AI vendors promote their enterprise products as if they are turnkey solutions, but chances are slim that AI agents will hit the ground running right away. Unless you make an effort to train a model about the specifics of your business, it’s unlikely to understand how your company, for example, defines revenue or knows who is allowed to see which file. This is part of the reason why we see AI companies deploying engineers to help integrate their AI products into customer systems.
startup based in New York Jediify it attacks this very gap. The company says its platform connects to enterprise knowledge sources via APIs to create a “context graph” of their business that AI agents can use to work better. These sources can be databases, data warehouses and lakes, SaaS applications or BI tools, as well as unstructured sources such as reports, documentation, code bases, and even Slack channels and meeting recordings.
To accomplish this, Jedify has raised $24 million in a Series A funding round led by Norwest, TechCrunch exclusively reports. Returning backers S Capital VC and Cerca Partners, as well as new investor Oceans Ventures, participated in the round. Data giant Snowflake has also joined as a strategic investor and is integrating the startup’s technology with its AI products, such as its Cortex AI service, Semantic Views and CoWork.
Jedify’s goal is that to be useful in business, AI agents need access to entity-specific relationships, data, permissions, domain knowledge, workflows, operational assumptions, and company-specific terminology. This framework, the company says, allows an AI agent to narrow its attention to information relevant to a specific task rather than searching through everything a company has.
Co-founder and CEO Assaf Henkin (pictured above, far 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 then built agent tools for different customer workflows.
“They wanted to arm their salespeople and account teams with a sophisticated 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 creates for them, on the fly, everything they need to know. And during the conversation, they can, in real-time, get very specific details that pop up proactively.”
Henkin argues that Jedify’s context graph differs from the semantic layers, metadata catalogs, and knowledge graphs that companies already use because it is multidimensional, capturing relationships between entities, data, people, permissions, and customers. It is also model agnostic and updates in real time as information flows in and out of the systems it is connected to.
“When you want to allow an agent solution to be truly autonomous, to drive decisions on CRM data, Zendesk tickets, maybe telemetry data coming in real-time, then a context graph is much better in terms of capabilities versus a semantic layer,” he said.
Permits are an obvious hurdle here. It wouldn’t do for an agent to give an intern access to the CFO’s revenue forecasts, for example. Henkin said his platform is working to address this by inheriting permissions from identity systems, file systems, SaaS tools and databases, including row-, column- and table-level access rules, and then allowing his customers to create additional groups that define what and which agents or workflows are allowed to access. It also offers observability and governance tools to help customers ensure their AI agents are behaving as intended.
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, one of which is The Weather Company, and is seeing interest from data-heavy industries such as gaming, industrials and consumer packaged goods.
Snowflake’s investment and partnership is notable because big data platforms are also trying to build similar capabilities. However, Henkin argues that Jedify is complementary to such efforts because much of a company’s data, and most of its institutional knowledge, is typically not stored in a single cloud provider.
“[The large data companies] he’ll say, “Oh yeah, bring everything.” But in reality, companies have multiple databases, warehouses and data solutions […] The big thing is that not all of your data is in these environments and most of your knowledge is not there, so it’s a bit of a disadvantage that they actually have,” he said.
Henkin also noted that for companies trying to do this on their own, training an AI model to create a comparable level of environment can be cost-prohibitive, especially as companies consider and limit the use of their discrete AI.
And rapid advances in AI model development play into the company’s broader bet: As models become more capable and more interchangeable, the proprietary framework that helps those models work better in business could prove a valuable and resilient moat.
The startup will use the fresh cash for product development, hiring and go-to-market. It brings the company’s total funding to about $33 million.
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