AI vendors often promote their enterprise products as turnkey solutions, but the chances that AI agents will hit the ground running immediately are low. Unless you invest effort in training a model on your business specifics, it's unlikely to understand how your company defines revenue or knows who is allowed to see which files. This is part of the reason why we see AI companies deploying engineers to help integrate their AI products into customer systems.
New York-based startup Jedify is addressing this gap. The company claims its platform connects to enterprise knowledge sources via APIs to build a "context graph" that AI agents can utilize for improved performance. These sources can include databases, data warehouses, SaaS applications, or BI tools, as well as unstructured sources like reports, documentation, code bases, and even Slack channels and meeting recordings.
To achieve this, Jedify has raised $24 million in a Series A funding round led by Norwest, as exclusively reported by TechCrunch. This round saw participation from returning backers S Capital VC and Cerca Partners, alongside new investor Oceans Ventures. Data giant Snowflake also participated as a strategic investor and is integrating the startup's technology with its AI products, such as its Cortex AI service, Semantic Views, and CoWork.
Jedify argues that for AI agents to be useful within enterprises, they need access to the relationships between entities, data, permissions, domain knowledge, workflows, operational assumptions, and company-specific terminology. This context allows an AI agent to narrow its focus to information relevant to a specific task rather than searching through all that a company possesses.
Co-founder and CEO Assaf Henkin pointed to Kiteworks, a compliance company, as an example of how customers are using Jedify. Kiteworks connected Snowflake, Tableau, Notion, and internal playbooks, including documents and screenshots, to Jedify, then built agentic tools for different 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.
Jedify's context graph is different from the semantic layers, metadata catalogs, and knowledge graphs that companies already use 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 into and out of the systems it connects to. "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 are an obvious hurdle here. It wouldn't be suitable for an agent to give an intern access to the CFO's revenue projections, for instance. Henkin stated 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, then allows its customers to create additional groups that define what and whom agents or workflows are allowed to reach. It also offers observability and governance tools to help customers ensure their AI agents are behaving as intended.
Jedify is currently targeting mid-market and large enterprise customers with mature data stacks and multiple databases or data warehouses. Henkin mentioned that the company has between 10 and 20 early customers, one of which is The Weather Company, and is seeing interest from data-heavy sectors like gaming, industrials, and consumer packaged goods.
Snowflake's investment and partnership are significant because large data platforms are also striving to build similar capabilities. However, Henkin argues that Jedify is complementary to such efforts since much of a company's data and most of its institutional knowledge is typically not 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 noted that for companies attempting to do this on their own, training an AI model to build a comparable context layer can be cost-prohibitive, especially as companies are scrutinizing and clamping down on their AI token usage. The rapid advances in AI model development also play into the company's broader bet: as models grow more capable and interchangeable, proprietary context that helps those models work better within businesses could prove a valuable and durable moat.
The startup will use the fresh capital for product development, hiring, and go-to-market strategies. This brings the firm's total funding to approximately $33 million.
Blogger's Review: Jedify's context graph technology provides essential background information for enterprise AI agents, making them more intelligent and efficient. As AI technology continues to evolve, companies need to focus on integrating data and knowledge to enhance the usability and decision-making capabilities of AI models. Such innovations lay the groundwork for digital transformation in enterprises and are worthy of attention.