Knowledge Management Platforms

Your beautiful, precise, painstakingly-crafted documentation is dying a slow death. And it's not your fault. It's your platform. Think about it. We spend weeks perfecting clarity, but users can't find the right article because the search function is a relic from 2005. We fight for accuracy, but have zero data on whether a single page is actually helpful, or if it's creating more support tickets. We're told docs are crucial for retention, yet they're hosted on a platform that looks and feels nothing like the actual product. It’s a disconnected content graveyard. And we're the only ones attending the funeral. In 2025, elite doc teams aren't just "writing docs." They're building documentation experiences. Their platforms are no longer static wikis, but dynamic knowledge engines that actively drive user success. This means: → AI search that understands intent, not just keywords. → Recognizing your docs are read not just by humans, but by LLMs as well. → Real analytics that connect content to user behavior and success metrics. → Interactive so your users can ask questions there, not just sterile text. → Seamless integration so help feels like part of the product (think a drop-in widget), not an afterthought. It’s time to stop treating docs as a chore and start treating them as a product. Your documentation platform isn't just a CMS. It's the UI for your company's knowledge. Product teams wouldn't ship a UI with a broken search, zero analytics, no regards for LLMs and a clunky interface. Why should we? The gap between standard doc platforms and what's now possible is massive. We have the tools to prove our value and transform docs from a cost center into a growth engine. This is why we've built Archbee (YC S21) — a next generation docs platform to fix all the problems presented above. But... I have to ask all the brilliant tech writers & doc managers out there: What's the #1 feature you wish your documentation platform had, or the #1 limitation you'd eliminate tomorrow? Drop it in the comments. Let's see the patterns.

Swiss Post’s Enterprise Metadata Strategy is a Blueprint to build an “Enterprise Brain”, so your organization can actually know what it knows. This week, Tim Gasper and I visited the Swiss Post, a data.world customer, and we were blown away by the execution of their ambitious enterprise metadata strategy. It goes far beyond a data catalog for data and analytics (search for data, having data lineage, etc, that’s all table stakes) They’re treating metadata as the backbone of their enterprise intelligence by building an “Enterprise Brain” that ✅  Helps people find experts and institutional knowledge enabling serendipity and avoid wasting so much time ✅  Enables business impact analysis, knowing what happens if there is a system changes to prevent and quickly identify issues ✅  Accelerates application development by shifting “knowledge left” ✅  Establishes a semantic foundation for AI, ensuring LLMs work with real enterprise context This is a clear example of what is possible with a true data catalog powered by a knowledge graph.  What is really impressive is that in less than one year, they’ve integrated metadata from: 📌 Enterprise Architecture Management Systems 📌 Business Process Management Systems 📌 Technical Data Catalogs 📌 Workforce Management Systems 📌 Relational Databases 📌 100,000+ business glossary terms across four languages They have been able to extend the ontology themselves, with no bottlenecks and roadblocks by data.world. Just a powerful, flexible data catalog and governance platform built on a knowledge graph, that scales as they need it. This truly exemplifies what is possible with knowledge graphs and so proud to see them doing this. This is the kind of innovation that redefines data catalogs and pushes the data industry to think bigger. Honestly, implementing a data catalog to manage data lake/warehouse, transformations, dashboards, data products is barely scratching the surface. Adrian Meyer, the enterprise data architect has had this vision for a long time. I’m lucky that our paths crossed many years ago, that I get to learn so much from him, get to work with him now and make our shared vision a reality now. The CTO Fabien Delalondre has a bold vision that leverages the knowledge graph for AI innovation. I’m incredibly lucky that I get to work with so many smart people who are transforming our industry. I’m thrilled to see this real world implementation, execution and impact of my personal vision of integrating data and knowledge at scale through knowledge graphs. This is also personally exciting. Switzerland is another home to me. I finished high school in Switzerland and my first startup was based out of Zurich. Switzerland holds a special place in my heart and it’s an honor to contribute to improving the quality of services provided by the Swiss Post, which impacts every single Swiss citizen. Are you thinking about metadata at this level? Or is your catalog still just a list of datasets?

Most companies treat “knowledge management” like a filing cabinet. Centralize everything → hope people find it. But doing it well is very different. It’s not just about managing knowledge—it’s about: 1/ Managing diverse content types across departments – Support writes post-sales content, Marketing creates pre-sales content, Product ships product documentation and release notes, HR builds onboarding and employee enablement, Sales shares playbooks and sales resources, Channels shares partner enablement content. Each team produces content in a different format. 2/ Distributing knowledge and content across touchpoints – Knowledge and content are needed in customer portals, support pages, AI assistants, in-app help, CRMs, ticketing systems, intranets, partner portals, and even sales and service partner's websites, portals, and ticketing systems. 4/ Designing experiences for specific audiences – What customers expect in self-service is different from what employees need on an intranet, or what distributors need in a portal. Each application must feel purpose-built to the user, but be created and evolved using nocode by the creator. 5/ Capturing feedback & measuring performance – Customers downvote unhelpful articles, partners flag missing details, employees leave comments. Without feedback loops, content gets stale and trust erodes. 6/ Blending traditional and AI-based experiences – Some users prefer to search, filter, and browse while others want to have a conversation with an AI Assistant, and often the best solution is both working together. The result isn’t a “central library.” It’s a living system that: - Bridges departments and silos - Serves every audience where they are - Improves with each interaction That’s how knowledge can be leveraged to help customers, partners, and employees be more successful with your products and services. #knowledgemanagement, #customerenablement, #partnerenablement, #employeeenablement, #AI, #selfservice

Continuing with the GenAI series, I am excited to share how we revolutionised the knowledge management system (KMS) for a leading client in the manufacturing industry. R&D teams in manufacturing often face the tedious task of manually sifting through complex engineering documents and standard operating procedures to ensure compliance, uphold safety standards, and drive innovation. This manual process is not only time-consuming but also prone to errors. To address this, we collaborated with our client to automate their R&D function’s KMS using Generative AI (GenAI). By allowing precise querying of specific sections of documents, our solution sped up access to critical information, reducing search time from hours to mere seconds. Our Generative AI team processed over 110 R&D-related documents, leveraging Large Language Models (LLMs) to generate accurate responses to complex queries. Hosted on a leading cloud platform with an Angular-based UI, the solution delivered remarkable benefits, including: - Significant accuracy in generated answers - Faster and more accurate data search and summarisation - Enhanced decision-making with easier access to critical R&D information - Improved overall employee productivity By implementing GenAI for knowledge management, the client's R&D function was also able to improve its competitive edge by tracking and responding quickly to market trends and consumer behavior. With plans to scale the solution to process over 1,500 documents across multiple departments, the client is creating a centralised hub for all their information needs. Taking advantage of GenAI can revolutionize knowledge management by delivering the right information to the right person on demand and enabling strategic impact. #GenAI #ManufacturingInnovation #KnowledgeManagement #GenAIseries #GenAIcasestudy #Innovation #R&D #DigitalTransformation #AI #Deloitte

For enterprises Knowledge as a Service (KaaS) is getting crucial for AI readiness. The knowledge layer needs to sit on top of existing enterprise systems, making organizational knowledge accessible, maintainable, and AI-ready while preserving existing operational capabilities and governance. Let me try to bring clarity to KaaS Knowledge Discovery and Mapping Map all operational databases and their relationships Identify data warehouses and their current analytical models Document unstructured data sources (documents, emails, process documentation, pictures, videos etc.) Catalog existing business intelligence reports and dashboards Knowledge Flow Analysis Map how data flows between different systems Identify key business processes and their data dependencies Document decision points that require knowledge access Knowledge Structure Development Categorize data based on business context and usage Identify critical knowledge areas and their relationships Create taxonomy for organizing enterprise knowledge Establish metadata framework for knowledge assets Knowledge Model Creation Design knowledge graphs connecting different data sources Create semantic relationships between business concepts Develop ontology for business domain knowledge Map data lineage across systems Technical Implementation Deploy knowledge management platform Implement connectors to operational databases and data warehouses Set up real-time data synchronization mechanisms Create APIs for knowledge access and retrieval Processing Pipeline Develop ETL processes for knowledge extraction Implement AI-powered categorization systems Create automated tagging and classification workflows Set up validation and quality control mechanisms Knowledge Transformation Enrich operational data with business context Create relationships between different knowledge components Implement version control and lifecycle management Integration Layer Connect knowledge platform with existing BI tools Enable knowledge discovery through search interfaces Implement role-based access control Create audit trails for knowledge usage AI Readiness Knowledge Componentization Break down complex information into AI-digestible components Create training datasets for AI models Implement RAG (Retrieval Augmented Generation) capabilities Develop knowledge validation workflows AI Integration Set up AI models for knowledge processing Implement machine learning for continuous improvement Create feedback loops for knowledge refinement Enable automated knowledge updates Operational Excellence Monitoring Setup Implement usage tracking and analytics Create performance dashboards Set up alerting for knowledge quality issues Monitor system performance and utilization Governance Implementation Establish knowledge management policies Define roles and responsibilities Create maintenance procedures Implement compliance controls #GenerativeAI #EnterpriseAI #LLMIntegration #AIImplementation #Innovation

I’m thrilled to introduce the #KnowledgeCatalog. It’s not just a directory; it’s the "brain" for your enterprise AI. It’s designed to provide your AI agents with the trusted context they need to perform at scale by focusing on three pillars: 🔹 Aggregation: Unifying native context across Google, Partner datastores, and Operational platforms (Salesforce, SAP, ServiceNow, and more). It’s a single, governed source of truth. 🔹 Enrichment: It doesn’t just store data; it understands it. With continuous enrichment and new "Smart Storage" capabilities, it turns unstructured data—like PDFs and images—into usable schemas that your AI can actually understand. 🔹 Search & Retrieval: It’s not just about finding data; it’s about finding the right data at speed. Using hybrid search (semantic + lexical) and native access controls, it ensures agents get the context they need without compromising security. The era of agents flying blind is over. It’s time for an enterprise-wide context layer. Stop the silos. Start building a unified, intelligent enterprise. Please read the more in my blog co-authored by Sam McVeety https://lnkd.in/gjKTJR7d #GoogleCloud #GenAI #DataAnalytics #AIagents #KnowledgeCatalog #DataStrategy