Retrieval-Augmented Multi-Agent System for Rapid Statement of Work Generation
Amulya Suravarjhula, Rashi Chandrashekhar Agrawal, Sakshi Jayesh Patel, Rahul Gupta

TL;DR
This paper presents an AI-driven multi-agent system that automates the drafting, legal checking, and formatting of Statements of Work, significantly reducing time and improving accuracy compared to manual methods.
Contribution
It introduces a novel multi-agent AI system that understands content meaning, customizes SOWs, and ensures legal and formatting compliance, advancing automation in legal document drafting.
Findings
Full SOW generated in under three minutes
High accuracy and quality in legal and formatting checks
Reduced legal risks and time savings
Abstract
Drafting a Statement of Work (SOW) is a vital part of business and legal projects. It outlines key details like deliverables, timelines, responsibilities, and legal terms. However, creating these documents is often a slow and complex process. It usually involves multiple people, takes several days, and leaves room for errors or outdated content. This paper introduces a new AI-driven automation system that makes the entire SOW drafting process faster, easier, and more accurate. Instead of relying completely on humans, the system uses three intelligent components or 'agents' that each handle a part of the job. One agent writes the first draft, another checks if everything is legally correct, and the third agent formats the document and ensures everything is in order. Unlike basic online tools that just fill in templates, this system understands the meaning behind the content and…
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Collaboration in agile enterprises
