Jedify, a New York-based startup, has successfully raised $24 million in a Series A funding round to address the gap between enterprise data and AI agents. The round was led by Norwest and included returning investors S Capital VC and Cerca Partners, along with new investor Oceans Ventures. Snowflake has also joined as a strategic investor and plans to integrate Jedify's context graph technology into its suite of AI products.
Jedify’s approach centers on building a live, permission-aware context graph that aims to enhance the relevance of AI agents within enterprises. By connecting to various knowledge sources through APIs, Jedify creates a context graph that includes both structured and unstructured data. This encompasses information from databases, cloud data warehouses, SaaS platforms, business intelligence tools, documentation, meeting transcripts, and Slack channels.
The role of context in AI applications is significant. Assaf Henkin, co-founder of Jedify, points out that for AI agents to operate autonomously and effectively, they need access to the intricate relationships between data entities, workflows, operational assumptions, and terminologies unique to each enterprise. Henkin states, “When you want an agent solution to truly become autonomous, to drive decisions based on data from CRM, Zendesk tickets, perhaps telemetry data arriving in real time, that is when the context-graph significantly outperforms the capabilities of a semantic layer.”
Unlike traditional semantic layers or knowledge graphs, Jedify’s context graph functions on a multi-dimensional framework. It tracks relationships among data, people, and entities while updating in real-time as new information is integrated. This dynamic capability allows AI agents to focus on relevant information without sifting through extensive data sets, thereby enhancing operational efficiency.
Access management is another key feature of Jedify’s platform. It inherits permissions from various authentication systems, file management tools, and databases, applying access rules at granular levels including row, column, and table. Clients can establish custom groups to dictate what data AI agents can access, ensuring that monitoring and governance align AI agent behavior with organizational expectations.
Jedify primarily targets mid-market and large enterprises with complex data infrastructures. The company has onboarded between 10 to 20 early customers, with The Weather Company among the notable clients. Interest is also growing from sectors such as gaming, industrial manufacturing, and consumer goods, indicating the broad applicability of Jedify’s technology.
The partnership with Snowflake is particularly strategic, providing Jedify with additional resources and enhancing its integration capabilities across Snowflake’s Cortex AI, Semantic Views, and CoWork products. This collaboration could lead to advancements in how enterprise data is utilized by AI agents, setting a new standard for operational efficiency and decision-making in business environments.
As demand for AI-driven solutions continues to rise, Jedify’s context graph technology may become essential for enterprises aiming to optimize data utilization and improve the effectiveness of their AI agents. The successful funding round and partnership with Snowflake position Jedify for future growth in a competitive market focused on the intersection of AI and enterprise data integration.
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