Jedify raises $24 million to help companies arm AI agents with the context of their business

AI vendors promote their enterprise products as if they are integrated solutions, but the chances of AI agents getting to work right away are low. Unless you make the effort to train a model on the details of your business, it’s unlikely to understand how your company, for example, determines revenue or knows who’s allowed to see a file. This is part of the reason we see AI companies deploying engineers to help integrate their AI products into customer systems.

Startup in New York laudatory It attacks this very gap. The company says its platform connects to organizations’ knowledge sources via APIs to build a “context graph” about their business that AI agents can use to work better. These sources can be databases, warehouses, data lakes, SaaS applications or business intelligence tools, as well as unstructured sources such as reports, documents, code bases, and even Slack channels and meeting recordings.

To build that out, Jedify has raised $24 million in a Series A funding round led by Norwest, TechCrunch has learned exclusively. The round saw participation from returning backers S Capital VC and Cerca Partners, as well as new investor Oceans Ventures. Data giant Snowflake also participated as a strategic investor and is integrating the startup’s technology with its own AI products, such as Cortex AI Service, Semantic Views, and CoWork.

The idea of ​​Jedify is that for AI agents to be useful within organizations, they need access to entity relationships, data, permissions, domain knowledge, workflows, operational assumptions, and company-specific terminology. The company says this context allows the AI ​​agent to narrow its attention to information relevant to a particular task rather than searching through everything the company has.

Co-founder and CEO Assaf Henkin (pictured above, far right) pointed to Kiteworks, a compliance firm, as an example of how customers use Jedify. Kiteworks connected Snowflake, Tableau, Notion, and internal playbooks, including documents and screenshots, to Jedify, and then built proxy tools for various customer workflows.

“They wanted to arm sellers and account teams with a cutting-edge app — you can think of it as a dashboard app and a real-time chat app. When they get into a conversation with customers, Jedify builds for them, on the fly, everything they need to know. And during the conversation, they can, in real-time, proactively have very specific details emerge.”

Henkin argues that the Jedify context graph differs from the semantic layers, metadata catalogs, and knowledge graphs that companies already use because it is multidimensional, capturing relationships across entities, data, people, permissions, and customers. It is also model-free and is updated in real time as information flows in and out of the systems it connects to.

“When you want to enable an agent solution to be truly autonomous, to drive decisions via CRM data, Zendesk tickets, and maybe telemetry data that comes in real time, then context graphing is much better in terms of capabilities versus the semantic layer.”

Permissions are an obvious hurdle here. It would not be helpful for the agent to give the intern access to the CFO’s revenue forecasts, for example. Henkin said its platform addresses this by inheriting permissions from identity systems, file systems, SaaS tools and databases, including row, column and table-level access rules, and then allows its customers to create additional groups that define what and who agents or workflows are allowed to access. It also provides monitoring and governance tools to help customers ensure that their AI agents are behaving as intended.

Jedify currently targets medium and large enterprise customers with mature datasets and multiple databases or data warehouses. The company has between 10 and 20 early customers, one of which is The Weather Company, and is seeing interest from data-heavy sectors such as gaming, industrials and consumer packaged goods, Henkin said.

Snowflake’s investment and partnership is notable because big data platforms are also trying to build similar capabilities so their customers can use AI more effectively with their data. But Henkin sees Jedify as complementary to such efforts because much of a company’s data — and most of its institutional knowledge — isn’t typically stored at a single cloud provider.

“(Big data companies) will say, ‘Oh yeah, just bring everything.’ But in reality, companies have multiple databases, warehouses, and data solutions (…) The thing is, not all of your data is in those environments, and most of your knowledge isn’t, so actually having it is a bit of a disadvantage.

Henkin also noted that for companies trying to do this on their own, training an AI model to build a comparable context layer can be expensive, especially since… Companies are examining and clamping down on the use of their AI codes.

And the rapid progress in AI model development plays into the company’s broader bet: as models grow more capable and more interchangeable, the ownership context that helps those models work better within companies will prove a valuable and enduring moat.

The startup will use the new funds for product development, hiring and go-to-market. This brings the company’s total funding to approximately $33 million.