Jedify raises $24M to help companies equip AI agents with context for their business

AI vendors are promoting their business products as if they are turnkey solutions, but it’s unlikely that AI agents will hit the ground running any time soon. Unless you make an effort to train the model on the details of your business, it is unlikely that you will understand how your company, for example, defines revenue or knows who is allowed to see which file. That’s part of the reason we’re seeing AI companies deploy engineers to help integrate their AI products into customer systems.

New York-based startup Jedify is attacking this gap. The company says its platform connects to business data sources through APIs to create a “content graph” about the business that AI agents can use to work better. These sources can be databases, data warehouses and pools, SaaS applications or BI tools, as well as informal sources such as reports, documents, code bases, and even Slack channels and meeting recordings.

To build on that, Jedify has raised $24 million in a Series A funding round led by Nowest, 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 combines the startup’s technology with its AI products, such as the Cortex AI service, Semantic Views, and CoWork.

Jedify’s pitch is that to be useful to businesses, AI agents need access to relationships between organizations, data, permissions, domain information, workflows, performance predictions, and company-specific terminology. This context, the company says, allows the AI ​​agent to narrow its attention to information related to a specific task instead of searching through everything the company has.

Founder and CEO Assaf Henkin (pictured above, right) pointed to Kiteworks, a compliance company, as an example of how customers are using Jedify. Kiteworks connected Snowflake, Tableau, Notion, and internal playbooks, including documents and screenshots, to Jedify, and created agent tools for different customer workflows.

“They wanted to equip their salespeople and account teams with a sophisticated application – you can think of it as a dashboard application and a real-time chat application. When they enter a customer conversation, Jedify builds for them, instantly, everything they need to know. And during the conversation, they can, in real time, get clear insights that are presented further,” said Henkin.

Henkin says Jedify’s context graph is different from the semantic layers, metadata catalogs, and knowledge graphs companies already use because it is multidimensional, capturing relationships across organizations, data, people, permissions, and customers. It is also model-agnostic and updated in real-time as information flows in and out of the systems it is connected to.

“When you want to enable an agent solution to be truly autonomous, to drive decisions across CRM data, Zendesk tickets, maybe telemetry data coming in real-time, that’s when the context graph is much better in terms of capabilities compared to the semantic layer,” he said.

Permissions are an obvious obstacle here. It would not be possible for an agent to give an employee access to the CFO’s income, for example. Henkin said his platform works to address that by obtaining permissions from proprietary applications, 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 management tools to help customers ensure their AI agents are behaving as intended.

Jedify currently targets mid-market customers and large enterprises with mature data stacks and multiple databases or data warehouses. Henkin said the company has between 10 and 20 initial customers, one of which is The Weather Company, and is seeing interest from data-intensive sectors such as sports, industrials, and consumer packaged goods.

Snowflake’s investment and partnership is notable because big data platforms are also trying to build similar capabilities. But Henkin says Jedify complements such efforts because a lot of company data, and a lot of institutional information, isn’t typically stored with a single cloud provider.

“[The large data companies] he’ll tell you, ‘Oh yeah, just bring everything.’ But in reality, companies have a lot of data, warehouses, and data solutions […] The big thing is that not all your data is in those places, and a lot of your information is not there, so it is very bad that they don’t have it,” he said.

Henkin also noted that for companies trying to do this alone, training an AI model to create a comparable context layer can be costly, especially as companies scrutinize and participate in the use of AI tokens.

And the rapid progress in AI model development plays into the company’s broader bet: as models grow more skilled and more flexible, the proprietary context that helps those models work better within businesses could prove a valuable and strong drain.

The startup will use the new cash for product development, hiring, and going-to-market. It brings the company’s total funding to nearly $33 million.