Businesses today utilize an incredible amount and variety of documents, from simple invoices, to complex legal contracts and technical manuals with detailed multi-column tables. Processing these documents manually is not only slow and resource-intensive but also prone to errors, with organizations losing as much 15-25% of employee time to these tedious tasks.

Over the years, enterprises have turned to RPA, OCR, and workflow tools to solve these problems, but these solutions are often rigid, complex to maintain and scale, and most notably adopted by individual business teams in isolation.

The solution lies in AI-powered automation, which can slash costs by a factor of ten. However, the complexity and variability of these documents presents a major challenge and when these richly structured documents are processed with basic tools that treat them as flat text, critical business context is lost, crippling the effectiveness of analytics and AI.

To overcome these challenges, companies need an intelligent document processing (IDP) system or Document AI that can provide a centralized platform to easily, automatically, and accurately extract relevant information trapped in these documents.

End-to-End Intelligent document processing in Snowflake with Document AI

Snowflake provides a comprehensive, end-to-end platform for document intelligence, seamlessly integrated within the AI Data Cloud. This allows organizations to manage the entire lifecycle of document processing from ingestion and extraction to validation and application, all within a single, secure, and governed environment.

The core component of this ecosystem is Snowflake Cortex AI, which provides the building blocks for intelligent applications. Key features include:

• Composable Framework: Combine Snowflake-native interfaces, functions, LLMs, and Python logic for tailored workflows
• End-to-End Lifecycle Support: Ingest, extract, validate, and apply document data all within Snowflake
• Multi-Format Compatibility: Process PDFs, images, handwritten scans and 10+ more formats without conversion
• Structured & Unstructured Fusion: Align document data directly with structured Snowflake sources
• Multimodal Intelligence: Use text, layout, tables, and images together for complete and accurate understanding
• Human-in-the-Loop Ready: Add review workflows, exception handling, and validation logic with ease
• Custom Model Training: Fine-tune extraction models or use pre-trained ones with integrated support
• Deploy Natively, Govern Centrally: Run everything in-platform with Snowflake’s built-in security and observability

Exciting New Innovations

We’ve recently enhanced our document processing capabilities to help you take document intelligence in your organizations to the next level:

• AI_EXTRACT is a new AISQL function that offers the most efficient and scalable method for extracting any information from your documents, images and text. It is powered by Snowflake’s newest proprietary model Arctic-Extract – supporting 29 languages. For more see 
• PARSE_DOCUMENT LAYOUT mode preserves the structural richness of complex business documents during extraction for improved enterprise RAG
• Table Extraction in Document AI now extracts structured, columnar data from tables within your documents, making it ready for immediate analysis

Let’s dive deeper into each of these.

Build and run Document Processing pipelines programmatically at scale

Finally, AI_Extract is our SQL API inference solution for transforming diverse, unstructured data into a structured format at enterprise scale. It allows you to pull structured information from sources like text, images, and documents and unify it into a standard format for efficient analytics. 

This function is powered by Arctic-Extract, Snowflake’s next-generation document understanding model that processes image, text, and layout information in a single pass, leading to reduced inference and training times.

The API first approach of AI_EXTRACT enables an “Infrastructure as a Code” practice, allowing users to programmatically extract data and dynamically define the extraction prompt for a given document without using a UI. This provides the flexibility to handle documents with different formats, such as invoices from various vendors. Additional capabilities include support for 29 languages and the intelligent normalization of variable data formats like dates and currency.

Layout-aware parsing for complex multimodal documents

When analyzing financial documents like balance sheets, understanding the numbers present in tables or columns in context is critical. A traditional document processing may correctly extract pieces of data but completely miss the critical footnote that details debt conditions or interest rates. This severs the essential link between a line item, like Long-term debt, and the corresponding explanation that qualifies that number. Issues like this limit your analysis or the capabilities of AI systems using the extracted value to surface level data only.

PARSE_DOCUMENT LAYOUT mode is engineered specifically for such challenges. By preserving the document’s precise layout, it understands the context associated with the required information whether it contains tables, images or another complex layout. This ensures the integrity of documents, like SEC filings that contain complex tables in the example below, is maintained during processing.

As a result, you can move beyond simple data retrieval (RAG) and perform deep, analytical inquiries on the documents. Instead of just asking for the value of total assets, you can now ask far more specific questions like:

• “What are the maturity dates and interest rates for the debt instruments listed in the ‘Liabilities’ table?”
• “Based on the notes, what were the major drivers for the increase in ‘Goodwill’ this quarter?”
• “Summarize the company’s accounting policy for revenue recognition as described in the text preceding the income statement.”

Accurate table extraction from documents 

Now, to make informed business decisions, you must often analyze complex documents like contracts, invoices and other financial statements. A common example is the annual 10-K report, which contains detailed financial performance data organized in complex tables, making automated extraction a significant challenge and manually extracting this data is a slow, error-prone, and resource-intensive process.

Snowflake Document AI tackles this challenge head-on with the new Table Extract feature. Lets take the example of the 2025 World Economic Outlook Update that has multiple tables with nearly identical structures.

As shown in the image below, Document AI performs a zero-shot extraction that identifies the correct table from the document and extracts all the data in a structured format, even with nested headers and rows. The underlying model is powerful enough to handle these complex layouts without any finetuning.

Beyond its zero-shot extraction, you can also use schema-based extraction by defining a schema and specifying the desired columns in natural language.  For documents that contain multiple tables with a similar format, a “Locator” field can be used to uniquely identify and target the correct one. Finally, table extraction in Document AI allows you to annotate and fine-tune the model to enhance extraction accuracy.

Get Started with Document Intelligence in Snowflake

Processing complex documents is no longer a slow, error-prone, and resource-intensive manual task. Traditional automated solutions that are rigid and strip away critical business context are a thing of the past. Snowflake Cortex AI provides a comprehensive, end-to-end platform for document intelligence, allowing you to manage the entire document processing lifecycle within a single, secure, and governed environment.

Click here to learn how you can use Cortex AI to build a Retrieval Augmented Generation (RAG) based LLM assistant or start your 30-day Free Trial today.