Independent, vendor-neutral hub at the intersection of Knowledge Management and Artificial Intelligence.
Linked relationships between KM4AI guides, glossary terms, and tools. Dogfoods the mission: expose internal content relationships as linked data.
Audit, cleanup, chunking, and metadata so a document repository is retrieval-ready.
Taxonomies and ontologies for LLM applications
How controlled vocabularies and formal models improve LLM filtering, grounding, and curation.
Entity-relationship models for KM practitioners adopting AI search and reasoning.
Human-in-the-loop tagging, cleanup, and enrichment for knowledge bases that feed AI.
KPIs and evaluation approaches for knowledge quality in AI initiatives.
Governance, provenance, and permissions for AI-consumed knowledge
Access control, audit trails, and ownership when knowledge feeds copilots and RAG.
Retrieval-Augmented Generation — retrieve relevant knowledge before an LLM answers.
Numerical vector representation of content for semantic similarity.
Formal model of concepts and relationships in a domain.
Emergent user tagging vocabulary; needs governance before AI use.
Know-how held by people that is hard to document.
Network of entities and relationships for structured organizational knowledge.
Tying AI outputs to verifiable source material.
Confident but incorrect AI output not supported by sources.
Splitting documents into coherent retrieval units.
Defined fields describing content for filtering, governance, and retrieval.
Controlled hierarchical classification of topics or entities.
Traceable history of source, ownership, and changes for knowledge items.
Capturing, organizing, governing, sharing, and reusing organizational knowledge.
Store optimized for similarity search over embeddings.
Ownership, quality, retention, and permission policies for knowledge assets.
Search that matches meaning rather than exact keywords.
Finding who knows what inside an organization.
How prepared knowledge assets are for reliable AI use.
Governed access layer connecting platforms for authorized AI and human use.
Garbage In, Garbage Out — AI quality is bounded by knowledge quality.
Crawl and sync content from enterprise repositories into a governed pipeline.
Assisted taxonomy and metadata tagging for trusted findability.
Keeps AI knowledge stores current through controlled transformation steps.
Flags outdated or risky content before AI consumption.
Structured database layer for KM4AI DataMesh deployments.
Visualization companion for exploring knowledge structures in a DataMesh.
Client-side static search for documentation sites.
Git-based CMS for Markdown content with PR review workflows.