Jedify, an AI infrastructure company developing context graph technology for enterprise applications and AI agents, emerged from stealth with $24 million in Series A funding, bringing total funding to $33 million. The round was led by Norwest Venture Partners, with participation from Snowflake, S Capital VC, Cerca Partners, and Oceans Ventures. The investment will support product development, core technology expansion, and go-to-market growth.
Solving the Enterprise AI Context Problem
Despite advances in AI, many enterprise AI and agentic systems struggle to operate effectively because they lack a deep understanding of business context. Organizations continue to manage fragmented data across CRM platforms, support systems, product tools, and other disconnected software environments, making it difficult for AI agents to execute workflows reliably.
Semantic Fusion™ and Context Graph Technology
Jedify addresses this challenge through Semantic Fusion™, a context graph technology designed to create a continuously updated representation of a business. By connecting structured and unstructured data across systems, the platform enables AI agents, applications, and workflows to access relevant business context and make more informed decisions.
The technology allows organizations to link customer interactions, operational data, support history, and business processes into a unified semantic layer that evolves over time.
Building Infrastructure for Agentic AI
Jedify positions context graphs as a foundational layer for enterprise AI, similar to how observability became essential for cloud infrastructure. Rather than relying on manually maintained prompts, instructions, and workflows, organizations can use context graphs to provide AI systems with a scalable and persistent understanding of business operations.
The company believes production-grade AI agents will increasingly depend on dedicated context layers to operate efficiently and accurately at scale.
A Model-Agnostic Approach
Jedify advocates for independent ownership of enterprise context data, emphasizing that a company’s semantic layer should remain separate from model providers. The platform is designed to be model-agnostic, enabling organizations to use different AI models while maintaining control over business knowledge, terminology, and operational intelligence.
The new funding will help accelerate adoption of context graph technology across industries including cybersecurity, media, and enterprise software.