NetDocuments Context Graph is a legal workflow context graph platform released by NetDocuments Software, Inc. on May 14, 2026. It connects relationships among matters, documents, communications, owners, and expertise scattered across law firms and legal organizations, supporting search and AI operations that reflect user access permissions. Rather than functioning as a document indexer that simply indexes file content, it provides context in the form of a graph showing which documents are related to which matters, who is involved, and how they link to specific communications. Just as GPT constructs answers by understanding relationships across entire sentences rather than merely listing words, Context Graph is designed to help users explore relevant information based on the interconnected relationships surrounding legal workflows, rather than focusing on individual files. Existing generative AI-based document tools often rely solely on files uploaded directly by users or limited search results. While this approach can be useful for simple summarization, it makes it difficult to simultaneously identify key parties, core documents, responsible attorneys, and the chronological flow of a case from long-accumulated matter records. For legal organizations, maintaining access controls and ethical walls is as critical as accuracy. The differentiator of Context Graph lies in its permission-aware architecture, which provides AI with an organization’s interconnected knowledge while reflecting existing authority structures. According to the public introduction, it supports meaning-based organizational search, matter summarization, and the identification of key documents, parties, and timelines. It can be utilized for drafting, redlining, playbook generation, and document comparison within NetDocuments and Microsoft 365 environments. In the legal and knowledge management departments of life sciences organizations, Context Graph can be used to explore non-disclosure agreements (NDAs), research contracts, patent-related communications, and responsible parties involved in new drug co-development or technology transfer matters within a single context. For example, by searching for the code name of a specific candidate compound or contract counterparty using semantic search, and then reviewing related contracts, emails, key parties, and chronological records, teams can reduce the effort of sequentially browsing individual folders and faster reconstruct the background of negotiations. However, since specialized data models for life sciences or features for regulatory documents have not been confirmed in the Discovery information, it is necessary to verify the scope of support and data governance before actual implementation. In the contract review process of pharmaceutical and biotech companies, Context Graph can be utilized to create playbooks reflecting organizational approval criteria, compare clauses of new license agreements with existing documents, or generate redlining drafts. In patent disputes or regulatory response preparations, it can serve as a supplementary layer where key documents, parties, and timelines for each matter are first organized, followed by responsible experts reviewing the original texts and evidence. The automated results provided by Context Graph do not replace legal advice or regulatory judgments. Before adoption, it is necessary to verify the scope of Microsoft 365 integration, data retention policies, audit logs, permission inheritance methods, and AI processing conditions in official contracts and security documentation.

NetDocuments Context Graph is a legal-context graph platform released by NetDocuments Software, Inc. on May 14, 2026. It connects relationships among cases, documents, communications, attorneys, and expertise scattered across law firms and legal organizations, supporting search and AI operations that reflect user access permissions. Rather than functioning as a document search engine that merely indexes file content, it provides context in the form of a graph showing which documents are related to which cases, who was involved, and how they connect through communications. GPTs consider entire sentences rather than mere word lists