Jedify Raises $24M to Bridge the Enterprise AI Context Gap
New York startup Jedify closes Series A funding to help AI agents understand business-specific context through multi-source knowledge graphs.
Last verified:
Jedify’s $24M Series A Closing
Jedify, a New York-based startup, announced a $24 million Series A funding round led by Norwest, according to TechCrunch. The round included participation from returning investors S Capital VC and Cerca Partners, new investor Oceans Ventures, and Snowflake as a strategic investor. Snowflake is integrating Jedify’s technology into Cortex AI, its conversational analytics service, alongside Semantic Views and CoWork.
The Enterprise AI Context Problem
AI agents delivered to enterprises without company-specific training often fail to understand basic operational realities: how revenue is defined, which employees can access which files, or what workflows govern decision-making. According to TechCrunch, vendors are responding by deploying engineering teams to customize integrations for each customer—a labor-intensive, non-scalable solution.
Jedify attacks this gap by building a “context graph” that connects to structured and unstructured enterprise data sources via APIs. According to the company, these sources span databases, data warehouses, SaaS applications, business intelligence tools, documentation, code repositories, Slack channels, and meeting recordings. The resulting graph enables AI agents to understand the relationships between entities, permissions, domain terminology, and operational assumptions rather than searching indiscriminately across all available information.
Differentiation Through Real-Time Multi-Dimensional Mapping
Jedify CEO Assaf Henkin distinguishes the platform from existing semantic layers and metadata catalogs by emphasizing its multi-dimensional, real-time nature. According to the TechCrunch report, the context graph captures relationships across data, people, permissions, and customers while remaining model-agnostic and updating dynamically as information flows through connected systems.
Kiteworks, a compliance software company, exemplifies the application. The company connected Snowflake, Tableau, Notion, and internal documentation to Jedify to build agentic tools for sales and account teams. According to Henkin, the resulting system surfaces relevant customer information on-the-fly during conversations, functioning as both a dashboard and real-time conversational interface.
Why This Matters
The enterprise AI market is constrained not by model capability but by context integration. Teams deploying agents into production will increasingly need platforms that stitch together fragmented knowledge sources without requiring retraining or fine-tuning. Snowflake’s participation signals confidence that context graphs will become table-stakes infrastructure for enterprise AI, positioning Jedify to capitalize on the gap between agent deployment and operational readiness. For teams selecting AI platforms, the availability of context-aware integrations may become a decisive vendor criterion as agents move from proof-of-concept to mission-critical workflows.
Frequently Asked Questions
What problem does Jedify solve?
Jedify addresses the gap between AI agent capabilities and enterprise readiness. Without business-specific context, AI agents cannot understand company-defined metrics, access rules, or domain-specific workflows, limiting their practical utility in production environments.
How does Jedify's context graph differ from existing knowledge management tools?
According to Jedify CEO Assaf Henkin, the platform's multi-dimensional approach captures relationships across entities, data, people, and permissions—and updates in real time as information flows through connected systems, unlike static metadata catalogs or semantic layers.
Who is using Jedify?
Compliance software company Kiteworks connected Snowflake, Tableau, Notion, and internal playbooks to Jedify to build agentic tools that help sales and account teams surface relevant customer context during conversations.