# Use Cases ## Document Intelligence ### Turn unstructured documents into decisions. Valuable information lives in contracts, clinical notes, invoices, and operational records that traditional data systems can't read. We build Document Intelligence systems that turn those documents into structured, analytics-ready data. Ingesting, extracting, and structuring data from documents, emails, and ERP systems. ## CHALLENGES ### What we typically see Unstructured content is the dark matter of enterprise data. It exists everywhere, it contains critical information, and most data infrastructure can't touch it. #### Information locked in documents Contracts, reports, clinical notes, and records sit in formats that can't be queried, analyzed, or acted on at scale. #### Manual extraction that doesn't scale Teams spend hours pulling information from documents by hand, creating bottlenecks that slow every downstream process. #### Missed insight Patterns and signals buried across thousands of documents never surface because nobody has the bandwidth to find them. #### Inconsistent interpretation Different people extract different things from the same document, creating data quality issues that compound over time. ## Approach ### How we work We build Document Intelligence systems, designing every solution around your specific document types, extraction requirements, and downstream use cases. 01 Document type assessment and extraction architecture design 02 Model configuration, training, and validation against your actual documents 03 Integration into the workflows and systems that consume the extracted data 04 Quality assurance and governance framework for extraction accuracy over time The output is a production system that processes documents at scale and routes extracted data to the people and systems that need it, along with the schemas, review patterns, and operational best practices. ### "A lot of companies have challenges in accessing their internal information. The new tooling around RAG systems has enabled us to leverage that data and turn it into actual insights." Jacob Zweig Managing Director, AI ## Applications ### Across industries and teams #### Sales & Customer Service Document Intelligence Built for Sales and revenue teams extracting insights from contracts, proposals, and customer communications. #### Academic Document Intelligence Built for Higher Education institutions processing research documents, applications, and academic records. #### Clinical Document Intelligence Built for Healthcare organizations extracting and routing insights from clinical notes, lab reports, and patient records. #### Operational Document Intelligence Built for Manufacturing teams processing maintenance records, inspection reports, and operational documentation. ## Accelerator ### Document Intelligence Accelerator For organizations with critical data trapped in unstructured files, we deploy the Document Intelligence Accelerator, a Snowflake-native pipeline that turns documents into analytics-ready data. ### What's included #### Snowflake-native extraction pipeline Streams and tasks ingest documents and extract structured data via Document AI and Cortex. Handles multi-column, nested-table, and mixed content. #### Schema-driven extraction Defined schemas per document type, updatable as new types come online. No template rebuilding for every format. #### Confidence scoring and review app Confidence scores on every field. A Streamlit review app surfaces low-confidence extractions next to source documents, and reviewer decisions feed back as quality signal. #### Analytics-ready output Output lands in governed Snowflake tables, ready for BI tools and downstream analytics. The same corpus powers semantic search, RAG, and Cortex-driven Q&A. ## Process ### How it works 01 #### Assessment Document type audit and extraction schema design 02 #### Configuration Pipeline setup, schema definition, and confidence threshold tuning 03 #### Validation Extraction accuracy testing against representative documents 04 #### Deployment Production pipeline activation and review app rollout ## Proof & Perspective ### From the field Innovative thinking. Real outcomes. #### Document Intelligence Encoding 40+ years of expertise into an AI knowledge assistant #### Document Intelligence Automating data review and verification with document AI .png) #### Document Intelligence Agentic AI: Using Tool Calling to Go Beyond RAG #### Document Intelligence Building the Future of Document Intelligence on Snowflake Cortex Code ## FAQ ### What types of documents can you work with? Contracts, clinical notes, research papers, financial reports, maintenance records, forms, invoices, regulatory filings, and more. The approach is designed around your specific document environment, not a generic template. ### How accurate is the extraction? Accuracy depends on document type, layout consistency, and field clarity. During validation we measure performance against your actual documents and report results in terms relevant to the use case: field-level precision and recall, percentage requiring human review, and end-to-end throughput. Every extracted field carries a confidence score, so you can route high-confidence results straight to production and hold uncertain extractions for review. ### Can this handle handwritten or scanned documents? Yes. Scanned and image-based documents are processed through Snowflake's Document AI and Cortex capabilities. Handwriting accuracy depends on legibility, but the same confidence scoring routes uncertain extractions to human review rather than producing silent errors. ### How does the extracted data get to the people who need it? Extracted data lands in governed Snowflake tables, immediately available to your existing BI tools, reporting layer, and downstream analytical workflows. The same parsed corpus can also power semantic search, retrieval-augmented generation, and Q&A over your document corpus. ### What happens to documents the model isn't confident about? Every extracted field carries a confidence score, and configurable thresholds determine what goes straight to production vs. what's routed to the built-in review application. Reviewers see the source document and the extracted data side-by-side, focus on the flagged fields, and approve or correct them. Their decisions feed back into the pipeline as quality signal over time.