Unlocking the Value of Unstructured Data with Snowflake Document AI

By James Power (Business Analyst, Advisory Services) and Dave Luttrell (Principal AI Consultant)

As organisations continue to explore the potential of artificial intelligence, one persistent obstacle remains: how to turn large volumes of unstructured data – particularly in documents – intro structured, usable information. From operations and compliance to customer service, documents remain foundational to enterprise activity.

Yet, the work of extracting meaning from them has too often relied on slow, error-prone manual processes or outdated legacy scanning technology, disconnected from broader data strategies. In this context, recent advancements in AI, particularly within the Snowflake platform, offer a compelling path forward. 

Snowflake has evolved well beyond its roots as a cloud data warehouse. Today, it offers a suite of AI-driven services embedded directly within the platform. Among these is Snowflake Copilot, an AI-assistant that allows users to query data using natural language, bypassing the need for traditional coding.  

Also included is Snowflake Cortex, which provides direct access to cutting-edge large language models from Anthropic, Meta, Google, and Snowflake itself. Snowflake’s own enterprise-grade model, Arctic, is designed for secure, scalable use – available natively within the platform, and accessible directly through SQL. This seamless integration eliminates the friction typically associated with bringing AI into existing data systems. 

Among these capabilities, Document AI stands out as one of the most immediately practical tools for enterprise use, with transformative potential for organisations looking to automate and scale how they handle unstructured information. Built on Snowflake’s multimodal model, Arctic-TILT, Document AI is engineered to solve a familiar and costly problem: converting the contents of forms, contracts, reports, and other documents into structured data – quickly, accurately, and without manual intervention.  

Importantly, no prior AI expertise is required. With as few as ten sample documents, teams can train highly accurate extraction models using an intuitive, browser-based interface. Once deployed, these models can be embedded into automated workflows that continuously extract and structure data as new documents arrive. Outputs are returned in standardised formats, ready to be queried and integrated into the broader Snowflake environment. 

In a current client engagement, we are solutioning an initiative to unify fragmented records across several internal systems. While structured data sources are relatively straightforward to process, a significant portion of important information resides in more than twenty document types – previously only accessible through manual review. The challenge is not only technical but strategic: how to bring essential, document-based information into a central system without adding operational burden. 

Using Document AI, we are helping the client develop tailored extraction models for each document type. These will be integrated into a fully automated pipeline that processes incoming documents and link content to master records within the data platform. The solution is being built using native Snowflake Cortex AI services, complemented by cloud-based tools for orchestration and transformation, with integration into a core case management system. Additionally, document governance is embedded via a custom interface that allowed users to review, validate, and, where needed, override extraction results, ensuring accuracy and accountability. 

The impact of this approach is clear. By reducing manual processing and improving data accuracy, organisations are positioned to unlock key insights that might otherwise be delayed or overlooked. What may begin as a targeted technical solution has the potential to drive broader outcomes – accelerated decision-making, enhanced transparency, and greater operational agility. 

For many organisations, AI remains a space of high promise but unclear application. The potential is evident, yet concerns about complexity, cost, and risk often obscure the path to value. Document AI offers a clear and pragmatic counterpoint. It doesn’t require a complete reinvention of systems or deep technical retraining. Instead, it addresses a widespread, high-impact challenge with clarity and efficiency. By turning documents into structured, actionable data – securely and at scale – it empowers organisations not just to keep pace with change, but to lead it. 

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