Enterprise AI

Answer-first: Naive RAG works well for simple keyword queries on isolated documents. For complex, global questions spanning multiple entities, GraphRAG is superior as it builds a knowledge graph using LLMs. Enterprise implementations require combining change data capture (CDC) with vector search to keep graphs synchronized. What You’ll Learn That AI Won’t Tell You Schema design for knowledge graphs that speed up global enterprise RAG. Syncing GraphRAG knowledge bases in real-time using PostgreSQL WAL events. Most RAG (Retrieval-Augmented Generation) implementations look the same: chunk documents, embed them into vectors, store them in a vector database, retrieve by cosine similarity, and inject the top-K chunks into the LLM context. This works for simple document Q&A. It fails systematically for enterprise knowledge bases where the answer to a question depends not on a single document chunk, but on the relationships between dozens of interconnected entities. ...

Google Cloud Next ‘26 ran in Las Vegas on April 22-23, 2026. After reading the full source material from the conference announcements, the picture that emerges is not a product update cycle. It is a strategic repositioning. Google Cloud CEO Thomas Kurian’s framing was explicit: “The experimental phase is behind us. How do you move AI into your entire enterprise? The answer is a unified stack.” Three interlocking bets define the announcement set. First, the Gemini Enterprise Agent Platform consolidates Google’s fragmented AI tooling into a single surface for building, running, and governing autonomous agents. Second, the eighth-generation TPUs split into two purpose-built variants — one for training, one for inference — reflecting a fundamental shift in how Google thinks about AI infrastructure economics. Third, Workspace Intelligence attempts to turn Google’s productivity suite into a shared knowledge layer that agents can reason across, not just a collection of isolated apps. ...