Jedify raises $24M to help companies arm AI agents with context on their business
Jedify raised $24 million to help enterprise AI agents understand company context, permissions, and workflows. Snowflake joined the round as a strategic investor.
Intelligence analysis by GPT-5.4 Mini
Jedify is betting that enterprise AI agents need a live context layer, not just a better model. Its platform connects business systems into a graph of entities, permissions, and knowledge, and Snowflake is both an investor and integration partner.
Jedify is like a smart instruction book for company robot helpers. It links together notes, chats, files, and rules so the helpers know what matters and what is private, like giving a delivery driver the right map before sending them into a huge office.
Analysis
What Jedify is building
Jedify says it connects to a company’s systems through APIs and builds a “context graph” that helps AI agents understand how a business actually works. That graph can pull from structured sources like databases, warehouses, lakes, SaaS apps, and BI tools, as well as unstructured material such as reports, docs, code, Slack channels, and meeting recordings.
The company argues that enterprise agents need more than access to raw data. They need to know the relationships between people, permissions, workflows, domain terms, operational assumptions, and other business-specific details. In Jedify’s view, that lets an agent focus on the information relevant to a task instead of searching across every system a company uses.
Funding and customers
Jedify raised $24 million in a Series A led by Norwest. Returning investors S Capital VC and Cerca Partners also participated, along with new investor Oceans Ventures. Snowflake joined as a strategic investor and is integrating Jedify with products including Cortex AI, Semantic Views, and CoWork.
CEO Assaf Henkin said customers are already using the platform. He pointed to Kiteworks, which connected tools like Snowflake, Tableau, Notion, and internal playbooks to Jedify and then built agentic tools for different workflows. Henkin described one use case as giving sales and account teams a dashboard-plus-chat interface that surfaces needed information during customer conversations.
Why the company thinks this matters
Jedify is targeting mid-market and large enterprises with mature data stacks and multiple databases or warehouses. The company says it has between 10 and 20 early customers, including The Weather Company, and is seeing interest from data-heavy sectors such as gaming, industrials, and consumer packaged goods.
Henkin also argues that building this layer internally can be expensive, especially as companies watch token usage. The broader bet is that as AI models become more capable and more interchangeable, proprietary business context could become the lasting advantage.
Key points
- Jedify raised $24 million in a Series A led by Norwest, bringing total funding to about $33 million.
- The startup builds a “context graph” that links enterprise data, people, permissions, workflows, and business knowledge.
- Snowflake invested strategically and is integrating Jedify with Cortex AI, Semantic Views, and CoWork.
- Jedify says it has 10 to 20 early customers, including The Weather Company.
- The company is targeting mid-market and large enterprises with mature data stacks.
If Jedify’s approach works, companies could make AI agents far more useful by giving them the right business context in real time. That could help teams get faster answers, better workflow automation, and fewer mistakes around data access. Snowflake’s support could also help Jedify reach more enterprises through existing data infrastructure.
The product still depends on enterprises connecting many systems and keeping permissions accurate, which is hard to do well at scale. If the context graph is too complex or expensive to maintain, customers may choose simpler tools or build their own internal version. It also faces competition from large data platforms that are trying to offer similar capabilities themselves.
