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?

🧠 5 Knowledge Management Pain Points — and How Whelp AI Solves Them In large organizations, knowledge is everywhere, but finding the right piece at the right time: that’s the real challenge. From buried documents to siloed systems, enterprises lose time, clarity, and momentum every day. Here are the top 5 pain points in enterprise knowledge management — and how Whelp AI turns each one into a strategic advantage. 1. 🔍 Scattered Information Across Tools The Problem: Documents in Google Drive, policies in Notion, conversations in Slack, spreadsheets in Excel — and no single place to search across them all. How Whelp AI Helps: Whelp connects to your existing tools and creates a unified, conversational interface. Employees can ask questions like: “What’s our Q3 pricing strategy?” “Show me the onboarding checklist for new hires.” And Whelp pulls the answer from wherever it lives, instantly. 2. 🧱 Knowledge Silos Between Teams The Problem: HR has one system, Finance another, Legal a third. Valuable insights stay locked inside departments, slowing collaboration and decision-making. How Whelp AI Helps: Whelp builds a knowledge graph that links concepts, documents, and decisions across teams. It breaks down silos by making institutional knowledge searchable and shareable, without changing how teams work. 3. 🕰️ Time Lost Searching for Answers The Problem: Employees spend hours each week hunting for information — asking colleagues, digging through folders, or recreating work that already exists. How Whelp AI Helps: Whelp turns search into conversation. Instead of keywords, employees ask questions in plain language and get contextual answers. It’s like having a smart teammate who knows everything your company knows. 4. 🧓 Tacit Knowledge Walks Out the Door The Problem: When experienced employees leave, their insights often leave with them. Tacit knowledge — the stuff that’s never written down — is hard to capture. How Whelp AI Helps: Whelp surfaces insights from conversations, decisions, and documents over time. It builds a living memory of your organization, so knowledge isn’t lost — it’s preserved, searchable, and reusable. 5. 📉 Low Engagement with Knowledge Systems The Problem: Traditional knowledge bases are clunky, hard to navigate, and rarely updated. Employees don’t use them — and don’t trust them. How Whelp AI Helps: Whelp is built for natural interaction. It feels like chatting with a colleague, not querying a database. And because it’s integrated into tools teams already use (like Slack and Notion), adoption is frictionless. 🚀 The Bottom Line Whelp AI doesn’t just organize your knowledge — it activates it. By turning fragmented data into intelligent dialogue, Whelp helps every employee move faster, make smarter decisions, and stay aligned. Ready to chat with your data? Whelp AI makes enterprise knowledge searchable, conversational, and always available.

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

The question for today’s law firms and in-house teams is no longer whether to adopt knowledge management (KM) systems, but whether they can survive without them. As legal problems become increasingly complex and clients demand greater value for money, the firms that fail to capture, structure, and redeploy their collective expertise are poised to fall behind and struggle to "ground" AI desires of their lawyers. Knowledge, once the preserve of individual lawyers or native knowledge or hard yards knowledge bases, must now be institutionalized more then ever before. KM systems are not auxiliary to practice; they are practice. They embody what the firm knows and how it solves problems, and increasingly, they form the bedrock for technologies like trusted knowledge search, document automation, predictive analytics, and generative AI assistants. If a legal problem has been solved before, it should not require a lawyer to solve it again. Instead, we should ask, “What is the most efficient and effective way to resolve this issue?” If the answer is a knowledge library, a "vault", database, a decision tree, or a machine learning model, a question and answer search, then so be it, it will probably be a combination of all of these talking to each other. In this new world, the firms that thrive will be those that redefine themselves - not as collections of individuals, but as knowledge businesses, deploying human and digital expertise in tandem. The end of lawyers? Probably not. The beginning of new kinds of legal professionals - technologically fluent, knowledge-driven, and innovation-led - is already underway. And knowledge professionals are driving this forward with more demand at their doors than ever before, especially as we move from experimental to institutional.