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Knowledge Graph and Vector Database Integration

37 posts · Dec 2025 – Sep 2026

What this trail covers

Glean's enterprise AI stack continues to evolve, with a focus on integrating its knowledge graph and vector database capabilities with external platforms via the Model Context Protocol (MCP). This integration allows external tools like v0 at Vercel and Langflow to access Glean's permission-aware, grounded context, enabling AI-generated applications to be informed by live company knowledge, including documentation, systems, and decisions. The system ensures consistent, permission-aware answers by leveraging RAG techniques for real-time search and assistive experiences across all company tools, securely, contextually, and at scale.

All Glean trails
  1. From enterprise search to enterprise context: what AI agents actually needThis post argues that AI agents require an "enterprise context layer" beyond traditional enterprise search to perform reliable work. It details the architectural requirements for such a layer, including authoritative retrieval, signal-driven volume reduction, cross-system understanding via a knowledge graph, permissions enforcement at retrieval, and context delivery tuned for multi-step workflows. It contrasts this with basic connectivity and highlights the importance of indexed, hybrid, graph-aware architectures for delivering high-quality, permission-aware context to AI agents, citing a 1.9x preference for Glean's context layer in evaluations.
  2. Retrieval-augmented generation (RAG) for LLMsThis post describes the core concepts and benefits of Retrieval-Augmented Generation (RAG) for LLMs. It details the RAG workflow, including input, retrieval from external knowledge sources (often stored in vector databases), contextualization, and generation. The post highlights RAG's advantages for enterprises, such as improved accuracy, domain-specific expertise, adaptability, cost efficiency, and trust. It also touches on key implementation considerations like source quality, relevance, stylistic consistency, balancing retrieval and generation, and evaluation. Finally, it briefly mentions Glean's use of RAG to provide grounded, permission-aware answers.
  3. Glean's MCP servers bring full company context to where your AI runsThis post details the engineering implementation of Glean's Model Context Protocol (MCP) servers, enabling AI agents to access full enterprise context for tasks like terminal debugging, CI/CD pipeline debugging, and documentation writing. It highlights the use of Glean's knowledge graph and search capabilities to provide context, and the security measures like centralized OAuth and permission enforcement for MCP servers. The post also mentions integrations with tools like Cursor and the agent toolkit.
  4. How knowledge graphs work and why they are the key to context for enterprise AIThis post details the technical underpinnings of Glean's knowledge graph, explaining its structure (entities, relationships, triplets, edge properties), disambiguation processes for entities and intents, and how it grounds LLMs for more reliable enterprise AI. It highlights the limitations of LLMs in handling multi-hop reasoning, enterprise-specific language, and deterministic queries, contrasting them with the structured, relationship-based approach of knowledge graphs. The post also introduces Glean's 'personal graph' as an extension to better understand human context at work.
  5. Introducing contextual image understanding in Glean: The right images at the right timeThis post details the integration of multimodal image understanding into Glean Assistant. The system now ingests images from connected sources (e.g., Google Drive, O365), applies OCR and captioning to build structured metadata, and passes both text and image context to the LLM. This allows Assistant to proactively surface relevant images (charts, diagrams, screenshots) in responses, transforming visual content into intelligent answers. This capability is integrated with the existing knowledge graph and vector database, ensuring permission-aware and contextually relevant visual discovery.
  7. Is MCP + federated search killing the index?This post argues that LLMs and protocols like MCP make enterprise search indexes *more* essential, not obsolete. It details the drawbacks of federated search (latency, limited data, inconsistent APIs, simple ranking, lowest common denominator filtering) and contrasts them with the benefits of a permission-aware index. The post highlights Glean's investment in custom crawlers, enterprise-adapted indexing and ranking (including semantic search and knowledge graphs), and continuous experimentation (cold-start deployment, topicality-aware ranking, calibration loops) as key differentiators. It positions the index as the most cost-effective and performant way to ground intelligent systems, emphasizing that larger context windows do not replace the need for precise, curated inputs from an index.
  8. RAG vs. LLM fine-tuning: Which is the best approach?This post details the strategic decision-making process between Retrieval-Augmented Generation (RAG) and LLM fine-tuning for enterprise AI. It explains the mechanics, benefits, and use cases of each approach, highlighting RAG's strength in providing contextual relevance and up-to-date information, and fine-tuning's efficacy in achieving domain fluency and task precision. The post advocates for a combined approach, where a fine-tuned model serves as a foundation and RAG injects real-time data, enabling more robust and personalized AI applications. It frames the choice not just technologically, but as a business strategy for knowledge management and precision.
  9. Retrieval Augmented Generation (RAG) – The key to enabling generative AI for the enterpriseThis post explains the core principles of Retrieval Augmented Generation (RAG) as a framework for enabling generative AI in the enterprise. It details how RAG separates knowledge retrieval from the generation process, grounding LLM responses in real, external enterprise knowledge. Key aspects highlighted include the importance of relevancy, recency, and permissions in the retrieval phase, and how this approach mitigates hallucinations and provides traceable, referenceable answers. The post contrasts RAG with fine-tuning, emphasizing RAG's cost-efficiency and ability to handle dynamic information.
  10. Retrieval augmented generation use cases: Transforming data into insightsThis post details the foundational concepts and technologies behind Retrieval Augmented Generation (RAG), explaining how it combines language models with external knowledge resources. It outlines the key components (retrieval mechanism, language model) and underlying technologies (Transformer architectures, vector embeddings, neural network retrievers). The post also explores RAG's applications in machine translation, question answering, summarization, dialogue systems, personal assistants, search engines, and recommender systems. Finally, it touches upon challenges like bias, scalability, and ethical considerations, positioning Glean as an enterprise-ready RAG solution.
  11. Secure generative AI for the enterprise requires the right permissions structureThis post details the engineering challenges and solutions for implementing a robust, permission-aware framework for generative AI in an enterprise context. It highlights the need for comprehensive yet nuanced permissioning, addressing concurrency and scalability issues with live permission updates and API rate limits. The post emphasizes the development of a unified permissions model that can understand and adapt to diverse datasource permission architectures, including edge cases like publicly visible documents after prior access, temporary permissions, and datasources with non-flattened user/group lists. It underscores that years of engineering effort were required to tune this solution for scale and complexity.
  12. Unlocking the Power of Vector Search in EnterpriseThis post details the benchmarking of text embedding offerings for enterprise search, comparing commercial API providers (OpenAI, Cohere) with open-source models (E5-large, Instructor-XL, MPNet). It demonstrates that open-source embeddings outperform commercial ones for enterprise search tasks based on NDCG@10 and R@100 metrics. Furthermore, it introduces Glean's method for fine-tuning embeddings with company-specific language, showing significant performance improvements over generic models and continuous adaptation over time. The post also reiterates Glean's hybrid search approach combining vector search, keyword search, and personalization.
  13. What are RAG models? A guide to enterprise AI in 2025This post explains the concept of Retrieval-Augmented Generation (RAG) models, detailing their architecture (indexing, retrieval, augmentation, generation) and benefits (timeliness, transparency, efficiency, modularity, scalability). It highlights RAG's importance for enterprise AI by addressing information fragmentation and increasing trust. Use cases in search, customer support, sales enablement, and content generation are discussed. Limitations related to data quality and semantic reasoning are acknowledged, emphasizing the shift towards grounded, adaptive AI. Glean's application of RAG for real-time search and assistive experiences is mentioned.
  14. What is Vector Search?This post details the technical underpinnings of vector search, including its definition, how it differs from traditional keyword search, and its role in modern AI systems like RAG. It explains vector embeddings, the process of generating them using models like BERT, and how they capture semantic meaning. The post also discusses customizing vector search for enterprises through fine-tuning embeddings to understand domain-specific terminology. A six-step pipeline for how vector search works is outlined: data ingestion, embedding generation, indexing (HNSW), query processing, similarity search (ANN), and scoring/ranking with permission checks. It highlights benchmarking results showing open-source embeddings can outperform commercial ones and emphasizes the importance of a hybrid search system combining vector search with keyword search and personalization.
  15. What sets great retrieval augmented generation apart — and why vector search isn’t enough for AIThis post details Glean's approach to Retrieval Augmented Generation (RAG) for enterprise search, emphasizing the limitations of simple vector or lexical search alone. It highlights Glean's hybrid search strategy, powered by a knowledge graph framework with proprietary anchors and signals, a scalable crawler, and optimizations for the LLM search interface. The post contrasts Glean's contextually rich, permission-aware responses with standard RAG solutions, showcasing how its hybrid approach, combining semantic and keyword matching with deep contextual understanding, delivers superior relevance and precision for enterprise use cases. It also touches on the importance of proprietary data for fine-tuning models and improving embedding scores.
  16. Why a unified document model is essential for a comprehensive workplace ChatGPTThis post details the necessity and challenges of building a unified document model for enterprise generative AI. It emphasizes the importance of structuring diverse data sources (documents, tickets, code, messages, etc.) by considering commonalities and idiosyncrasies. Key challenges include modeling user identities, permissions, unstructured text, relationships, engagement, crawling, context, understanding, indexing, scoring, presentation, duplication, and deprecation. The post highlights how a well-designed unified model is foundational for ranking models and enables comprehensive, coherent search and AI experiences by putting all data sources on equal footing.
  17. What is semantic search, and how does it enhance enterprise productivity?This post explains the concept of semantic search and its benefits for enterprise productivity. It details how semantic search works by understanding user intent, conceptual mapping, contextual relevance, cross-platform integration, and continuous learning. It contrasts semantic search with lexical/keyword search, highlighting its advantages in ambiguity resolution, synonym recognition, language variations, and contextual understanding, leading to improved precision and recall. The post also provides examples of semantic search in workplace scenarios and introduces Glean as an enterprise search platform that leverages semantic search.
  18. RAG, or Retrieval Augmented Generation: Revolutionizing AI in 2025This post provides a foundational overview of Retrieval Augmented Generation (RAG), detailing its architecture, key concepts (dense retrieval, sequential conditioning, marginalization), and technical components (document encoder, query encoder, Transformer-based generator). It explains the integration mechanisms (joint training, cross-attention) and outlines applications in chatbots and content generation. The post also touches upon dataset preparation, model pretraining, hyperparameter optimization, and challenges like data quality, bias, performance, scalability, and ethical considerations. While it mentions RAG's role in enhancing AI, it does not detail Glean's specific implementation or evolution of RAG systems beyond a general explanation.
  19. Enterprise context starts with indexing, but it doesn’t end thereThis post elaborates on Glean's 'system of context' beyond indexing, emphasizing the importance of specialized indexes for different data types (company data, code, experts, profiles, tools, calendars) and the integration of graphs, memory, data connectors, and tools. It highlights how this comprehensive approach enables AI to understand the enterprise and perform complex tasks, contrasting it with vendors that only offer basic indexing. The post details how breadth and depth of indexing, along with diverse retrieval methods (semantic, lexical, structured), contribute to improved token efficiency and task completion. It also poses critical questions for enterprises to evaluate AI vendors based on their indexing capabilities and broader contextual systems.
  20. Generative AI for software engineers is more than code completionThis post introduces and elaborates on the 'two-layer model' for generative AI in software engineering, emphasizing the critical role of a 'context layer' beneath coding surfaces. It details how this context layer, powered by Glean's knowledge graph and enterprise graph capabilities, connects disparate engineering systems (code repositories, Jira, Slack, observability tools, design docs, wikis) to provide AI tools with comprehensive, permission-aware context. This enables AI to move beyond code completion to assist with finding context, navigating incidents, and shipping safely. The post explains how this architecture addresses the 'AI productivity paradox' by solving the bottleneck of context assembly, which slows down developers and impacts onboarding, daily coding, and incident response. It highlights Glean's role in indexing code and documents with hybrid search, building an enterprise graph linking services, APIs, incidents, tickets, and owners, and respecting security and governance boundaries.
  21. Glean MCP Gateway: The context AI needs to get to workThis post introduces the Glean MCP Gateway, which enhances the Model Context Protocol (MCP) by providing a dedicated context layer for AI. It addresses the limitations of off-the-shelf MCP tools by leveraging Glean's precomputed index and knowledge graph for more efficient and accurate context delivery. The Gateway offers secure access to data and tools with granular access controls, prompt injection protection, and centralized administration. It also enables centralized rollout via MDM and provides detailed usage insights through a centralized dashboard.
  22. Context makes the Coworker: Glean preferred ~2.5x as often as off-the-shelf MCP tools, which consumed 30% more tokens in Claude CoworkThis post benchmarks Glean's centralized indexing and knowledge graph approach against federated search (off-the-shelf MCP tools) for AI coworker applications. It demonstrates that Glean's approach results in ~2.5x higher user preference and ~30% lower token consumption by Claude Cowork. The technical contribution lies in the comparative analysis of federated vs. indexed context layers, highlighting the economic trade-offs (storage vs. compute/token costs) and the impact of context quality on response utility, correctness, completeness, and tool fidelity. The post quantifies these benefits across various enterprise work categories and query complexities, showcasing the superiority of Glean's context layer in enabling AI to handle complex tasks efficiently and accurately.
  23. Best AI tools for software engineers: how to build the right AI stackThis post discusses the broader landscape of AI tools for software engineers, focusing on building the right AI stack. It highlights the challenges of "outcome gap," "trust gap," and "safety gap" in AI coding tools. It categorizes AI tools into coding assistants (Cursor, GitHub Copilot, Claude Code, Windsurf, Sourcegraph Cody) and engineering context/knowledge platforms. Glean is presented as a context layer, building an enterprise context graph over various tools (code hosts, Jira, PagerDuty, Datadog, Confluence, Notion, Drive, SharePoint, Slack, Teams). It emphasizes Glean's hybrid lexical + semantic search, permission-aware access, MCP server for IDE integration, and agents that convert tickets/threads into PRs. The post also touches on the evolution of AI coding assistants to incorporate organizational context, incident response, and governance.
  24. Is MCP dead? When to use MCP for enterprise AIThis post clarifies the role and value of the Model Context Protocol (MCP) in enterprise AI, distinguishing its utility at company scale from its potential overkill in local, single-user workflows. It argues that MCP, when implemented as remote MCP servers, provides essential enterprise capabilities such as centralized per-user auth and secrets management, org-wide telemetry and observability, standardized prompts/skills and docs across surfaces, and a unified integration surface for diverse AI hosts. The post contrasts this with simpler CLI or SDK wrappers, which are more appropriate for individual developers or small-scale projects. It emphasizes that MCP at enterprise scale addresses platform-level concerns like governance, observability, and consistent capability distribution, rather than just token efficiency.
  25. Knowledge graph vs vector database: how to choose your AI foundationThis post details the fundamental differences and complementary strengths of knowledge graphs and vector databases in the context of enterprise AI. It explains how knowledge graphs excel at representing explicit relationships, governance, and complex reasoning, while vector databases are suited for semantic search across unstructured data. The post argues for a hybrid approach, highlighting how Glean combines these technologies to provide explainability, robust governance, and effective retrieval. It contrasts the 'structure vs. semantics' and 'governance and safety' aspects, and discusses how each approach benefits specific workflow scenarios, ultimately advocating for a combined strategy to leverage the strengths of both.
  26. Not all enterprise context is created equal: Glean results are preferred ~2× more than ChatGPT and 1.6× more than ClaudeThis post details an evaluation comparing Glean's enterprise context retrieval capabilities against OpenAI ChatGPT's company knowledge and Anthropic Claude's enterprise search. It quantifies Glean's superior performance with a 1.9x preference over ChatGPT and 1.6x over Claude in human-graded correctness and completeness evaluations. The post explains the challenges of context rot in LLMs and contrasts Glean's approach (specialized indexes, Enterprise Graph, enterprise memory) with the 'deep' (sequential, in-depth) strategy of Claude and the 'wide' (parallel, skimming) strategy of ChatGPT. It highlights how Glean's architecture, rooted in enterprise search, effectively handles company-specific jargon, ambiguous queries, and technical questions by leveraging semantic search models and an Enterprise Graph. The evaluation methodology, including queryset design, dataset connection, and grading criteria, is also described.
  27. How do you build a context graph?This post details Glean's approach to building context graphs, which model the flow of work by connecting enterprise entities with temporal traces of actions and events. It explains how these graphs are constructed by transforming actions into first-class entities (nodes) and their causality/correlation into edges. The process involves deep connectors and observability, building a unified knowledge graph to infer higher-level entities and relationships, creating a personal graph to synthesize individual activity streams into timelines, and then normalizing these into anonymized, abstracted traces for aggregate analysis. The context graph is stored using a hybrid model of free-form text chunks embedded with entity IDs. The loop is closed by learning from agentic traces, which become new data points for reinforcing patterns and identifying anti-patterns, ultimately creating a joint model of human and agent behavior.
  28. Context is the next data platform—and why context graphs are key to understanding processesThis post details Glean's technical investment in building context graphs as a foundational component of its enterprise AI stack. It elaborates on the 'how' of capturing process reality by focusing on observability through connectors and apps, understanding low-level activity signals (discrete, timestamped actions), and deriving higher-level understanding of tasks and projects from these signals. The post highlights the challenge of separating signals from noise in enterprise data, noting an ~80% accuracy in task understanding and the algorithmic inference required due to privacy constraints. It also positions context graphs as part of a broader stack including connectors, indexes, and memory for agentic automation, emphasizing the six years of technical investment in this context platform.
  29. Glean and Vercel: Supercharging v0 with enterprise contextThis post details the integration of Glean's enterprise context into Vercel's v0 platform via the Model Context Protocol (MCP). It highlights how Glean's remote MCP server allows MCP clients like v0 to query Glean's unified, permission-aware enterprise graph, which comprises over 100 data sources. The integration enables v0 to generate AI-powered applications grounded in live company knowledge, including documentation, security policies, architecture decisions, product specs, and incident history. Specific use cases demonstrated include generating auth logic flows from internal documentation, drafting website copy based on product updates, and prototyping UIs from PRDs, all while respecting user permissions.