AI Startup Jedify Secures $24 Million to Give Enterprise AI Agents Better Business Context

Many AI vendors market their enterprise products as ready-to-use solutions, but in reality, AI agents often require significant customisation to operate effectively within a business. Without being trained on company-specific information, an AI system may struggle to understand how an organisation defines key metrics such as revenue or determine which employees are authorised to access certain files. This challenge has led many AI companies to deploy engineers directly into customer environments to help integrate and configure their products.

New York-based startup Jedify is aiming to solve this problem. The company says its platform connects to an organisation’s knowledge sources through APIs and creates what it calls a “context graph,” giving AI agents a deeper understanding of how a business operates. These sources can include databases, data warehouses, data lakes, SaaS platforms, business intelligence tools, and unstructured information such as reports, documentation, code repositories, Slack conversations, and meeting recordings.

To support its growth, Jedify has secured $24 million in a Series A funding round led by Norwest. Existing investors S Capital VC and Cerca Partners also participated, alongside new investor Oceans Ventures. Data cloud company Snowflake joined the round as a strategic investor and is integrating Jedify’s technology into several of its AI offerings, including Cortex AI, Semantic Views, and CoWork.

Jedify argues that AI agents can only become truly useful within enterprises when they understand the relationships among data, entities, permissions, workflows, operational processes, company terminology, and domain-specific knowledge. By providing this context, the company says AI systems can focus on the information relevant to a particular task rather than searching across all available corporate data.

Co-founder and CEO Assaf Henkin cited the compliance software company Kiteworks as an example of how customers use the platform. According to Henkin, Kiteworks connected Snowflake, Tableau, Notion, and various internal playbooks, documents, and screenshots to Jedify before building AI-powered tools for multiple customer-facing 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 generate customer conversation, Jedify builds everything they need to know on the fly. For them, and during the conversation, they can, in real time, get very specific details surfaced proactively,” Henkin said.

Henkin said Jedify differs from traditional semantic layers, metadata catalogues, and knowledge graphs because its context graph captures relationships across multiple dimensions, including people, customers, permissions, operational data, and business entities. He added that the platform remains model-agnostic and updates continuously as connected systems generate new information.

“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.

Managing permissions is one of the biggest challenges when deploying AI agents inside businesses. Organisations need to ensure that employees only gain access to information they are authorised to view. Henkin said Jedify addresses this by inheriting permissions from identity systems, file repositories, SaaS applications, and databases, including row-level, column-level, and table-level access controls. Customers can also create additional governance rules that define which information agents and workflows can access. The company additionally provides observability and governance features to help organisations monitor AI behaviour.

Jedify is currently targeting mid-sized and large enterprises with mature data infrastructures and multiple databases or warehouses. Henkin said the company has between 10 and 20 early customers, including The Weather Company, and is seeing growing interest from industries such as gaming, industrial manufacturing, and consumer packaged goods.

Snowflake’s investment is particularly noteworthy because major data platform providers are pursuing similar capabilities. However, Henkin believes Jedify complements those efforts rather than competing directly with them, as much of a company’s knowledge and data typically resides across multiple systems rather than within a single cloud environment.

“[The large data companies] will tell you, ‘Oh yeah, just bring everything.’ But in reality, companies have multiple databases, 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 noted that building a comparable context layer internally can be expensive for many organisations. Training AI models to create and maintain such a system may become increasingly difficult as businesses pay closer attention to AI-related costs and token consumption.

The company’s broader strategy is based on the belief that as AI models become more powerful and increasingly interchangeable, proprietary business context will become one of the most valuable differentiators. The ability to provide AI systems with a deeper understanding of how organisations operate could create a lasting competitive advantage.

Jedify plans to use the new funding to accelerate product development, expand its workforce, and support go-to-market efforts. With the latest round completed, the startup’s total funding now stands at approximately $33 million.