Jedify raises $24M to give AI agents business context
New York startup Jedify closed a $24 million Series A led by Norwest Venture Partners, bringing total funding to approximately $33 million. Snowflake joined as a strategic investor and integration partner, signaling confidence in Jedify's approach to making AI agents more useful within enterprise environments.
The problem Jedify solves is straightforward: AI agents deployed in enterprises often lack the specific knowledge they need to operate effectively. They don't understand a company's terminology, data permissions, or operational workflows without significant customization work.
How the context graph works
Jedify builds what it calls a context graph-a multi-dimensional map connecting databases, data warehouses, SaaS applications, and unstructured sources like Slack channels and meeting recordings. The graph captures relationships between data, people, permissions, and domain-specific terminology.
Unlike traditional knowledge graphs or semantic layers, Jedify's context graph updates in real time and works across different AI models. This allows agents to focus on relevant information rather than searching across all available data. Co-founder and CEO Assaf Henkin said the approach enables AI agents to operate autonomously across CRM systems, support tickets, telemetry, and other sources without extensive custom integration.
Permissions and governance built in
A core challenge in enterprise AI deployment is managing data access. Jedify inherits permissions from identity systems, file systems, SaaS tools, and databases-including row-, column-, and table-level access rules. Customers can define additional groups to control what agents and workflows can access.
The platform includes observability tools so companies can monitor and verify that AI agents behave as intended.
Early customers and use cases
Jedify counts between 10 and 20 early customers, including The Weather Company and compliance firm Kiteworks. The company targets mid-market and large enterprises in data-heavy sectors like gaming, industrials, and consumer packaged goods.
Kiteworks connected Snowflake, Tableau, Notion, and internal playbooks to Jedify to build tools for customer-facing workflows. The system generates real-time dashboards and conversational applications that surface relevant details during customer conversations.
Why Snowflake's backing matters
Large data platforms like Snowflake are building similar capabilities. Henkin argues Jedify's approach is complementary because most of a company's data and institutional knowledge exists outside a single cloud provider.
"The big thing is that not all of your data is in those environments, and most of your knowledge is not there," he said. Building a comparable context layer internally can be prohibitively expensive, especially as companies scrutinize AI token usage. As AI models become more interchangeable, proprietary context that helps them work better within specific businesses could become a durable competitive advantage.
What's next
Jedify plans to use the Series A funding for product development, hiring, and go-to-market efforts. The company is positioning itself as a bridge between generic AI capabilities and real-world business needs, where AI for Product Development requires understanding both technical capabilities and organizational constraints.
Understanding how Generative AI and LLM systems work in practice-particularly how they integrate with existing enterprise infrastructure-is becoming essential for product leaders evaluating AI agent deployments.
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