn Postgres into an interface to manage an open lakehouse." Kleinerman said that Snowflake has shortened its product release cycles and is moving faster than ever. "Many of the technologies that we're saying are [generally available] now were introduced for the first time six months ago or even less," he said. Snowflake plans to continue addressing adoption barriers, which often stem from fragmented data access and governance rather than the AI models themselves. The company aims to do that by embedding AI into its native data environment and enforcing existing governance rules across all new capabilities.

Snowflake Announces New Product Innovations To Accelerate The Development Of Enterprise-Grade Agentic AI Apps

Snowflake Intelligence is now generally available, equipping organisations to democratise data and AI across their business * New advancements to Snowflake Horizon Catalog and Snowflake Openflow enable enterprises to make all their data accessible for AI agents * New suite of developer tools to supercharge agentic AI, enabling developers to deploy AI apps faster and more reliably Sydney, Australia - November 5, 2025 - Snowflake, the AI Data Cloud company, today announced several product innovations to its platform, empowering organisations to easily deploy agentic AI at scale and deliver trusted and accurate insights. These enhancements include the general availability of Snowflake Intelligence, an enterprise intelligence agent that provides every user with the ability to answer complex questions in natural language and put insights at every employee's fingertips. Together with advancements to Snowflake Openflow and Snowflake Horizon Catalog, organisations can drive more robust data insights by connecting all of their data -- structured, unstructured, and semi-structured -- from disparate sources and catalogs to power agentic AI. This all takes place within Snowflake's secure, governed and interoperable environment - free of vendor lock-in. Snowflake also announced a suite of enhanced AI-native and collaboration tools enabling developers to build, test, and deploy enterprise-ready AI apps faster and safer while reducing overhead and total cost of ownership - all within a single governed platform. "For more than a decade, Snowflake has served as a cornerstone of global enterprises' data strategies. Our next evolution is about bringing AI to this data, allowing every customer to unlock intelligence that is uniquely their own," said Christian Kleinerman, EVP of Product at Snowflake. "Our latest enhancements to the Snowflake platform make this possible, democratising the power of AI so every employee can make smarter and faster decisions, fundamentally changing how our customers will innovate for years to come." Snowflake Intelligence: All Your Knowledge. One Trusted Enterprise Agent Snowflake Intelligence is now generally available to Snowflake's global customer base of more than 12,000 organisations. With a single question, Snowflake Intelligence can facilitate deep research and suggest solutions to previously difficult and time-consuming business problems -- allowing users to move beyond the "what" to the critical "why." Built for reliability and scale, it's enterprise-ready with trust, governance, and security at the forefront, allowing employees to confidently access and analyse data through natural language while helping ensure that confidential information stays confidential. This, in turn, reduces the guesswork in decision-making, fostering a new culture of data across the enterprise. In the past three months alone, more than 1,000 Snowflake customers - including Cisco, Toyota Motor Europe, TS Imagine and the USA Bobsled/Skeleton Team - have already leveraged Snowflake Intelligence to rapidly and easily deploy over 15,000 AI agents across their businesses¹. "Snowflake Intelligence has transformed our development timeline, reducing agent deployment from months to weeks. This has fundamentally shifted our team's focus from writing code, to prioritising what truly drives value: building rich business context and robust semantic models," said Thierry Martin, Head of Data and AI at Toyota Motor Europe. "The result is a significant competitive advantage -- we're bringing secure, compliant data solutions to market faster, while eliminating data movement risks." Powered by industry-leading AI models from providers like Anthropic, Snowflake Intelligence is transforming complex queries into conversational insights, ultimately democratising data and AI access across the enterprise. New innovations from Snowflake's AI Research Team make Snowflake Intelligence up to three times faster on text-to-SQL queries, delivering real-time answers with the same trusted accuracy. To increase the trustworthiness and accuracy of responses, the team also pioneered a novel evaluation method coined the Agent GPA (Goal, Plan, Action) framework that catches up to 95 per cent of errors when tested on standard datasets, achieving near-human levels of error detection. Snowflake Delivers the Enterprise Lakehouse with Enhanced Open Data Access and Flexibility for Agentic AI Snowflake has also announced advancements to Snowflake Horizon Catalog and Snowflake Openflow (now generally available) to enable enterprises to easily connect all of their data from disparate sources and catalogs to fuel more robust, accurate and trusted AI-driven insights. New innovations to Horizon Catalog provide context for AI and a unified security and governance framework that secures and connects data across every region, cloud, and format -- all interoperable and without vendor lock-in. In turn, Openflow allows enterprise users to securely automate data integration and ingestion from virtually any source, making it easier to keep data centralised across the enterprise lakehouse. Snowflake also unveiled the following advancements to make data more accessible for AI agents (such as Snowflake Intelligence) to drive value -- all with consistent security and governance: * By bringing open APIs from Apache Polaris™ (Incubating)2 and Apache Iceberg™ REST Catalog3 directly into Horizon Catalog, Snowflake now provides customers with an enterprise lakehouse that centralises governance, security, and interoperable access management across their data in open table formats. * With Interactive Tables and Warehouses (generally available soon) Snowflake is redefining how enterprises can build and power AI agents and apps by helping organisations turn data into immediate insights and near real-time experiences. * With near real-time streaming analytics (private preview soon), Snowflake is enabling organisations to act on live data within seconds, using the familiar tools and secure platform they already trust. Customers can now combine live data with historical context to power mission-critical use cases like fraud detection, personalisation, recommendations, observability, and IoT monitoring. * Snowflake is expanding integration options through its partnership with Oracle (now in private preview), enabling customers to harness near real-time change data capture built on Openflow to continuously stream transactional updates into the Snowflake AI Data Cloud. * Following Snowflake's recent acquisition of Crunchy Data, the company has introduced Snowflake Postgres (public preview soon), a fully-managed service that brings the world's most popular database onto the Snowflake platform. Snowflake is also open sourcing pg_lake (now generally available), a set of Postgres extensions designed to help developers and data engineers integrate Postgres with a powerful lakehouse system. * Snowflake is also enhancing data resilience with Business Continuity and Disaster Recovery (now in public preview) for managed Iceberg tables, further safeguarding enterprises' critical data across the entire enterprise lakehouse. Snowflake Unveils New Developer Tools to Supercharge Enterprise-Grade Agentic AI Development Snowflake also unveiled a suite of new developer tools designed to help organisations rapidly build, test and deploy cutting-edge, production-ready AI apps faster and more securely. * Developers can now streamline their data workflows with Cortex Code (in private preview), a refreshed AI assistant within the Snowflake UI that lets users interact with their entire Snowflake environment using natural language. Cortex Code helps users easily understand their Snowflake usage, optimise complex queries, and fine-tune their results to maximise cost savings. * With enhancements to Snowflake Cortex AISQL (now generally available), developers can build scalable AI pipelines within Snowflake Dynamic Tables (now generally available) to create AI-inference pipelines through a simple declarative SQL query. Leveraging AI Redact (in public preview soon) within Cortex AISQL, users can scale more confidently with the ability to detect and redact sensitive data from unstructured data, allowing them to ready their multimodal dataset for AI while maintaining security and privacy. * Snowflake's centralised development environment Workspaces (now generally available) eliminates siloed data work and boosts collaboration, providing a unified editor for creating, organising, and managing code across multiple file types. Workspaces is enhanced with direct Git Integration (now generally available), providing developers a seamless way to review version control, and VS Code Integration (now generally available), allowing users to work from their preferred Integrated Development Environment (IDE) and share code with the rest of their team. * With dbt Projects on Snowflake (now generally available), enterprises can build, test, deploy, and monitor their dbt projects directly within their Snowflake environment -- empowering engineers to focus on delivering insights rather than maintaining various tools and infrastructure. Snowflake is also helping organisations further accelerate developer productivity by running existing Apache Spark™4 code on Snowflake's secure engine with Snowpark Connect for Apache Spark (now generally available). Learn More: * Double click into how Snowflake Intelligence is democratising access to data with agentic AI in this blog post. * Check out all the innovations and announcements coming out of BUILD 2025 on Snowflake's Newsroom. ¹ As of October 24, 2025. 2 Polaris Catalog, a vendor-neutral, open catalog implementation for Apache Iceberg -- the open standard of choice for implementing data lakehouses, data lakes, and other modern architectures. 3 Iceberg defines a REST-based Catalog API for managing table metadata and performing catalog operations. The REST catalog protocol is a common API (using the OpenAPI spec) for interacting with any Iceberg catalog. 4 "Apache Spark" is a registered trademark or trademark of the Apache Software Foundation in the United States and/or other countries. About Snowflake Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 12,000 customers around the globe, including hundreds of the world's largest companies, use Snowflake's AI Data Cloud to build, use and share data, applications and AI. With Snowflake, data and AI are transformative for everyone.

Snowflake's bold AI bet: Turn AI agents into your next colleagues

SAP partnership and data sovereignty shape Snowflake's intelligent enterprise vision Snowflake's annual BUILD 2025 wasn't just another product showcase. The cloud data expert now wants to make AI feel practical, tangible, and yes, accountable. And it wants to unlock the potential for AI at work through something called as the Snowflake Intelligence - the company's self-described "trusted enterprise agent." Think of it less as a chatbot and more as a sentient analyst that can parse, reason, and respond to complex business questions in plain English, all within the safety of Snowflake's AI Data Cloud. "Our latest enhancements to the Snowflake platform make this possible, democratizing the power of AI so every employee can make smarter and faster decisions, fundamentally changing how our customers will innovate for years to come," says Christian Kleinerman, EVP of Product at Snowflake. Over 1,000 customers have already deployed 15,000 AI agents in the past quarter alone, says Snowflake. Toyota Motor Europe and Cisco, among others, report major cuts in development time, according to Snowflake. One Toyota data head said Snowflake Intelligence let them "shift focus from writing code to building rich business context" - a subtle but profound change that reframes AI as more like a colleague, not just an inanimate codebase. What makes Snowflake Intelligence distinctive is its blend of speed and self-regulation. Powered by large models from Anthropic and assessed with an "Agent GPA" (Goal, Plan, Action) framework, the platform claims to detect up to 95% of reasoning errors. That kind of self-checking loop nudges AI closer to what Kleinerman calls "near-human" decision accuracy. Also read: Snowflake's new AI agents aim to democratize data analytics: Here's how Kleinerman sees this shift - from static tools to autonomous collaborators - as a new computing era. "By 2026, one interesting way AI will be deployed in the enterprise is AI agents becoming integral members of the workforce... Organizations will onboard AI agents much like new employees - giving them access to contextual documents, letting them observe workflows, assigning tasks, and providing feedback to help them learn and improve." He even envisions "manager agents" supervising others - a self-improving AI workforce nested inside corporate ecosystems. It's an audacious reimagining of enterprise software, one that stretches Snowflake's identity from a data warehouse into a digital organism that learns. That ambition requires serious developer muscle. Enter Cortex Code, a conversational assistant that lives inside the Snowflake UI, letting developers debug, query, and optimize through natural language. With Cortex AISQL and Dynamic Tables, teams can spin up inference pipelines with a few lines of SQL, while AI Redact ensures sensitive data never leaks into training sets. Developers can now code and version-control directly inside Snowflake Workspaces - Git and VS Code included - while staying within the guardrails of enterprise governance. Kleinerman calls it "reducing overhead and total cost of ownership - all within a single governed platform." Also read: NVIDIA's Kari Briski believes open models will define the next era of AI There's also a quiet but deliberate push toward open standards. Apache Iceberg, Polaris Catalog, and cross-cloud interoperability. In the AI arms race, flexibility is Snowflake's idea of security. Speaking on security, Kleinerman acknowledges a tectonic shift in how nations and corporations view control. "Sovereign AI clouds are a natural evolution of digital sovereignty - where organizations want more control over where their data resides, how it's protected, and how AI models are trained and deployed." He points out that Snowflake's early investments in regional infrastructure and partnerships now let customers meet "evolving standards around sovereign AI clouds, without compromising on performance, scale, or security." His mantra is simple. "Planning for change, rather than reacting to it, is the only way to be equipped to navigate disruption." One of BUILD's headline reveals was Snowflake's deepened alliance with SAP. The new SAP Snowflake solution extension ties Snowflake's AI Data Cloud directly into SAP's Business Data Cloud, marrying SAP's semantically rich data with Snowflake's compute and AI muscle. "By tightly integrating SAP and Snowflake, we're making it simple for enterprises to connect their critical business data... with the power of seamless AI app and data agent development at scale in Snowflake," Kleinerman said. The result? Zero-copy data sharing, unified governance, and a powerful bridge between business context and AI execution - a missing piece in the puzzle of enterprise-scale intelligence. If BUILD 2025 was about enabling, 2026 will be about embedding - AI agents that coexist with human teams, governed by sovereignty, powered by openness, and learning from everything they touch. Kleinerman's final words captured both pragmatism and inevitability, "Sovereign demands will always shift, and in today's fast-changing AI landscape, flexibility is the new security."