Snowflake Case Study: AI Agent for Contract Review
Snowflake's internal audit team developed an AI-powered agent using Snowflake's Cortex AI and Openflow to automate contract review processes, significantly reducing manual effort and improving accuracy. The agent extracts data from PDFs, classifies terms against a user-managed playbook, and allows auditors to focus on exceptions rather than exhaustive reading. This approach cuts review time by 70% and provides auditable proof of all decisions, scaling with deal volume without linear headcount growth.
- →AI Agent Automates Contract Data Extraction and Classification
- →Auditor-Controlled Playbook Enhances Contract Review Accuracy
- →Reviewer-Centric Interface and Learning Mechanism
- →Snowflake Shares Internal AI Agent for Contract Review
- →Novel Term Detection Identifies Unforeseen Contractual Language
Features (3) ›
- AI Agent Automates Contract Data Extraction and Classification
The Contract Review Agent uses Snowflake Openflow and Cortex AI to ingest PDFs, extract structured fields like customer name and discount terms via AI Extract, and classify clauses against a user-managed playbook. This automates the detection of standard and nonstandard terms, providing confidence scores and explanations.
- Auditor-Controlled Playbook Enhances Contract Review Accuracy
The system features a governed Snowflake table acting as a playbook, which auditors directly manage to define 'standard' and 'nonstandard' contract terms. This allows the agent to evaluate clauses against evolving business context and regulatory changes without engineering intervention.
- Reviewer-Centric Interface and Learning Mechanism
Findings are presented in a user-friendly application where auditors can approve, override, or escalate flags. Every correction is logged as feedback, refining the agent's extraction precision and classification accuracy over time. A natural-language interface provides portfolio-level visibility.
Enhancements (1) ›
- Novel Term Detection Identifies Unforeseen Contractual Language
Beyond rule-based detection, the agent uses semantic similarity to a corpus of known language to flag potentially novel terms. Auditors then label these terms, teaching the system to better identify meaningful anomalies and improve its performance on unique clauses.
Notes (2) ›
- Snowflake Shares Internal AI Agent for Contract Review
This is an internal case study detailing how Snowflake's Forward Deployed Engineer team built an AI-powered contract review agent on the Snowflake platform. The tool, used internally, demonstrates how advanced AI capabilities can transform high-stakes, regulated workflows by automating contract analysis.
- Significant Improvements in Review Efficiency and Auditability
The AI agent reduces contract review time by 70%, enables full coverage of high deal volumes without proportional headcount increases, and provides a detailed audit trail for all extraction, classification, and correction activities, meeting external auditor expectations.
https://www.snowflake.com/content/snowflake-site/global/en/blog/agentic-intelligence-contract-review-snowflake
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