How a scalable intelligence layer turns enterprise data into production A
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. ] The post How a scalable intelligence layer turns enterprise data into production A appeared first on SiliconANGLE.
Key Takeaways
- A scalable intelligence layer built on knowledge graphs delivers the institutional context that turns generic AI models into reliable production systems.
- "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.
- "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.
- "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.
- Neither Neo4j, the sponsor of theCUBE's coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.
Stats & Key Facts
- #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.
- #9 billion today to nearly $10 billion by 2032 , driven by the reality that agents cannot operate reliably without governed context.
A scalable intelligence layer built on knowledge graphs delivers the institutional context that turns generic AI models into reliable production systems. 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.
For more details please read the original article at SiliconANGLE AI.
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