AI context graph startup Jedify has closed a $24 million Series A financing round, to advance its autonomous context graph platform built to empower enterprise AI agents with comprehensive business context awareness.
Founded in New York in 2023 by CEO Assaf Henkin, CTO Adi Elimelech and CPO Erik Shani, Jedify previously secured an $8.5 million seed round in the same year. The founding team aims to equip AI agents with full contextual awareness of entity relationships across enterprise data ecosystems. New capital will accelerate product iteration, expand market outreach and support team recruitment.
CEO Assaf Henkin commented: “Scalable enterprise agentic workflows require deep business comprehension. Enterprise data is fragmented across disparate systems, access permissions and internal workflows. Jedify unifies scattered corporate knowledge into a live context graph, enabling AI agents to deliver precise, cost-effective and business-aligned outputs.”
Jedify notes that modern AI models generate fluent text outputs but lack real-time business context. They cannot identify official revenue standards, valid customer records or critical operational assumptions without runtime contextual support. Enterprise business data spreads across dozens of SaaS platforms, data warehouses, CRM and financial systems, plus unstructured data including documents, Slack logs and meeting recordings. Manual context consolidation is slow, costly and requires repeated rebuilding for every new AI agent or workflow.
Graph technology is the optimal solution to automate semantic context capture and map entity relationships for autonomous AI agents. CTO Adi Elimelech explained the core gap between semantic layers and context graphs in her official blog:
“AI agents work differently from human analysts. They run autonomously with multi-step decision chains, and need full background beyond basic metric definitions: metric ownership, historical definition changes, trusted data sources for conflicting records and actionable data anomalies. A semantic layer only defines metrics, while a context graph delivers complete decision-making infrastructure for correct agent execution.”
She added that traditional semantic layers unify consistent metric queries for BI analysts, but AI agents face far more complex real-world scenarios. For instance, agents need to distinguish official quarterly MRR tables, inconsistent cross-team data schemas, implicit business rules hidden in old Slack threads, and known billing data anomalies — all institutional knowledge that semantic layers cannot store.
Semantic layers only record what metrics mean, whereas context graphs store all implicit business knowledge required for AI agents to use metrics correctly. Jedify stressed enterprises should avoid handing proprietary organizational context data to large AI model vendors, as these providers hold conflicting interests: they profit from high-token-consumption solutions, raising customer overall costs. Jedify delivers an independent, model-agnostic context layer with no vendor lock-in.
Powered by patent-pending Semantic Fusion technology, Jedify’s platform automatically builds customized live context graphs based on enterprises’ existing data stacks. It connects structured data from warehouses, CRM and BI tools with unstructured internal knowledge, forming a real-time updated AI semantic model tailored to actual business operations. The graph covers metric definitions, data lineage, access permissions, business rules and domain terminology to boost agent response accuracy.
This Series A round was led by Norwest, with strategic investment from Snowflake Ventures, alongside participation from existing backers S Capital VC, Cerca Partners and new investor Oceans Ventures. Norwest partner Assaf Harel has joined Jedify’s board of directors. He stated: “Jedify solves a fundamental AI infrastructure pain point by merging structured and unstructured data into an evolving context graph. Its model-agnostic design avoids vendor lock-in and brings growing compound value for enterprises, making it a critical long-term AI infrastructure layer.”
Industry Footnote
Graph technology is gaining rapid traction across the AI agent sector, with key industry moves as follows: Israeli generative semantic graph startup Illumex was acquired by Nvidia for $60–$75 million in February 2026. Neo4j and Memgraph are both developing graph-native tools for AI agents. Besides, data protection vendor Druva has integrated graph technology to organize backup metadata for its intelligent deep analysis agents.
Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
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Email: yangyd@qianxingdata.com
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