Contracts are a rich source of business intelligence, capturing everything from pricing and service-level agreements to renewal terms, discounts, and termination clauses. Yet for many organizations, this critical information remains buried in unstructured PDFs and legal documents, making it difficult to search, analyze, and act upon.
To help solve this challenge, Hakkoda and IBM developed a proof of concept using Snowflake CoCo (formerly Cortex Code) to transform static contract documents into structured, queryable data. The resulting solution enables non-technical users to unlock a host of critical insights previously hidden in contract language.
The Challenge: Seeing the Forest for the Trees
Organizations often manage thousands of contracts across business units, vendors, and customers, each with unique terms and conditions. While these documents contain valuable operational and financial information, extracting that data has traditionally required manual review by legal, procurement, or operations teams.
This approach is time-consuming, difficult to scale, and limits an organization’s ability to answer critical business questions, such as:
- Which contracts include specific SLA commitments?
- Where are discount terms or pricing exceptions applied?
- Which agreements are nearing renewal?
- What contractual obligations could introduce compliance or operational risk?
Without a structured way to analyze contract language, organizations miss opportunities to improve visibility, reduce risk, and make more informed business decisions.
The Solution: Making Contract Data Actionable
The Hakkoda and IBM team used Snowflake Cortex to rapidly build a contract intelligence proof of concept that converts unstructured contract PDFs into normalized, searchable data within Snowflake.
The solution leverages Snowflake AI capabilities to:
- Parse and understand contract documents.
- Extract key business terms and metadata.
- Classify clauses into a consistent taxonomy, including pricing, SLAs, discounts, renewals, and termination provisions.
- Store extracted information in structured Snowflake tables for search, reporting, and downstream analytics.
To accelerate development, the team used Snowflake CoCo to generate much of the solution framework, including pipeline scaffolding, Cortex integrations, database schemas, documentation, and evaluation components. This allowed the team to spend less time building foundational code and more time validating business outcomes and refining extraction accuracy.
What Becomes Possible with Searchable Contract Intelligence?
In just three weeks, Hakkoda and IBM delivered a fully functional proof of concept demonstrating the feasibility of AI-powered contract intelligence.
Information that was once locked inside lengthy contract documents became structured, searchable, and reportable through a standardized data model. The solution established a scalable architecture for future contract analytics while also documenting key considerations for production deployment, including AI model limitations, document chunking strategies, evaluation approaches, and cost management.
By transforming unstructured contracts into actionable business data, organizations can improve contract visibility, strengthen compliance efforts, accelerate contract reviews, support procurement and legal teams, and identify opportunities for optimization across their contract portfolio.
As enterprises continue investing in AI-driven automation, contract intelligence represents a powerful opportunity to unlock value from information they already own without changing where that data lives.
Whether you’re looking to modernize contract management, improve compliance, or unlock new insights from unstructured documents, Hakkoda, an IBM Company, is positioned to help you accelerate your AI journey with Snowflake CoCo.
Let’s talk today to discuss how we can help you build intelligent, scalable solutions that transform enterprise documents into actionable business intelligence.
