knowledge graphs
Beyond Market Intelligence keeps knowledge graphs in one place: 5 stories so far. The section currently leads with “Smart Graph Decisions at Scale: Where TypeSafe Jev Meets LLM Reasoning”, “Unlock Your Codebase: Explore AI-Powered Knowledge Graphs for Seamless Development”, and “Unlock Advanced RAG: 6 Architectures for Semantic Search & LLMs”. High-frequency graph decisions demand speed and consistency, not just clever prompting. Multi-file reasoning has long been the silent killer of AI coding workflows. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every knowledge graphs story on Beyond Market Intelligence, newest first.
Smart Graph Decisions at Scale: Where TypeSafe Jev Meets LLM Reasoning
High-frequency graph decisions demand speed and consistency, not just clever prompting. TypeSafe Jev delivers that calibrated, System One approach, while LLMs handle the synthesis and open-ended reasoning where they excel. It is a practical division of labor that finally makes GraphRAG scalable. For readers tracking how these models handle structure and long-form generation, the related piece on paragraph structure in token space offers a useful complement.
Unlock Your Codebase: Explore AI-Powered Knowledge Graphs for Seamless Development
Multi-file reasoning has long been the silent killer of AI coding workflows. Graphify, launched in April 2026, tackles this head-on by turning codebases and unstructured data into queryable knowledge graphs. The recent parser upgrades and cross-file resolution improvements show real momentum, though community feedback rightly notes that daily integration still needs polish. We see promising architecture here, and we are watching closely. For more on how AI tools are reshaping developer expectations, check out our piece on navigating AI/ML job requirements.
Unlock Advanced RAG: 6 Architectures for Semantic Search & LLMs
Basic graph retrieval only gets you so far. This guide walks through six production-oriented architectures that pair semantic search with knowledge graphs and LLM reasoning, giving practitioners a practical route beyond simple vector lookups. It's a grounded, no-nonsense read for teams ready to move past toy demos. For a deeper look at how LLMs handle structure internally, pair it with our piece on paragraph structure and token space. The ideas complement each other well.
Move beyond basic RAG with four knowledge graph patterns for agentic AI.
Knowledge graphs are moving from the background to the backbone of agentic AI, and Cassie Shum is here to show you why. In her talk, she moves past basic retrieval to outline four practical architecture patterns that bring reasoning into production. It's a grounded, actionable look at building systems that don't just retrieve, but reason. For a broader perspective on how these ideas connect, our piece on bridging retrieval and action offers a useful parallel.
Enterprise AI agents need your business context, not just general knowledge
Enterprise AI agents fail when they lean on general knowledge instead of the messy, specific context that defines how a company actually works. That's the core insight from SAP's Max McPhee, who told VentureBeat's Rob Stretchay at VB Transform 2026 that grounding agents in knowledge graphs and governance is what turns a chatbot into a true collaborator. It's a practical reframe.