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Jedify raised $24M in a Series A led by Norwest to build context graphs that give enterprise AI agents real-time access to business rules and data definitions.
- Jedify secured $24M in a Series A led by Norwest, with Snowflake Ventures, S Capital VC, Cerca Partners and Oceans Ventures participating.
- The Israeli startup's patent-pending "Semantic Fusion" platform auto-builds a live context graph spanning data warehouses, CRMs, BI tools and unstructured sources.
- The round brings total funding to over $33M, following an $8.5M seed in September 2023.
Enterprise AI Has a Memory Problem, and Jedify Just Got $24M to Fix It
Jedify, the Tel Aviv-founded startup building context graph infrastructure for enterprise AI, closed a $24 million Series A on June 10, 2026. Norwest led the round. Snowflake Ventures joined as a strategic investor, alongside returning backers S Capital VC and Cerca Partners and new participant Oceans Ventures. Assaf Harel, a partner at Norwest, will join Jedify's board. Valuation was not disclosed.
The financing lands as enterprises hit a specific, well-documented wall in AI deployment: large language models perform well in demos and fail in production because they have no reliable access to what a business actually means by its own terms. A model asked to pull "active customers" cannot answer accurately if it does not know how the company defines active. Jedify is building the infrastructure layer that resolves that gap.
What Problem Does Jedify Actually Solve?
Enterprise AI agents consistently break when they encounter ambiguous business terminology, conflicting data definitions across systems, and permission rules that live in no single place. The result is hallucinated outputs, wrong numbers, and compliance exposure - problems that block production deployment regardless of how capable the underlying model is.
Jedify's platform addresses this by building an autonomous, continuously updated semantic model across an organization's data stack. The system ingests structured data from warehouses, CRMs, financial platforms and BI tools, then layers in unstructured sources such as documents, Slack messages and meeting recordings. What emerges is a live "context graph" that captures business definitions, entity relationships, operational rules and domain-specific terminology - all in one queryable layer that AI agents can access in real time.
The underlying technology, which the company calls Semantic Fusion, is patent-pending. Jedify was founded in 2023 by Assaf Henkin (CEO), Adi Elimelech (CTO) and Erik Shani (CPO). The company now employs 35 people.
Why Does Snowflake's Strategic Stake Matter?
A financial return is not Snowflake's primary motivation here. The company is integrating Jedify's context graph with its own Cortex AI products, including Semantic Views, Cortex Analyst and Snowflake CoWork. For Snowflake, the bet is that enterprise customers who struggle to extract value from Cortex AI need a business context layer before the AI can function reliably - and that Jedify is the cleanest way to provide one.
This pattern - a cloud data platform investing in a startup that makes its AI products usable - reflects a broader dynamic in the current enterprise AI market. The model and the compute are largely commoditized. The differentiator increasingly sits in the data preparation and context infrastructure that sits between raw data and the AI layer. Norwest, which has a long record in enterprise SaaS, is making the same read.
Prior Funding in Context
The $24 million Series A follows an $8.5 million seed round from September 2023, meaning Jedify raised its A roughly 33 months after founding. Total capital now exceeds $33 million. The jump from seed to A - nearly three times the seed size - suggests investors gained meaningful confidence in either early customer traction or the technical differentiation of Semantic Fusion. The company has not disclosed revenue figures or customer counts.
What Does This Round Signal About the Enterprise AI Infrastructure Market?
The Jedify deal is one of several signals pointing to context infrastructure as the next discrete funding category in enterprise AI. Companies have spent the past 18 months buying access to foundation models; the current problem is making those models trustworthy inside real business environments. That gap - between raw AI capability and reliable enterprise output - is exactly where Jedify is positioned.
The platform's integrations with Snowflake's ecosystem also suggest the company is deliberately aligning with where enterprise data already lives rather than asking customers to move it.
Outlook
Jedify enters the second half of 2026 with a board seat occupied by a Norwest partner, a strategic pipeline into Snowflake's customer base, and a technical thesis - that a shared context graph can replace the patchwork of manual metadata management most enterprises currently rely on - that has attracted two serious investors in the same round. The company's near-term priorities will likely center on deepening those Snowflake integrations and converting early pilots into paid production deployments. The market for enterprise AI context infrastructure does not yet have a clear category leader. Jedify now has the capital to push for that position.
