GenAI-Powered Contract Intelligence for Legal Document Automation
Enhancing contract creation, review, and compliance for startups and SMEs through context-aware automation
85% Reduction in contract review time
by automating document analysis and clause extraction with GenAI
95% Compliance accuracy across contracts
with AI-driven validation of clauses, templates, and jurisdiction rules
About the Client
A LegalTech company offering a unified platform to manage, sign, and store contracts. Through intelligent workflows, customizable templates, digital signatures, and centralized repositories, it streamlines legal document management and improves operational efficiency for startups and SMEs.
Industry
LegalTech
Years in business
3+ years
Employee count
10+
Presence
India
“Forgeahead helped us modernize our cloud infrastructure in a way that improved scalability, reliability, and overall efficiency for our research platform.”
Dr. Michael Harrington
Chief Information Officer
the need
The client faced high manual effort in managing NDAs, agreements, and other legal documents, along with inconsistent template usage and evolving compliance requirements. Maintaining standardization was challenging due to variations across jurisdictions and contracting parties, resulting in duplication of effort, errors, and delays in contract review cycles.
Startups and SMEs, constrained by limited legal bandwidth, were more likely to overlook critical terms and generate inconsistent document versions, impacting contract accuracy and efficiency. This created the need for a system capable of automating template generation, analyzing contracts, and providing contextual recommendations while ensuring compliance and accuracy.
the solution
Forgeahead implemented a context-aware AI solution integrated with the client’s contract management platform to streamline document workflows through automation and intelligence. The solution enabled end-to-end support across the contract lifecycle, including document creation, analysis, and review through structured AI-driven capabilities.
A large language model was fine-tuned using AWS Bedrock with a curated dataset of over 20,000 legal documents, enabling highly context-aware outputs aligned with legal standards. The platform introduced automated document understanding, which allowed classification of contracts, identification of key clauses, and detection of missing or inconsistent sections.
Document generation capabilities were enhanced through template automation tailored to contract type, jurisdiction, and contextual inputs, ensuring consistency and compliance. In addition, document intelligence features enabled summarization of lengthy legal texts and generated actionable recommendations, supporting faster and more informed decision-making. Version comparison and change tracking functionalities were also incorporated, allowing users to track modifications and maintain accuracy across document iterations.
The Impact
85% Reduction in Review Time
Faster contract review cycles compared to manual processing
95% Accuracy in Document Compliance
Improved adherence to legal standards with reduced risk exposure
70% Improved Decision Quality
Better contract evaluation through AI-driven recommendations
75% Increase in Favorable Contract Outcomes
Stronger contract terms with reduced legal risk
Role of AWS
AWS services played a critical role in enabling intelligent automation and scalable processing. AWS Bedrock was used for model fine-tuning and inference, powering contract understanding and analysis with improved contextual accuracy. AWS Lambda supported event-driven workflow automation, accelerating document processing and reducing manual effort across various stages of the contract lifecycle.
Tech Stack
The solution was built using Python along with AWS Bedrock and AWS Lambda, enabling scalable AI-driven processing and seamless workflow automation.