Jedify nabs $24M to equip AI agents with business context

AI vendors often market their enterprise products as ready-to-deploy solutions, but the likelihood that AI agents will perform effectively from day one is low. Without customizing a model to your company's unique operations, it cannot grasp how your business defines revenue or which employees have access to specific files. This is one reason why AI companies are dispatching engineers to help integrate their tools into customer environments.

Jedify, a startup based in New York, is addressing this exact challenge. According to the company, its platform uses APIs to link to enterprise knowledge sources and create a "context graph" of their business, enabling AI agents to function more effectively. These sources include databases, data warehouses, data lakes, SaaS applications, BI tools, and unstructured sources like reports, documentation, code bases, Slack channels, and meeting recordings.

To support this development, Jedify has secured $24 million in Series A funding led by Norwest, as exclusively reported by TechCrunch. Returning investors S Capital VC and Cerca Partners joined the round, along with new backer Oceans Ventures. Data giant Snowflake also participated as a strategic investor and is integrating Jedify's technology into its own AI offerings, including Cortex AI, Semantic Views, and CoWork.

Jedify's core proposition is that AI agents must understand relationships among entities, data, permissions, domain knowledge, workflows, operational assumptions, and company-specific terminology to be truly valuable in an enterprise setting. This context, the company explains, enables an AI agent to focus on information relevant to a specific task rather than scanning the entire organization's data.

Co-founder and CEO Assaf Henkin (pictured above, far right) cited Kiteworks, a compliance company, as an example of how customers leverage Jedify. Kiteworks integrated Snowflake, Tableau, Notion, and internal playbooks—including documents and screenshots—with Jedify, and then developed agentic tools for various customer workflows.

"They aimed to equip their sales and account teams with a sophisticated application—imagine a dashboard combined with a real-time conversational tool. When they enter a customer discussion, Jedify dynamically generates everything they need to know. During the conversation, it proactively surfaces highly specific details in real time," Henkin said.

Jedify's context graph. Image Credits: JedifyImage Credits:Jedify /

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 need an agentic solution to operate autonomously—making decisions across CRM data, Zendesk tickets, and maybe real-time telemetry data—a context graph offers far superior capabilities compared to a semantic layer," he said.

Permissions present a clear challenge. For instance, an agent should not 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 rules—and then allows customers to create additional groups that specify what agents or workflows can access. The platform also provides observability and governance tools to help customers verify that their AI agents behave as expected.

Jedify currently targets mid-market and large enterprise customers with mature data stacks and multiple databases or data warehouses. According to Henkin, the company has between 10 and 20 early customers, including The Weather Company, and is attracting interest from data-intensive sectors like gaming, industrials, and consumer packaged goods.

Snowflake's investment and partnership are noteworthy, as major data platforms are also developing similar capabilities. However, Henkin maintains that Jedify complements these efforts because a significant portion of a company's data and most of its institutional knowledge typically reside across multiple providers, not a single cloud.

"[The large data companies] will tell you, 'Oh yeah, just bring everything.' But in reality, companies have multiple databases, warehouses, and data solutions. The key point is that not all your data lives in those environments, and most of your knowledge isn't there either, so it's actually a disadvantage for them," he said.

Henkin also pointed out that for companies attempting to build a comparable context layer independently, training an AI model can be prohibitively expensive, especially as businesses become more careful about their AI token consumption.

Moreover, the fast pace of AI model development supports the company's broader thesis: as models become more powerful and interchangeable, proprietary context that enhances their performance within businesses could become a valuable and enduring competitive advantage.

The startup plans to use the new capital for product development, hiring, and go-to-market activities. This brings the company's total funding to approximately $33 million.