Jedify raises $24M to give AI agents business context

AI vendors often pitch their enterprise products as plug-and-play solutions, but the reality is that AI agents rarely perform effectively without deep business-specific training. Without customizing a model to understand how your company defines key metrics like revenue or manages file permissions, these tools struggle to deliver real value. This challenge has led many AI companies to deploy engineers for hands-on integration with customer systems.

Enter Jedify, a New York-based startup tackling this exact problem head-on. The company’s platform connects to enterprise knowledge sources through APIs, building a context graph that equips AI agents with the business understanding they need to operate effectively. These sources span databases, data warehouses, data lakes, SaaS applications, BI tools, and unstructured data like reports, documentation, code repositories, Slack channels, and meeting recordings.

To accelerate this vision, Jedify has secured $24 million in Series A funding led by Norwest, as confirmed exclusively to TechCrunch. The round includes participation from existing backers S Capital VC and Cerca Partners, along with new investor Oceans Ventures. Notably, data giant Snowflake joined as a strategic investor and is integrating Jedify’s technology with its AI offerings, including Cortex AI, Semantic Views, and CoWork.

Jedify’s core argument is straightforward: for AI agents to be truly useful in enterprises, they must grasp the relationships between entities, data, permissions, domain knowledge, workflows, operational assumptions, and company-specific terminology. This context enables an agent to focus on relevant information for a given task, rather than sifting through an entire organization’s data.

Co-founder and CEO Assaf Henkin highlights Kiteworks, a compliance company, as a prime example. Kiteworks connected Snowflake, Tableau, Notion, and internal playbooks,including documents and screenshots,to Jedify, then built agentic tools for various customer workflows.

“They wanted to arm their sellers and account teams with a sophisticated app , you can think of it as both like a dashboard application and a real-time conversational application. When they go into a customer conversation, Jedify builds for them, on the fly, everything they need to know. And during the conversation, they can, in real time, get very specific details surfaced proactively,” Henkin said.

Henkin argues that Jedify’s context graph differs from existing semantic layers, metadata catalogs, and knowledge graphs because it is multi-dimensional, capturing relationships across entities, data, people, permissions, and customers. It is also model-agnostic and updates in real time as information flows in and out of connected systems.

“When you want to enable an agentic solution to really be autonomous, to drive decisions across CRM data, Zendesk tickets, maybe telemetry data that’s coming in real time, that’s when a context graph is much better in terms of capabilities versus a semantic layer,” he said.

Permissions remain a critical hurdle. An agent must not, for example, grant an intern access to the CFO’s revenue projections. Henkin explains that his platform addresses this by inheriting permissions from identity systems, file systems, SaaS tools, and databases,including row-, column-, and table-level access rules. Customers can then create additional groups to define what and whom agents or workflows can reach. The platform also provides observability and governance tools to ensure AI agents behave as intended.

Jedify is currently targeting mid-market and large enterprise customers with mature data stacks and multiple databases or data warehouses. Henkin reports between 10 and 20 early customers, including The Weather Company, with growing interest from data-heavy sectors like gaming, industrials, and consumer packaged goods.

Snowflake’s investment and partnership are significant, as major data platforms are also developing similar capabilities. However, Henkin contends that Jedify is complementary because much of a company’s data,and most of its institutional knowledge,isn’t stored with a single cloud provider.

“[The large data companies] will tell you, ‘Oh yeah, just bring everything.’ But in reality, companies have multiple databases, and warehouses, and data solutions […] The big thing is that not all of your data is in those environments, and most of your knowledge is not there, so it’s a bit of a disadvantage that they actually have,” he said.

Henkin also notes that for companies attempting to build this on their own, training an AI model to create a comparable context layer can be cost-prohibitive, especially as organizations increasingly monitor and restrict AI token usage.

The rapid evolution of AI models plays into Jedify’s broader bet: as models become more capable and interchangeable, proprietary context that enhances their performance within businesses could become a valuable and durable competitive advantage.

The startup plans to use the fresh funding for product development, hiring, and go-to-market efforts. This round brings Jedify’s total funding to approximately $33 million.