How a context graph makes enterprise AI more accurate and less expensive
Atlassian introduces context graphs to improve enterprise AI accuracy by mapping relationships between organizational data, reducing reliance on costly models and fragmented retrieval.
Enterprise AI often delivers plausible but irrelevant answers because it lacks organizational context, despite access to documents, tickets, and repositories. Documents, decisions, and discussions exist in silos, forcing AI to infer connections rather than using them directly. A context graph structures these relationships, enabling AI to understand how work, people, and systems interconnect. Atlassian’s Teamwork Graph, built over 20 years, now maps over 150 billion objects and relationships to provide this living organizational map.
Generic AI tools retrieve isolated fragments like Jira issues or Confluence pages but miss related decisions, dependencies, or owners. This results in technically correct but incomplete answers that fail at critical points. A context graph answers not just 'where is something?' but 'what does it mean and what is it connected to?' It helps AI identify relevant context upfront, reducing the need for expensive reasoning models. Teams often over-invest in model selection while neglecting context design, leading to higher costs and lower accuracy.
Confluence can serve as the foundation for high-quality knowledge, with the graph transforming trusted content into precise answers. Organizations must first ensure source quality, enforce permissions, and model relationships before evaluating models. A permission-aware knowledge base ensures AI retrieves only authorized information, preserving security while enabling useful responses. Atlassian’s platform enforces these boundaries across its tools and third-party data.
The Teamwork Graph processes over 12 billion updates daily, acting as organizational memory that connects people, work, and context. Pre-curated relationships allow AI to traverse dependencies, decisions, and impact paths efficiently. This approach scales from individual workflows to enterprise-wide knowledge sharing, improving AI reliability and business outcomes. The graph integrates with Atlassian tools and MCP-compatible interfaces to deliver context-aware actions.