Enhancing Contract Market Intelligence and Benchmarking with C3 Generative AI

Unlock contract intelligence with C3 Generative AI to benchmark terms, reduce risk, and gain a competitive edge in private markets

By Drew Patel, AI Solutions Manager, C3 AI and Mindy Lin, Senior Data Scientist, C3 AI

In private markets, understanding contract terms has traditionally been challenging due to the lack of publicly available benchmarks. Without a reliable way to compare new agreements against industry standards, businesses struggle to determine whether contract terms are competitive. Historically, addressing this uncertainty required extensive manual review and reliance on internal data, leading to inefficiencies and increased risk.

C3 Generative AI for contract market intelligence and benchmarking transforms this process by analyzing past agreements to identify key negotiation points, assess contract competitiveness, and provide data-driven insights.

Challenges in Contract Analysis

Across industries, professionals responsible for contract management — such as procurement specialists, business development managers, and financial analysts — face several challenges when ensuring contract terms align with industry standards:

• Lack of Public Benchmarking Data: Unlike public markets, private contract terms are not readily available for comparison. This makes it difficult to determine whether specific terms are in line with historical company contracts.
• Reliance on Experience and Internal Data: Many professionals depend on personal expertise or limited internal records to assess contract competitiveness, which can put less experienced team members at a disadvantage.
• Uncertainty in Negotiation Strategies: Without concrete market comparisons, organizations lack the data-driven insights needed to strengthen their negotiation positions and make informed decisions.

How C3 Generative AI Powers Data-Driven Decisions

The solution first extracts relevant contract terms and compares them to company historical documents using a similar extraction process. As shown in Figure 1, the key terms page enables users to review each extracted term along with its corresponding market insights. By selecting a specific term, users can visualize how it compares to market standards and identify any deviations.

Figure 1: Users can benchmark new contracts and visualize market insights

By leveraging C3 Generative AI, contract teams can extract and analyze thousands of historical agreements, enabling them to:

• Benchmark contract terms at scale: Compare new agreements against a vast database of past company contracts to determine which terms are standard and which may require negotiation.
• Gain a competitive advantage: Use proprietary data sources to access insights that would otherwise be unavailable in private markets.
• Enhance negotiation strategies: Provide teams with quantifiable contract comparisons to support negotiations with objective, data-backed arguments.

How It Works: Contract Market Intelligence and Benchmarking

The contract market intelligence and benchmarking solution is built on three main technical components:

• Contract Document Extraction: Identifies and extracts key contract terms for historical benchmarking.
• Contract Term Analytics: Applies a tailored comparison methodology for each contract term based on its characteristics.
• Dynamic Cohort Benchmarking: Allows users to create customized historical cohorts for precise contract comparisons.

After the AI-driven extraction is complete, contract managers, procurement specialists, and business teams can access the contract terms details page to review extracted terms and compare them against selected historical cohorts to uncover valuable insights.

Contract Key Term Extraction

To establish a robust benchmarking system, C3 AI collaborated with industry professionals to identify key contract terms for extraction. Once these terms were defined, the extraction process was executed across two primary company data sources:

• Historical Document Extraction: The system is connected to historical data sources to extract relevant terms from historical contracts into a structured database.
• Uploaded Document Extraction: When a contract is uploaded to the application, generative AI analyzes the agreement. After the attorney finishes their review, the terms are extracted and benchmarked against the historical database to determine if they are in-market or out-of-market.

This dual-layered approach ensures that both past agreements and newly processed contracts contribute to the continuously improving dataset for contract benchmarking.

Contract Term Analytics

Each contract term requires a tailored approach for comparison based on its data type:

• Numerical Terms: Metrics like duration were stored as quantitative data values for statistical comparison.
• Boolean Terms: Simple yes or no data points that indicate the presence or absence of a certain condition.
• Categorial Terms: Discrete attributes such as governing law enables a state-by-state predefined category-based benchmark.
• Multi-Categorial Terms: Terms that can belong to multiple categories simultaneously which require a pick-list format for flexible comparison.

To ensure accurate and meaningful contract comparisons, the system classifies each term based on its appropriate methodology and applies the relevant analytical techniques.

Interactive UI for Dynamic Cohort Benchmarking

A critical part of the solution is enabling business and contract professionals to dynamically filter and refine contract comparisons. The interactive user interface includes:

• Cohort-Based Filtering: User-defined custom cohorts for comparison, ensuring that contracts were benchmarked against similar agreements. For example, a contract with a small deal size could be compared exclusively to other small deals rather than an unrelated large deal.
• Advanced Filtering Options: Multiple different active filters allow for highly specific cohorts.
• Data Visualization: UI includes graphs, charts, and statistics to help legal teams quickly understand deviations from market standards. Attorneys can quantitatively measure how far a contract term deviated from the median, enabling data-driven negotiation strategies.

By combining automated term extraction, tailored benchmarking analytics, and an interactive interface, C3 Generative AI for contract intelligence empowers professionals across industries with unparalleled insights into contract terms and market trends. This enables more informed decision making, strategic negotiations, and a deeper understanding of competitive contract positioning.

About the Authors 

Drew Patel is an AI Solutions Manager on the Generative AI team at C3 AI, where he works with large companies across various industries to identify high-impact AI use cases within their organizations. He leads cross-functional teams of data scientists and engineers, driving the development of generative AI pilots from concept to large-scale production. Drew holds a bachelor’s degree in computer engineering from Lehigh University and a master’s degree in computer science from the University of Illinois Urbana-Champaign.

Mindy Lin is a Senior Data Scientist on the Generative AI team at C3 AI, where she drives the design and implementation of advanced AI solutions for applications that solve complex business challenges and deliver impactful results. Mindy holds a bachelor’s degree in statistics from University of Waterloo and a master’s degree in business analytics from Massachusetts Institute of Technology.

Special thanks to Leticia Schettino, Christian Giovanelli, and John Abelt for their assistance in authoring this piece.