Jedify raises $24M to give AI agents the business context they need

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Jedify raises $24M to give AI agents the business context they need

New York-based startup Jedify has raised $24 million in a Series A funding round to help enterprises build a so-called context graph that makes AI agents more useful within their specific business environments. The round was led by Norwest Venture Partners, with participation from existing investors S Capital VC and Cerca Partners, as well as new backer Oceans Ventures. Snowflake also joined as a strategic investor and is integrating Jedify’s technology with its own AI products, including Cortex AI and Semantic Views.

Why AI agents need business-specific context

AI vendors often market their enterprise products as ready-to-deploy solutions, but in practice, these tools rarely understand a company’s unique terminology, data permissions, or operational workflows without significant customization. Jedify’s platform addresses this gap by connecting to an enterprise’s existing knowledge sources—databases, data warehouses, SaaS applications, business intelligence tools, and even unstructured sources like Slack channels, meeting recordings, and documentation—to build a dynamic, multi-dimensional context graph. This graph captures relationships between entities, data, permissions, domain knowledge, and company-specific terminology, allowing AI agents to focus on relevant information rather than searching across all available data.

How the context graph works

Jedify’s context graph differs from traditional semantic layers, metadata catalogs, and knowledge graphs because it is designed to be multi-dimensional, capturing connections across data, people, permissions, and customers. It is model-agnostic and updates in real time as information flows into and out of connected systems. Co-founder and CEO Assaf Henkin explained that this approach enables AI agents to operate autonomously across diverse data sources—from CRM data and Zendesk tickets to real-time telemetry—without requiring extensive custom integration.

Permissions and governance

One of the key challenges in deploying AI agents within enterprises is managing data access permissions. Jedify’s platform inherits permissions from identity systems, file systems, SaaS tools, and databases, including row-, column-, and table-level access rules. Customers can also create additional groups to define what agents and workflows are allowed to access. The platform includes observability and governance tools to help companies monitor and ensure their AI agents behave as intended.

Customer adoption and market focus

Jedify currently targets mid-market and large enterprise customers with mature data stacks and multiple databases or data warehouses. Henkin said the company has between 10 and 20 early customers, including The Weather Company. Use cases span data-heavy sectors such as gaming, industrials, and consumer packaged goods. One customer, compliance company Kiteworks, connected Snowflake, Tableau, Notion, and internal playbooks to Jedify to build agentic tools for customer workflows. The system generates real-time dashboards and conversational applications that surface specific details during customer conversations.

Strategic positioning and Snowflake partnership

Snowflake’s investment and integration are notable because large data platforms are also working on similar capabilities. However, Henkin argues that Jedify’s approach is complementary, noting that much of a company’s data and institutional knowledge is not stored with 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. He also pointed out that building a comparable context layer internally can be cost-prohibitive, especially as companies scrutinize AI token usage. As AI models become more capable and interchangeable, proprietary context that helps them work better within businesses could become a valuable and durable competitive advantage.

Use of funds

Jedify plans to use the fresh capital for product development, hiring, and go-to-market efforts. The Series A brings the company’s total funding to approximately $33 million.

Conclusion

Jedify’s approach addresses a fundamental challenge in enterprise AI adoption: the need for business-specific context that allows AI agents to operate effectively and securely. With backing from Norwest and Snowflake, the startup is well-positioned to help companies bridge the gap between generic AI capabilities and real-world business needs.

FAQs

Q1: What is a context graph and how is it different from a knowledge graph?
A context graph is a multi-dimensional representation of relationships between data, people, permissions, and business-specific terminology, designed to help AI agents narrow their focus to relevant information. Unlike traditional knowledge graphs, it captures dynamic, real-time connections across diverse data sources and is model-agnostic.

Q2: How does Jedify handle data permissions and security?
Jedify inherits permissions from existing identity systems, file systems, SaaS tools, and databases, including row-, column-, and table-level access rules. Customers can also define custom groups to control what agents and workflows can access, and the platform includes observability and governance tools for monitoring AI agent behavior.

Q3: Which companies are using Jedify?
Jedify has between 10 and 20 early customers, including The Weather Company and compliance firm Kiteworks. The company is targeting mid-market and large enterprises in data-heavy sectors such as gaming, industrials, and consumer packaged goods.

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