When enterprise AI agents fail, it's rarely because the underlying language models aren't smart enough. More often, it's because they don't know what they're talking about—literally.
That's the pitch from Jedify, a New York-based startup that just closed a $24 million Series A round led by Norwest Venture Partners. Announced on June 10, 2026, the funding brings the young company's total capital raised to just over $33 million and arrives at a moment when corporate America is racing to deploy AI agents that can actually do useful work.
The round drew strategic backing from Snowflake Ventures alongside participation from S Capital VC, Cerca Partners, and new investor Oceans Ventures. Norwest partner Assaf Harel is joining Jedify's board.
What Jedify actually does
The company's technology addresses what founders describe as the "context problem" in enterprise AI. While large language models excel at generating coherent text, they struggle when asked to navigate the messy, permission-laden reality of corporate data—customer records scattered across Salesforce, strategic insights buried in Slack threads, institutional knowledge locked in meeting transcripts.
Jedify's answer is something it calls Semantic Fusion, a patent-pending system that builds what the company describes as an "autonomous context graph" over existing enterprise infrastructure. Think of it as a dynamic knowledge layer that sits between AI agents and the dozens of systems where business-critical information actually lives.
The platform pulls from data warehouses, CRM systems, business intelligence tools, documents, and communication platforms to create what amounts to a company-specific understanding for AI agents. It's model-agnostic by design, which means it can work with whatever LLM a company prefers—OpenAI, Anthropic, or something else entirely.
More importantly, perhaps, it inherits existing access controls. Row-level, column-level, table-level permissions carry over from identity and database systems, with additional controls layered on for agent-specific workflows. The goal is to reduce what engineers call "token waste" and hallucinations by scoping AI queries to relevant entities at runtime rather than dumping entire datasets into prompts.
Timing and traction
Founded in 2023 by Assaf Henkin (CEO), Adi Elimelech (CTO), and Erik Shani (CPO), Jedify previously raised an $8.5 million seed round in September 2023, also led by S Capital VC. As of June 2026, the company employs between 11 and 50 people.
As of the funding announcement, Jedify reports between 10 and 20 early customers—a figure confirmed by TechCrunch. Named deployments include The Weather Company, which is using the platform to provide business context for agents and analysts, and Kiteworks, which connected its Snowflake, Tableau, Notion, and internal playbooks to build what it calls "agentic tools" for seller and account workflows.
The strategic investment from Snowflake Ventures hints at deeper integration work ahead. Jedify's platform already connects with Snowflake's Semantic Views, Cortex Analyst, and CoWork products, suggesting the data giant sees context graphs as a necessary complement to its cloud infrastructure.
A category gaining definition
The timing aligns with broader enterprise interest in context graphs for AI systems. The category has gained conceptual momentum as venture firms and enterprises recognize context graphs as essential infrastructure for the next wave of enterprise AI applications.
In a blog post published the same day as Jedify's announcement, Norwest described the startup as "the missing layer in enterprise AI"—a signal that venture firms are now backing specific architectural bets on how companies will actually operationalize AI agents.
Whether context graphs become standard infrastructure or just another layer of complexity remains an open question. But with $24 million in fresh capital, Jedify is betting that enterprises need more than just smarter models. They need systems that know what questions to ask, and where to find the answers.
The company says it will use the funding to accelerate product development, expand go-to-market efforts, and grow its team—the familiar roadmap of a young company trying to establish category leadership before the window closes.
