Case Study: Transforming Statement of Work Review with AI
An AI-powered system that streamlines Statement of Work reviews, uncovering risks and improvements in hours instead of days.
Case Study: Transforming Statement of Work Review with AI
Introduction The SOW Analyzer is an AI-powered system that automates the review of Statements of Work. Traditionally, reviews were slow and inconsistent. Today, the Analyzer is in production use by dozens of directors, managers, and solution architects, delivering fast, structured insights and raising the overall standard of project documentation.
The Problem SOWs often contain gaps, risks, and inconsistencies that only surface mid-project, leading to cost overruns, delays, and disputes. Manual reviews were resource-intensive and inconsistent, heavily dependent on reviewer expertise. The business needed a way to standardize quality, reduce review time, and identify risks early.
Technical Solution The Analyzer runs on an eleven-phase LangGraph workflow that evaluates SOWs across Sales, Implementation, Legal, Execution, and Support domains. Azure Document Intelligence provides reliable parsing with a caching layer for performance. A contextual RAG system balances project documents with best-practice templates, allowing the Analyzer to assess not only whether clauses exist but whether they meet the intent of risk mitigation. WebSocket event streaming gives real-time progress, and structured reporting delivers executive-ready summaries alongside technical detail.
Outcomes Since adoption, the Analyzer has delivered strong results. Reviews now complete in hours instead of days. Each review surfaces dozens of high-quality improvements that would otherwise have been missed. Quality across projects is standardized, reviewer workload is reduced, and leadership has greater confidence in contracts before execution. Senior staff are able to focus on higher-value strategic work rather than clerical checks.
Conclusion The SOW Analyzer shows how applied AI can address a real business pain point. By simplifying the architecture, embedding intelligence, and aligning with enterprise needs, the tool has become a production-ready platform delivering tangible value. Its evolution highlights the impact of iterative improvement and demonstrates that AI can move from experimentation to repeatable outcomes that improve project delivery, risk management, and organizational confidence.