KnowledgeForge: Turning Enterprise Chaos into a Living Knowledge Graph 🧠

Modern enterprises are overwhelmed by vast amounts of unstructured data from emails, documents, and more. This valuable information is typically siloed, making it difficult to search and connect. KnowledgeForge addresses this by acting as an AI-powered knowledge management platform. It transforms unstructured content into a queryable knowledge graph, allowing natural language exploration of organizational knowledge. The platform combines LLM understanding, graph-based modeling of entities like people and topics, and natural language access. Key capabilities include intelligent entity extraction from all content types. These extracted entities then form a living knowledge graph, showing relationships between people, topics, metrics, and time. KnowledgeForge offers semantic search that understands meaning, going beyond simple keyword matching. Its conversational AI assistant, KB Genie, traverses this graph and uses vector search to provide contextual answers with evidence. A significant differentiator is its integration with Denodo, enabling it to query structured data sources as well. The ingestion pipeline processes content, extracting and organizing information into relational and vector databases. When a user asks a question, the system traverses the graph, performs semantic searches, queries structured data, and synthesizes a final answer. The data model centers around "knowledge items" linked to people, topics, metrics, and time. Built for scalability, KnowledgeForge uses modular components for backend, frontend, and data storage. Ultimately, the platform aims to convert scattered information into connected, searchable insights and conversational answers.