How a scalable intelligence layer turns enterprise data into production A

Coverage from SiliconANGLE's livestreaming video studiohelp_outline UPDATED 17:19 EDT / JULY 29 2026 AI How a scalable intelligence layer turns enterprise data into production A by Jonathan Anthony Enterprises are discovering that a scalable intelligence layer built on knowledge graphs delivers the institutional context that turns generic AI models into reliable production systems. As companies push AI from pilots into production, a new software layer is emerging to link structured, unstructured and connected data into the neural pathways of a business. Startups are racing to build this foundation, with context-graph specialists such as Jedify Inc. raising fresh capital to give agents the business knowledge they need to operate reliably. The real intelligence stays inside the company, grounded in the relationships across its data, according to Jeevan Pathuri (pictured), vice president of software engineering at Microsoft Corp. “The real value of intelligence comes from these connections and the relationships between the different distinct data that we’ve got across the enterprise,” Pathuri said. “As we model things in the form of nodes and edges and the relationships, it becomes really easy for these language models to kind of understand and answer questions in a more sophisticated way than your standard answering questions by a SQL query.” Pathuri spoke with theCUBE’s John Furrier for an exclusive AI Luminaries interview series on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how graphs, ontologies and a scalable intelligence layer are reshaping the path from AI experimentation to production value. (* Disclosure below.) The knowledge graph as a horizontally scalable intelligence layer Grounding a large language model in domain-specific context is what turns generic capability into business results, and connected data does the heavy lifting. In Microsoft’s own supply chain work, modeling a bill of materials as a graph let the team stand up agents dramatically faster, Pathuri noted. “We were able to build 10 to 15 agents in production in a matter of a few weeks,” he said. “Without this kind of connected intelligence, we would have had to build these agents individually one after the other, kind of rediscovering the relationships and the metadata along the way, versus having all of this created once and making it available for many scenarios.” That reusable semantic layer is where enterprises should invest, and the market is following suit. The enterprise knowledge graph market is projected to grow from about $1.9 billion today to nearly $10 billion by 2032, driven by the reality that agents cannot operate reliably without governed context. Defining intent clearly compresses time-to-market, Pathuri noted. “If you want to really extract the intelligence and make the system work for you, defining the semantic layer on top is going to be very critical,” he said. “An agent we spent three or four weeks to develop in the past, now we can do that in a matter of a few days.” Building that foundation is now central to trusted, production-grade AI, a theme echoed in theCUBE Research’s 2026 predictions, which flagged knowledge graphs and semantic layers as core to enterprise deployments. Humans remain in charge, with evals and production traces backing the system — but the data groundwork cannot be skipped, Pathuri noted. “There are no shortcuts here. It’s still hard work,” Pathuri said. “Creating that clean data layer, the connected data layer, is absolutely a must.” Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of