While AI vendors frequently market their enterprise products as ready-to-use solutions, the immediate operational effectiveness of AI agents is often limited. Without dedicated effort to train these models on an organization's specific operational nuances—such as how revenue is defined or which personnel have access to particular files—their ability to function effectively remains hindered. This inherent need for customization is precisely why many AI companies currently deploy their engineers to facilitate the integration of their products into customer systems.
Addressing this critical gap is Jedify, a New York-based startup. The company asserts that its platform connects to an enterprise's diverse knowledge sources via APIs, constructing a "context graph" unique to their business. This graph then empowers AI agents to operate with heightened intelligence and precision. These foundational knowledge sources encompass both structured data, like databases, data warehouses, data lakes, SaaS applications, and BI tools, as well as unstructured information, including reports, documentation, code repositories, Slack channels, and even meeting recordings.
To further develop and scale its innovative platform, Jedify has successfully closed a Series A funding round, securing $24 million. This exclusive information, reported by TechCrunch, reveals that the round was led by Norwest. Significant participation came from returning investors S Capital VC and Cerca Partners, alongside new investor Oceans Ventures. Notably, data industry titan Snowflake also joined as a strategic investor, signaling a partnership that will see Jedify’s technology integrated into Snowflake’s own AI product suite, including its Cortex AI service, Semantic Views, and CoWork.
Jedify's core proposition is that for AI agents to deliver tangible value within enterprises, they must have access to the intricate relationships between entities, data, permissions, domain-specific knowledge, workflows, operational assumptions, and company-specific terminology. The company emphasizes that this rich, granular context enables an AI agent to precisely focus its attention on information directly relevant to a given task, rather than indiscriminately sifting through all available company data.
Assaf Henkin, Co-founder and CEO of Jedify, highlighted Kiteworks, a compliance company, as a prime example of customer adoption. Kiteworks leveraged Jedify by connecting its Snowflake, Tableau, Notion instances, and internal playbooks—which included various documents and screenshots. This integration then allowed Kiteworks to develop specialized agentic tools tailored to different customer workflows.
Henkin elaborated on the Kiteworks use case, stating, "They wanted to arm their sellers and account teams with a sophisticated app — you can think of it as both like a dashboard application and a real-time conversational application. When they go into a customer conversation, Jedify builds for them, on the fly, everything they need to know. And during the conversation, they can, in real time, get very specific details surfaced proactively."
Henkin asserts that Jedify’s context graph stands apart from conventional semantic layers, metadata catalogs, and knowledge graphs already utilized by companies. Its distinction lies in its multi-dimensional nature, capable of capturing complex relationships across entities, data, people, permissions, and customers. Furthermore, the platform is model-agnostic and dynamically updates in real time as information flows into and out of its connected systems.
"When you want to enable an agentic solution to really be autonomous, to drive decisions across CRM data, Zendesk tickets, maybe telemetry data that’s coming in real time, that’s when a context graph is much better in terms of capabilities versus a semantic layer," Henkin explained.
Addressing the critical challenge of permissions, Henkin confirmed that Jedify’s platform is designed to overcome this hurdle. For instance, preventing an AI agent from inadvertently granting an intern access to a CFO’s revenue projections is paramount. Jedify tackles this by inheriting permissions from various identity systems, file systems, SaaS tools, and databases, including granular row-, column-, and table-level access rules. It also empowers customers to establish additional custom groups, precisely defining what information and to whom agents or workflows are permitted access. Complementing this, the platform offers robust observability and governance tools, ensuring AI agents operate strictly within their intended parameters.
Jedify is strategically targeting mid-market and large enterprise customers who possess mature data stacks and manage multiple databases or data warehouses. Henkin disclosed that the company currently serves between 10 and 20 early customers, including prominent names like The Weather Company. Interest is also surging from data-intensive sectors such as gaming, industrials, and consumer packaged goods.
Snowflake’s investment and partnership hold particular significance, especially given that major data platforms are themselves endeavoring to develop similar capabilities to enhance AI utility for their customers. However, Henkin posits that Jedify’s solution is complementary to these efforts. He argues that a substantial portion of a company's data—and indeed, most of its institutional knowledge—is rarely consolidated within a single cloud provider.
Henkin further elaborated on this point, stating, "[The large data companies] will tell you, ‘Oh yeah, just bring everything.’ But in reality, companies have multiple databases, and warehouses, and data solutions […] The big thing is that not all of your data is in those environments, and most of your knowledge is not there, so it’s a bit of a disadvantage that they actually have."
Moreover, Henkin highlighted the prohibitive costs associated with companies attempting to build a comparable context layer by independently training AI models, particularly as enterprises increasingly scrutinize and constrain their AI token usage.
The company’s overarching strategy aligns with the rapid advancements in AI model development: as models become more capable and increasingly interchangeable, the proprietary context that uniquely enables these models to perform optimally within specific business environments will emerge as a highly valuable and enduring competitive advantage.
The recently secured capital will be allocated towards accelerating product development, expanding the team through strategic hiring, and bolstering the company’s go-to-market initiatives. This latest funding round elevates Jedify's total capital raised to approximately $33 million.
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