Jedify, a New York-based enterprise AI data platform company, has closed a $24 million Series A funding round led by Norwest. The round brings the company’s total funding to $32.5 million, following a seed round raised in 2023, the year the company was founded.
S Capital VC, Cerca Partners, and Oceans Ventures also participated. Snowflake Ventures came in as a strategic backer, signaling that one of the largest cloud data platforms sees something worth betting on in Jedify’s approach to making enterprise data actually usable for AI systems.
What Jedify actually does
Jedify’s answer to the problem of fragmented enterprise data is what it calls Semantic Fusion technology. It creates a contextual data layer that stitches together structured data, unstructured data, and business knowledge into something AI systems can actually reason over. The goal is building a layer that understands what data means in context, so AI agents can operate with precision rather than hallucinating their way through ambiguous inputs.
The company’s leadership team includes CEO Assaf Henkin, CTO Adi Elimelech, and CPO Erik Shani, all of whom bring backgrounds in data platform development. With a lean team of roughly 18 to 35 employees, Jedify is operating with the kind of staffing ratio that suggests the $24 million is meant to fuel a significant scaling push.
Why Snowflake’s involvement matters
Snowflake Ventures’ participation here likely carries more weight than a typical strategic investment. Snowflake has built one of the dominant cloud data platforms in the enterprise world. Jedify’s contextual data layer is designed to sit on top of existing data infrastructure and make it AI-ready, and Snowflake is exactly the kind of platform it needs to integrate with deeply. A strategic investment from Snowflake Ventures suggests the two companies see a product relationship worth formalizing.
Norwest, which led the round, has a track record of investing across fintech and data infrastructure. Specific revenue or customer metrics have not been publicly disclosed.
The bigger picture for data infrastructure and AI
Jedify is not building AI models themselves. It is building the plumbing that makes those models actually useful in enterprise settings where data is messy, siloed, and context-dependent. Jedify’s bet is that a purpose-built semantic layer, rather than a general-purpose data platform with AI features bolted on, will win in environments where precision matters.
Jedify’s challenge will be proving that Semantic Fusion is genuinely defensible technology, not just a feature that Snowflake or another platform partner decides to build in-house. Whether Jedify can convert its Snowflake relationship into meaningful distribution will depend on how deeply the integration goes and whether it translates to actual enterprise contracts.