System of Context
Glean's horizontal, AI-powered index of enterprise knowledge connects to every app and unifies organizational data—capturing content, activity signals, people metadata, and permissions—to provide comprehensive context that enables AI to work better for everyone in the organization. Gartner predicts that 75% of new analytics content will use generative ai by 2027, emphasizing the growing need for contextualized insights in enterprise environments.
What is a System of Context?
A system of context is the foundational layer that makes AI truly useful in the enterprise. Think of it as the connective tissue between all your company's scattered information—from Slack conversations and Google Docs to Salesforce records and Jira tickets. Employees waste 1.8 hours daily (9.3 hours per week) searching for information, representing a massive productivity drain that Systems of Context can address.
Most enterprise ai solutions struggle because they lack the full picture of how your organization actually works. They might have access to documents, but they don't understand relationships between people, projects, and processes. A system of context solves this by creating a unified understanding of your company's knowledge, relationships, and permissions.
At Glean, our system of context goes beyond simple data aggregation. We build a comprehensive map of your organization that includes:
Content and documents across all your applicationsActivity signals that show how information flows and what's actually importantPeople metadata that captures expertise, relationships, and organizational structurePermissions that ensure the right information reaches the right people
This unified view enables AI to provide relevant, accurate, and secure responses that reflect how your company actually operates.
How Systems of Context Work
Building an effective system of context requires solving several complex challenges that traditional enterprise search and AI solutions have struggled with.
Relationship mappingRaw data tells only part of the story. The real value comes from understanding relationships—which documents are related to which projects, who collaborates with whom, and how information flows through your organization. This requires building a knowledge graph that captures these complex interconnections. A Forrester study found 320% ROI for an enterprise knowledge graph platform, driven by $2.6 million in infrastructure savings and $3.8 million in time savings for data scientists.
Relationship mapping: Raw data tells only part of the story. The real value comes from understanding relationships—which documents are related to which projects, who collaborates with whom, and how information flows through your organization. This requires building a knowledge graph that captures these complex interconnections.
Activity signal processing: Not all information is equally important. A system of context analyzes activity signals—like views, edits, shares, and comments—to understand what information is actually valuable and current within your organization.
Permission inheritance: Enterprise security is complex, with permissions that vary across applications and change over time. A robust system of context must understand and enforce these permissions to ensure AI responses respect your organization's security model.
A well-designed system of context transforms AI from a generic tool into a knowledgeable colleague that understands your organization. It enables AI to: 97% of AI-related breaches occur in organizations lacking proper access controls, highlighting the critical importance of permission inheritance in enterprise ai systems.
Why Systems of Context Matter
Without comprehensive context, enterprise ai falls short in predictable ways. LLMs might hallucinate information, provide outdated answers, or miss critical nuances specific to your organization. They treat all information as equally valid and lack understanding of your company's unique terminology, processes, and relationships. For example, LinkedIn achieved a 78% accuracy improvement in customer service AI by integrating enterprise knowledge graphs with retrieval-augmented generation (RAG).
A well-designed system of context transforms AI from a generic tool into a knowledgeable colleague that understands your organization. It enables AI to:
Provide answers grounded in your actual company knowledge
Understand the relationships between people, projects, and processes
Respect security boundaries and permissions
Learn your organization's specific terminology and context
Surface relevant information based on who's asking and why
This comprehensive understanding is what separates truly useful enterprise ai from basic chatbots or document search tools.
System of Context in Practice
Customer support teams use systems of context to automatically route tickets to the right experts, suggest relevant knowledge base articles, and draft responses that reflect company policies and tone.
Engineering teams leverage contextual understanding to find relevant code examples, understand system dependencies, and get answers about architecture decisions that consider the full technical landscape.
Sales teams benefit from context that connects customer conversations with product information, competitive intelligence, and deal history to provide more informed and personalized interactions.
HR teams use contextual AI to answer policy questions, guide employees through processes, and surface relevant resources based on role, location, and tenure.
Common Questions
How is a system of context different from a data warehouse?
While data warehouses store structured data for analysis, a system of context creates a living, interconnected understanding of your organization. It captures not just what information exists, but how it relates to people, processes, and activities across your entire enterprise.
Does building a system of context require replacing existing tools?
No. A system of context works by connecting to your existing applications through APIs and connectors. It creates a unified layer on top of your current tech stack without requiring you to migrate data or change workflows.
How does a system of context handle data privacy and security?
Security is built into the foundation. The system inherits and enforces permissions from source applications, ensuring users only see information they're authorized to access. This upstream permission model prevents data leakage and maintains your existing security boundaries.
What's the difference between a system of context and enterprise search?
Traditional enterprise search focuses on finding documents that match keywords. A system of context understands relationships, intent, and organizational dynamics to provide contextually relevant information. It's the difference between finding a document and understanding how it fits into your work.
How long does it take to see value from a system of context?
Organizations typically see immediate improvements in search quality and AI responses. However, the system becomes more valuable over time as it learns your organization's patterns, terminology, and relationships. Most companies see significant quality improvements within the first six months.
