New York-based startup Jedify has secured $24 million in Series A funding to develop enterprise context-graph technology for artificial intelligence agents. The round was led by Norwest, with participation from returning investors S Capital VC and Cerca Partners, alongside new backers Oceans Ventures. Strategic investment came from data platform Snowflake, which will integrate Jedify technology into its Cortex AI, Semantic Views, and CoWork services. Enterprise AI deployments frequently struggle with initialization because foundational models lack institutional knowledge. Jedify addresses this by constructing dynamic context graphs that map relationships across data entities, permissions, workflows, and domain-specific terminology. The platform ingests information from databases, data warehouses, SaaS applications, and unstructured sources such as documentation and communication channels. This architecture enables AI agents to filter relevant information, operate autonomously, and execute cross-system workflows without manual configuration. Co-founder and CEO Assaf Henkin distinguishes Jedify approach from traditional semantic layers and knowledge graphs by emphasizing multi-dimensional relationships and real-time synchronization. The system continuously updates as data flows between connected environments. A critical component is enterprise-grade access control. The platform inherits row, column, and table-level permissions from existing identity and file systems, while allowing administrators to define granular access groups for specific agents or workflows. Governance and observability tools ensure compliance and predictable model behavior. Early adoption demonstrates practical utility. Compliance firm Kiteworks integrated Jedify to unify data from Snowflake, Tableau, Notion, and internal playbooks, creating real-time conversational tools for sales and account management. The Weather Company is also among the ten to twenty early customers, which predominantly serve data-intensive sectors including gaming, industrials, and consumer packaged goods. Snowflake involvement underscores a strategic complementarity rather than direct competition. Henkin notes that enterprise knowledge resides across fragmented stacks that rarely consolidate within a single cloud provider. By operating across distributed data environments, Jedify reduces the prohibitive costs associated with custom model training and token consumption. As foundational models become increasingly standardized, proprietary context infrastructure may establish a durable competitive advantage for enterprises. The startup will allocate the new capital toward product development, technical hiring, and commercial expansion. This financing brings Jedify total raised capital to approximately $33 million. The company continues to target mid-market and large enterprise organizations seeking to deploy autonomous AI agents within complex, regulated data ecosystems.