What’s so great about Glean, the leader in enterprise search?

1. Glean background: from Enterprise Search to Work AI Platform

    Glean was co-founded in 2019 by former Google senior search engineer Arvind Jain (current CEO), and the company’s core mission is to “ Expand human potential to do extraordinary work ”.

    Arvind Jain worked at Google for more than 10 years as an early engineer in the Google search field, and later co-founded the data security company Rubrik, which has now been successfully listed. Most of Glean’s core members have deep backgrounds in top technology companies such as Google Search, and this founding background has laid a solid foundation for Glean in the field of enterprise search technology.

    The original intention of Glean was based on Arvind Jain's personal experience at Rubrik - although the enterprise had a large number of cloud applications and data, employees faced great difficulties in finding the information they needed, and the problem of information silos seriously hindered work efficiency. He mentioned that even within Google, where he had worked, information search was a difficult problem. Therefore, Glean's initial positioning was to create an "enterprise version of Google Search", but due to the complexity of enterprise permissions and domain knowledge, it was far from being a simple copy of search technology.

    With the rapid development of this round of generative AI technology, Glean keenly captured this opportunity and quickly expanded its products from AI-driven enterprise search to an AI assistant (Glean Assistant) that can provide precise answers, and then developed into a comprehensive " Work AI Platform ".

    Work AI Platform not only helps employees find the information they need quickly and accurately in increasingly decentralized enterprise applications and data silos, but also helps employees generate content, automate tasks, and optimize workflows through Glean Assistant and Glean Agents. The company has quickly gained market favor with its founding team's deep background in search engines, advanced AI technologies (including knowledge graphs, search enhancement generation RAG, large language model LLM), and enterprise-level security and governance capabilities.

    At the just concluded Glean:GO 2025 conference , Glean founder and CEO Arvind Jain clearly put forward three core strategic directions to guide the company's future development:

1. Agents are for everyone
   

       Glean firmly believes that the power of intelligent agents should benefit every employee. By providing powerful agent building tools, everyone can create and use personalized intelligent agents to assist work and automate tasks. The ultimate goal is to enable every employee to become a " 10X -er".

2. Think Platform
   

       Faced with the possibility of tens of thousands of intelligent agents within enterprises in the future, Glean stressed the need to adopt a unified platform strategy, that is, to provide a horizontal, open AI platform for standardization and implementation of agent construction, management, and security assurance.

3. Context is King
   

       Glean believes that the key to an agent working efficiently and accurately in an enterprise is to have a deep understanding of the complete context of the enterprise. This includes not only understanding the people, projects, customers, data and their relationships, but also understanding complex business processes and how work is done. The enterprise knowledge graph that Glean has built over the past six years, as well as the newly launched personal graph, can provide the agent with crucial "context fuel".

    These three strategic directions clearly outline Glean's ambition to transform from a leading enterprise search provider to a core infrastructure provider for future enterprise intelligence. This strategic evolution not only enhances Glean's value proposition, but also enables it to participate more widely in the wave of enterprise AI applications and seize greater market opportunities.

2. Market and competition status

    Glean's ARR has tripled in the past year. Just over three years after its founding, it has achieved $100 million in ARR by early 2025 , making it one of the fastest-growing SaaS startups. With daily active users accounting for 40% of monthly active users, user activity also indirectly reflects the real value that the product brings to customers.

    In December 2024, Glean announced the completion of a round E financing of more than $260 million , with a company valuation of $4.6 billion . Glean has more than $550 million in cash on hand, and there have been recent market rumors that Glean may conduct a new round of financing, with a valuation of $7 billion. The overall development trend is still quite strong.

Market and Trends

    Glean is positioned in the fast-growing enterprise AI intelligent infrastructure (Enterprise AI Infra) track.  Its starting point and core capabilities lie in enterprise search (Enterprise Search), but it has evolved into an enterprise work AI platform (Work AI Platform) that provides AI assistants and AI agents.

    The enterprise search and knowledge management markets are undergoing a profound transformation driven by AI technology. The low efficiency of information retrieval within traditional enterprises and the widespread existence of knowledge silos are problems that have long plagued organizations. The global enterprise search market is estimated to be approximately US$5.3-5.5 billion in 2024, and is expected to grow to US$11.7-15.8 billion by 2033, with a compound annual growth rate (CAGR) of approximately 9.2%-12.5%. At the same time, the knowledge management software market is also showing strong growth. The integration of AI is a key driver of the growth of these two markets. Enterprises increasingly expect search tools to provide highly relevant and context-aware answers, and use AI technology to improve the efficiency of enterprise knowledge management and applications.

    On the other hand, Work AI is also rapidly moving from the experimental stage to practical application. According to the 2025 AI Index Report released by Stanford University HAI, the US private sector's investment in AI in 2024 will reach $109.1 billion, and 78% of organizations will use AI in 2024, a significant increase from 2023. Glean accurately positions itself as a "Work AI platform". Its core products, Glean Assistant and Glean Agent, can be deeply integrated into employees' daily workflows, providing intelligent search, answer generation, content creation, and task automation functions to help companies and employees improve productivity and efficiency.

Main competitors

    Although many new players have emerged in the field of AI Agent, Glean is currently in a relatively advantageous position due to its nearly six years of first-mover advantage in the field and its deep accumulation of enterprise-level capabilities (such as deep integration, strict permissions, data governance, etc.). This is also the reward for starting early and being willing to do the dirty work in the early stages.

    At the Glean:GO 2025 conference, many well-known corporate customers such as Rivian, Ericsson, Zillow, Deutsche Telekom, Reddit, Citi, Booking.com , Time Magazine, Uber, Databricks, CoreWeave and other executives attended and spoke in person, demonstrating Glean's penetration and influence in different industries. Among them, the case of Booking.com is particularly prominent. Its executives said that Booking employees rely on Glean to the extent of "like tap water" and widely use its AI capabilities to complete work, not just to find information. It seems that it has been integrated into the company's core business processes and employee workflows.

    Currently, Glean’s main competitors fall into several categories:

• Platform-level solutions from large technology companies : such as Microsoft's Microsoft 365 Copilot/Viva and Google's Google Cloud Search. The advantages of these large companies lie in their seamless integration with their own ecosystems and their large user base.

• Professional enterprise search and AI platform providers : such as Coveo, Elastic (Elasticsearch), Lucidworks Fusion, Algolia, etc. in the customer service and e-commerce fields. The advantage of these companies is that they have been deeply involved in the field for many years.

• AI search embedded in knowledge management/collaboration platforms : such as Guru, ClickUp, etc., which focus more on knowledge creation and maintenance, as well as information delivery in specific workflows

• Emerging AI-native search and question-answering engines : such as Perplexity Enterprise and You.com Enterprise. These companies are developing rapidly in integrating external network information and using GenAI to provide more diverse answers.

  
  

Glean's Difference :

    Glean regards OpenAI, Anthropic, Google and other basic model providers as important partners rather than direct competitors. Its core differentiation lies in:

• Deep enterprise integration and contextual understanding : Through more than 100 connectors, enterprise knowledge graphs and personal graphs, Glean can deeply understand the people, content, activities, business processes and authority relationships within the enterprise. This is the basis for Glean's repeated emphasis on the concept of " Context is King ".

• Complete evolution from Enterprise Search to Work AI Platform : Glean not only provides powerful enterprise search, but also has an AI assistant and the intelligent agent platform that was just announced to be GA at Glean:GO 2025, providing an AI work platform for employees across the entire business process of the enterprise, including raw data, domain information, business workflows, etc.

• Enterprise-level security and governance : Digital security and compliance are the most important enterprise-level requirements. Glean Protect provides comprehensive security guardrails, strictly enforces source application permissions, and provides options such as localized deployment.

3. Users and Products

user :

Glean’s core target users are all “ knowledge workers ”     in the enterprise , regardless of their specific roles (such as engineers, sales, HR, customer service, IT, finance, legal affairs, marketing, etc.).

    Glean believes that all knowledge workers are faced with the pain points of finding information, getting answers, and handling daily repetitive tasks. Glean's vision is to create a "truly personalized team of AI assistants, AI colleagues, and AI coaches" for every knowledge worker, with the goal of enabling everyone to become a "10X-er ".

product :

    Glean provides a comprehensive Work AI Platform that integrates search, AI assistant and agent functions. It provides employees with AI-driven personalized intelligence and automation capabilities by connecting and deeply understanding internal and external knowledge of the enterprise.

• Glean Search :

• Value : Help employees quickly and accurately find the information they need in increasingly dispersed enterprise applications and data silos, breaking down information barriers.

• Function : As the cornerstone of Glean, it enables secure, highly relevant and personalized search results through deep integration and enterprise knowledge graph (combined with personal graph).

• Glean Assistant :

• Value : Like an AI work partner that accompanies employees at all times, it helps them "understand information" and "generate content", thereby improving decision-making and content creation efficiency.

• Functionality : Glean Assistant can be simply described as "a more powerful and relevant enterprise version of ChatGPT". The Deep Research Agent demonstrated at Glean:GO 2025  can generate comprehensive research reports containing internal company knowledge and web page information in minutes. The Analytics Agent  is able to query structured data through natural language and combine structured insights with unstructured content to provide a comprehensive analytical view.

• Glean Agents :

• Value : Apply AI to actual workflow automation, evolving from simple information assistance to a task execution partner, significantly improving enterprise operational efficiency and employee productivity.

• Function : Glean Agents was officially announced as GA at Glean:GO 2025. It is an open, horizontal platform for building, deploying, orchestrating and governing intelligent agents in enterprises. Glean:GO 2025 showcased a variety of agent cases, such as daily summaries, meeting reviews, self-assessments, customer snapshot generation, sales lead cultivation email writing, work order timeline generation, new feature design document generation, HR process automation, etc., fully demonstrating its wide application potential. Currently, the number of intelligent agent actions executed on the Glean platform has exceeded 100 million times per year, and is growing at a rate of 10 times per year.

Business Model:

    Glean mainly adopts SaaS (Software as a Service) business model:

1. Subscription Fee:  Glean's basic pricing typically starts at $30 per user per month, and for large enterprises that need more advanced features or integrations, the price may exceed $50 per user per month. Its advanced AI assistant features related to Work AI may add an additional fee of about $15 per user per month. It is said that Glean's minimum annual contract value (ACV) threshold is about $50,000, the median is around $65,000, and some large contracts are worth hundreds of thousands of dollars.

2. Support Fee:  Typically 10% of Annual Recurring Revenue (ARR) and is non-removable.

3. Implementation and settlement service fees:  including consulting, technical configuration and training, etc. These costs may not be clearly listed in the early stage and may result in extra-budgetary expenditures.

4. Paid POC Trial:  For enterprises that want to fully evaluate Glean's performance in a real data environment, they may need to pay up to $70,000 for a POC. Paid POC is a good way to filter out some customers who just want to try it out.

   
   

    Glean doesn’t typically offer multi-year contracts, but it maximizes value by allowing the sales team to tailor plans and prices based on the client’s specific needs and perceived value.

    In addition, the Work AI feature is charged extra ($15 per user per month) and is becoming a core part of the platform. The bundling of Work AI Platform features indicates that Glean's strategy is to gradually increase the average revenue per user (ARPU) as customers adopt more advanced AI features and rely more on the platform. The mainstream charging model for Agents in the future will be outcome-based pricing.

4. Technical Challenges

    Glean has gradually evolved into a complete technology system around its core concepts of "Context is King", "Think Platform" and "Agents are for everyone".

    Glean is not a simple "LLM Wrapper + RAG", but has done a lot of work in data, intelligence, application interaction, security governance and open interoperability.

Layer 1: Data layer - Overcoming the "last mile" of connectivity and building a "live" knowledge graph

• Difficulties of extensive data connectivity:

  
  

    Data connectivity within an enterprise is a prerequisite for the success of AI applications, but it also faces huge challenges:

• Extreme heterogeneity and dynamic complexity : Enterprises have many application systems (documents, messaging, CRM, HRM, code bases, custom applications, etc.), different APIs, data models, and permission systems. Glea provides more than 100 data connectors that can synchronize these heterogeneous permissions in real time. Building these connectors and unifying data and permissions is the first step in building a search product and is also part of the core technology stack.

• Identity fragmentation and unification : The same user may use different usernames or email addresses in different applications, so it is crucial to build a unified identity graph that can accurately link these decentralized identities  , otherwise permission mapping will be out of the question. Eddie Zhou mentioned that Glean has invested a lot of effort in building internal identity services to accurately map user identities in different systems to a unified "person" node.

• Data depth and "signal" capture : Effective AI requires not only content, but also understanding the "signals" behind the content - metadata, version history, access frequency, comments, likes, shares, modification records, collaborative relationships between users and other activity data. Glean's connector is designed to deeply ingest these "behavioral signals", not just text. These "signals" are crucial to understanding the authority, popularity and timeliness of documents , which is the enterprise's advanced domain data.

• Real-time and consistency : Information and permissions change dynamically within an enterprise. Glean emphasizes that its connector supports real-time or quasi-real-time synchronization , especially permission changes, which must be reflected immediately in search and AI access control to prevent data leakage. This is a very difficult engineering problem.
  

• Scaling challenges : Large enterprises have a huge amount of data. Efficiently and stably indexing and updating this data while maintaining high-performance retrieval is a great test for the system architecture. Glean has taken scalability as a core design principle since its inception, with the goal of handling the hundreds of millions of documents that large multinational enterprises may have.
  

    Glean has overcome these difficulties through years of continuous investment and engineering optimization, and has made "connection" itself a core competitiveness. Its "out-of-the-box (Turnkey)" feature also shows its efforts in simplifying deployment and configuration.

• The long-standing problem of building enterprise knowledge graphs

  
  

      The pain points of traditional enterprise knowledge graph construction are the fragmentation of data sources, the difficulty of relationship extraction, the lag in graph updates, and the lack of close integration with practical applications. Glean claims to be able to effectively build and utilize enterprise knowledge graphs. Its uniqueness lies in:

• Dynamic construction driven by "live data" : Glean's knowledge graph is not based on static rules or one-time imports, but is built and updated in real time by the three pillars of "content", "people" and "activity" that continuously flow in through connectors. Every search, every click, and every collaboration of the user contributes new "edges" and "node weights" to the graph.

• "Conceptual understanding" beyond text matching : Glean automatically discovers and extracts key entities (such as projects, products, customers, technical terms, etc.) and their relationships from massive data , and performs effective disambiguation and normalization. The construction of the knowledge graph goes beyond text matching to understand "concepts" in the context of the enterprise, even if these concepts appear under multiple names in different documents, messages or queries.
  

• Personal Graph : This is a major innovation of Glean. The corporate graph depicts the knowledge structure and collaboration network at the company level, while the personal graph released at Glean:GO 2025 builds a unique "personal graph" for each employee based on the corporate graph to understand their projects, concerns, common tools, collaboration objects, and communication styles. The integration of corporate graphs and personal graphs enables AI to provide personalized services that truly "understand you", greatly improving practicality and user stickiness.
  

    It can be said that Glean’s knowledge graph is a “living” system that is “fed” by real business activities, continuously evolving, and deeply personalized.

Layer 2: Intelligent layer - customized models, featured RAG enhancements, and innovations in intelligent agent reasoning engines

• Glean's model strategy: Beyond "shelling" to "efficiency enhancement"
  

      Glean does not simply apply external large models (such as the GPT series) directly to enterprise data. Glean cooperates with all mainstream LLMs (OpenAI, Anthropic, Google, Meta and open source models), but the core strategy is to build a very deep technology stack on top of these basic models and continue to move upstream in the value chain. When the basic LLM has some capabilities that Glean previously developed, Glean will choose to abandon the self-developed part and instead use more basic capabilities, focusing on higher-level value creation and deep adaptation to enterprise scenarios.

  
  

  The "incremental value" of its large and small model combination strategy is reflected in:

• Fine-tuning Embedding Models for Enterprise RAG : This is one of Glean's core technical investments. They use enterprise-specific data to deeply optimize the Embedding Model, which is crucial to the retrieval process , so that it can more accurately understand the jargon, abbreviations, project codes, and context within the enterprise, thereby greatly improving the recall and accuracy of retrieval in RAG.

• Customized small models or knowledge distillation for specific tasks : Glean mentioned that he would train customized small language models or fine-tune existing models to understand the internal language of the enterprise. When the application reaches a certain scale and cost becomes an important factor, a  smaller, faster, more accurate and lower-cost specialized model can be obtained through model distillation .
  

• LLM-agnostic architecture and intelligent routing : Glean's model-centricity and the ability to "select models at each step" in the agent reflect the flexibility of its architecture. This means that Glean can intelligently select or route to the most appropriate LLM for processing based on task characteristics, cost, and performance requirements, rather than being bound to a single model. Glean believes that there will be a large number of models coexisting in the future, and Glean's responsibility is to help customers choose the most appropriate model technology for specific use cases. I personally agree with this.

  
  

• Glean's RAG: A unique practice that deeply integrates "context" and "authority"

      RAG has become the mainstream paradigm for enterprises to apply LLM. The special features of Glean's RAG are that it is efficient and reliable:

• Super powerful retrieval driven by "dual graphs" : Glean's RAG not only relies on vector similarity search, but also deeply combines its enterprise knowledge graph and personal graph for contextual retrieval. The retrieval process can understand more complex semantic relationships, entity links, and user preferences to find truly relevant contexts. Technically, Glean's RAG system will simultaneously use keyword search, semantic search, and structured information of the knowledge graph during the retrieval stage to perform multi-way recall and intelligent sorting to ensure the comprehensiveness and relevance of the retrieval results.

• Security assurance of “authority penetration” : Before feeding the retrieved information to LLM, Glean strictly enforces the complex and dynamic permissions defined in the source application . This is the core guarantee of enterprise-level RAG security, ensuring that LLM will not access data that the user is not authorized to access.
  

• Intelligent query planning and rewriting : In the "Plan" stage of RAG, Glean will use LLM to understand, rewrite or decompose the user's original query to better adapt to the diverse data sources and knowledge structures within the enterprise. This step is basically available in RAG systems, but it is not certain whether Glean's rewriting also incorporates the enterprise-specific contextual information.

  
  

• Agentic Reasoning Engine 

      Glean's agent reasoning engine is the brain of its agent platform, responsible for driving agents to perform complex tasks. Its technical highlights are:

• Dynamic planning and adaptive execution : The engine can expand from simple single-step operations to multi-step workflows that require deep reasoning and planning according to the complexity of the task. It supports advanced logic such as conditional branching, loops, parallel processing, and sub-agent calls. It is said that the Agent can execute a complex business process that may contain hundreds of steps.

• Deep context dependency and intelligent tool orchestration : The reasoning process is deeply dependent on the real-time context provided by enterprise and personal knowledge graphs . It can intelligently select and combine available enterprise tools, such as Glean search, analysis, enterprise application APIs, etc., to achieve the goal in the most efficient way.
  

• LLM scheduling optimization for effect and cost : Through the flexibility of "selecting models at each step", the inference engine can match the most suitable LLM for different links in the workflow, such as understanding intent, data extraction, logical judgment, content generation, API calls, etc., thereby optimizing costs and response speed while ensuring effect. Glean's ability to finely manage LLM resources is worth learning from.

  
  

Layer 3: Application and interaction layer - Agent Builder is available to everyone

    The goal of Glean Agent Builder is to make the creation of AI agents truly accessible to everyone. You don’t need much technical background to use Agent Builder to create your own applications. Its technical highlights include:

• Ultimate ease of use
  

      Users can directly describe the task objectives in natural language to quickly generate a draft of the intelligent agent , or they can fine-tune the logic, data source, LLM called, and action executed at each step through a drag-and-drop visual interface (visual builder)  . This dual mode takes into account both the needs of quick start and deep customization, significantly lowering the threshold for AI applications.

  
  

• Unique advantages inherent in “enterprise context”
  

      The intelligent agents built by Glean Agent Builder are naturally and securely rooted in the deep enterprise context provided by the Glean platform's unique enterprise knowledge graph and personal graph . This means that when performing tasks, the intelligent agents can automatically understand the relevant project background, personnel roles, historical communications, data permissions, etc. This is a core advantage that general agent building platforms cannot match.

      During the execution of the Agent, Glean's technical director mentioned the concept of "Context Injection". I have a similar view: loading the most appropriate domain context information on demand and in real time is the key to improving the Agent's understanding and decision-making quality.

  
  

• Integrated enterprise-level platform support
  

      Unlike many independent agent frameworks or tools, Glean Agent Builder is part of its complete WorkAI platform . This means that the design, construction, testing, deployment, operation, monitoring and governance of the agent are all completed in a unified, secure and compliant enterprise-level environment, seamlessly integrating Glean's powerful search, RAG, permission management and Glean Protect's security protection capabilities.

  
  

• Emphasis on "Actionability" and enterprise application integration
  

      The core of Glean's intelligent agent design is to perform actual enterprise operations, rather than just the transmission or generation of information . Its ever-expanding action library  (from dozens to hundreds of targets) and deep integration with core enterprise applications (such as Salesforce, Jira, Workday, etc.) enable the intelligent agent to truly drive the automation of business processes.

  
  

• “Business people become builders (Citizen Developers)” driving force of organizational change
  

      By greatly lowering the technical threshold, Glean Agent Builder is expected to spawn a large number of "business developers" within the enterprise. They understand the pain points and optimization points of their own work best and can quickly build a large number of "small and beautiful" practical intelligent entities, thereby promoting the enterprise's AI transformation and efficiency improvement from point to surface.

Layer 4: Security and Governance - Addressing the core concerns of enterprises and in-depth consideration of localized deployment

    When enterprises embrace AI, data security, privacy protection and compliance are their primary concerns.

    Glean emphasizes that its products are "secure by design" . The Glean platform has met multiple industry security and compliance standards, including SOC 2 Type II certification, GDPR and HIPAA.

    Glean's key safety features include:

• Authentication and authorization:  All access must be authenticated through the company's identity provider (SSO is supported), and permission control is strictly enforced to ensure that users can only see the information they are authorized to access in the source application. Permission changes are synchronized to Glean in real time.

• Data encryption and isolation:  Customer data is encrypted using AES 256-bit (FIPS 140-2 validated encryption module) at rest and TLS 1.2+ when in transit. The platform adopts a single-tenant model, customer data is stored in its own VPC, and cloud or local deployment options are provided.

• Index Control and Auditing:  Enterprises can control the scope of data that Glean crawls and indexes, and can configure comprehensive audit logs and export them to SIEM systems for monitoring.

    On this basis, Glean also launched more advanced security solutions:

• Glean Protect:  This is a functional suite that helps enterprises safely apply AI at scale, including preparing data for AI and protecting autonomous agents. Its functions include protection against LLM attacks such as prompt injection and jailbreak.

• Proactive Data and AI Governance:  The framework continuously scans over 100 connected enterprise applications, automatically detecting and remediating over-sharing of sensitive data and flagging potential permission misconfigurations to administrators.

    Glean's multi-layered security and governance framework, with Glean Protect and active data and AI governance as its highlights, effectively addresses enterprises' primary concern about data security in the AI ​​era and establishes a baseline of trust.

Layer 5: Open Operation Layer - Building a differentiated ecosystem with " Contextual Intelligence " as the core

    Openness and interoperability have become standard for AI platforms in the enterprise market, because there are too many other systems that need to be integrated. Glean's ecological strategy has chosen openness:

• Unique "Contex-as-a-Service"
  

  Glean provides its unique, deeply processed and permission-controlled enterprise contextual information     through its comprehensive APIs, such as search, dialogue/assistant, agent API and MCP Server, which are open core capabilities . The "contextual intelligence" carried by its enterprise knowledge graph and personal graph is a highly valuable and differentiated asset that Glean ecosystem partners can use.

  
  

• "Enterprise System of Context Provider" positioning
  

      Glean's strategic positioning is to become a systematic context provider for enterprise AI applications. This means that Glean is committed to making its platform a link between various AI models, various AI agents and the massive, heterogeneous, and complex data and knowledge within the enterprise.

  
  

• Embrace open standards
  

      Glean not only announced its support for MCP, but also hosted the MCP server and actively participated in the A2A protocol of Google, Agent Protocol of LangChain, and Agency and other protocol standards promoted by industry partners such as Cisco. Glean's goal is to achieve true bi-directional agent orchestration , so that Glean's agents can call external tools, and external agents can also call Glean's capabilities. This decision is very visionary.

    Glean's open ecosystem strategy is essentially to use its core "contextual intelligence" as a platform service , thereby occupying a unique and indispensable ecological niche in the enterprise AI ecosystem.

5. Hard Questions for Glean

    Although Glean is currently developing well and has a good position in the entire ecosystem, there are still some key issues that will face uncertainty in the future if they are not resolved properly.

    Question 1: AI basic models are changing with each passing day. How can we maintain continuous innovation and value at the application layer?

    Glean CEO Arvind Jain has clearly stated that Glean's model is to "maximize the industry innovation of LLM, but at the same time build an important value layer on it and continue to move up the value chain as the underlying capabilities improve ." Glean focuses on deep optimization of enterprise-specific scenarios , context injection, permission control, workflow and agent orchestration capabilities , LLM-independent flexibility and cost optimization , etc., which is a very pragmatic choice. 

    Question 2: How to solve the problems of difficulty in building enterprise knowledge graphs, high maintenance costs, and difficulty in scaling?

    Glean's practical approach is the pragmatism of "using to promote construction" , the dynamic nature of "signal-driven" , the deep personalization of "dual maps" , and the engineering ability to embrace "imperfect data" , which are still pragmatic practices.

    Question 3: In the enterprise scenario of professionalism and reliability, how to solve the reliability, controllability and "black box" problems of intelligent agents?

    Glean emphasizes "observability" and "debugging capabilities" , built-in enterprise-level security and governance framework, structured intelligent agent construction and orchestration , gradual autonomy improvement and manual supervision. Glean's intelligent agent is currently more of a "co-pilot" mode of "enhancing humans" rather than pursuing completely unsupervised "autonomous driving", which is a more pragmatic and responsible path in the enterprise environment.

    Question 4: How to solve the security and trustworthiness challenges faced by enterprise data under its extreme complexity and diversity?

    Glean's innovation lies in its deep understanding of the enterprise's permission system and its "pixel-level" restoration and execution capabilities , including real-time combination and dynamic changes of multiple complex permissions such as ACL, group inheritance, directory structure, link sharing, etc. At the same time, Glean deeply integrates this permission perception capability into every link of data indexing, knowledge graph construction, RAG retrieval, and intelligent agent execution. This is a governance idea of ​​" security left shift " that runs through the entire life cycle of AI applications.

  Question 5: How to balance standardization in an open ecosystem and the “last mile” customization challenge?

    Glean not only supports existing standards such as MCP, but also actively participates in and promotes the formulation of new standards , striving to gain a voice in cutting-edge fields such as agent interoperability. Its methods such as hosting MCP servers are also lowering the threshold for ecological partners to access and use the core capabilities of the Glean platform, and actively assume some responsibilities as "eco-builders". Glean's goal is to enable its "Contextual Intelligence" to seamlessly enable various AI applications and agents inside and outside the enterprise in a standardized way, solving the "last mile" problem of AI landing in the enterprise. This is a very critical capability of a platform product.

6. Key Takeaways

    Personally, I think that Glean’s rapid development in the past few years and its three major strategies clearly defined in Glean:GO 2025 demonstrate that the company still has great potential and is worthy of study and continued attention.

• “Context is King” is the soul of enterprise AI
  

      As general big model capabilities become increasingly popular, simply relying on the intelligence of general big models is no longer enough to constitute core competitiveness. Glean has a deep insight that the real value of enterprise AI lies in the deep integration of powerful AI capabilities with the enterprise's specific, complex and dynamic internal "Context". This includes a comprehensive understanding of organizational structure, business processes, project details, personnel relationships, communication habits, implicit knowledge and even personal work preferences.

      The enterprise knowledge graph and the newly launched personal graph that Glean spent six years to build are the key to building this "Contextual Intelligence" moat. With context, AI can truly "come alive" and transform from a "toy" into an indispensable "partner".

  
  

• “Agents for Everyone” + “Think Platform” is a pragmatic path to ignite a revolution in corporate productivity

      Glean not only sees AI agents as a technology, but also as a platform strategy to empower all knowledge workers and reshape the way they work. Through the no-code/low-code agent builder, Glean lowers the threshold, allowing employees who understand the business best to personally create and deploy agents that solve practical problems and achieve universal access . This will not only unleash the automation potential of a large number of "long-tail" scenarios, but also stimulate employees' creativity and accelerate the popularization and cultural recognition of AI within the company. The case of Booking.com shows that when employees can easily use and benefit from it, AI tools will quickly integrate into daily work.

      At the same time, Glean provides a horizontal, open, and unified AI agent platform for building, managing, protecting, and purchasing various agents within the enterprise. This platform-based thinking helps enterprises avoid fragmentation and duplication of AI applications, achieve economies of scale, and lay the foundation for more complex collaboration between agents in the future with the help of open protocols such as MCP and A2A.

  
  

• Solving the "dirty and tiring work" is the "slow work" to build a real technological barrier

      Glean chose a path of "difficult first, easy later". While many companies were eager to show off all kinds of cool demos, Glean quietly invested a lot of energy to solve basic problems such as enterprise data integration, massive heterogeneous data indexing, dynamic and complex permission management, and knowledge graph construction.

      These tasks are technically difficult and slow to produce results, and may not be so "sexy" in the short term, but they form a solid foundation for Glean to safely and reliably drive AI agents today. This willingness to tackle tough problems and solve the complexity of the real world often has more lasting business value and technical barriers than simply pursuing the leading edge of algorithm models.
  

  
  

  
  

     In short, Glean's strength lies in its deep understanding of the essence of enterprise intelligent transformation, its choice of a difficult but effective path, and its implementation of a large number of "things that don't scale". 

    Glean's technology implementation path and way of coping with challenges reflect its deep understanding of the real needs of enterprise scenarios and its pragmatic style of " solving basic problems first and then adding intelligent applications ". Glean's innovation is more about combinatorial innovation and deep optimization innovation for enterprise scenarios, rather than pursuing breakthroughs in a single algorithm model. This continuous investment in "dirty work" and the ultimate pursuit of "enterprise context" constitutes its core barrier that is difficult to be quickly replicated in fierce competition.