Jedify raises $24M to arm AI agents with business context they desperately need

Jedify raises $24M to help companies arm AI agents with context on their business

AI vendors promote their enterprise products as if they're turnkey solutions, but the chances are low that AI agents will hit the ground running right away. Unless you put in the effort to train a model on 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. That's part of the reason why we're seeing AI companies deploying engineers to help integrate their AI products into customers' systems. New York-based startup Jedify is attacking this very gap. The company says its platform connects to enterprises' 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, data warehouses and lakes, SaaS apps or BI tools, as well as unstructured sources such as reports, documentation, 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 exclusively learned. 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 tech with its AI products, such as its Cortex AI service, Semantic Views, and CoWork. Jedify's pitch is that to be useful within enterprises, AI agents need access to the relationships between entities, data, permissions, domain knowledge, workflows, operational assumptions, and company-specific terminology. This context, the company says, allows an AI agent to narrow its attention to the information that is relevant to a particular task instead of searching across everything a company has. Co-founder and CEO Assaf Henkin (pictured above, on the 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, then built agentic tools for different customer workflows. "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 said. Henkin argues that Jedify's context graph is different from the semantic layers, metadata catalogs, and knowledge graphs that companies already use because it is multi-dimensional, capturing relationships across entities, data, people, permissions, and customers. It's also model-agnostic and updates in real time as information flows into and out of the systems it is connected to. "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," he said. Permissions are an obvious hurdle here. It wouldn't do for an agent to give an intern access to the CFO's revenue projections, for example. Henkin said his platform works to address that by inheriting permissions from identity systems, file systems, SaaS tools, and databases, including row-, column-, and table-level access rules, then lets its customers create additional groups that define what and whom agents or workflows are allowed to reach. It also offers observability and governance tools to help customers ensure their AI agents are behaving as intended. Jedify is currently targeting mid-market and large enterprise customers that have 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 sectors such as gaming, industrials, and consumer packaged goods. Snowflake's investment and partnership are notable because large data platforms are also trying to build similar capabilities so their customers can use AI more effectively with their data. But Henkin argues that Jedify is complementary to such efforts because much of a company's data -- and most of its institutional knowledge -- isn't usually stored with a single cloud provider. "[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," he said. 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 cost-prohibitive, especially as companies are scrutinizing and clamping down on their AI token usage. And the rapid advances in AI model development play into the company's broader bet: as models grow more capable and more interchangeable, proprietary context that helps those models work better within businesses will prove a valuable and durable moat. The startup will use the fresh cash for product development, hiring, and go-to-market motion. It brings the firm's total funding to about $33 million.

Jedify raises $24M to give enterprise AI agents the business context they lack

Jedify raises $24M to give enterprise AI agents the business context they lack Enterprise artificial intelligence context startup Jedify Inc. today announced that it has raised $24 million in new funding to build what it calls context graphs that give AI agents the business knowledge they need to run in production. Jedify sells software that automatically assembles a customer-specific "context graph" on top of a company's existing data and knowledge systems. The platform links a company's operational data, held in warehouses, customer relationship management and financial systems, with the unstructured material scattered around it: documents, playbooks, Slack threads and meeting recordings. Out of that, Jedify says, it builds a semantic model that keeps current on how the business defines its metrics, how its records relate and who is allowed to see what. Enterprise AI deployments have struggled with exactly this gap. Large language models can produce fluent answers but cannot reliably determine which definition of revenue applies, which customer record is current or which operational assumptions matter unless that context is supplied at runtime. Without it, the company argues, agents either hallucinate or burn tokens processing irrelevant information. "In order for an agentic workflow to really work well for an enterprise at scale, it needs a very deep understanding of that business," said co-founder and Chief Executive Assaf Henkin. "Enterprise data is fragmented across systems, definitions, permissions and workflows. Jedify turns that fragmented knowledge into a live context graph that agents can use to produce accurate, cost-efficient, business-ready answers." Jedify positions itself as a model-agnostic layer that sits apart from the model providers. The company argues that enterprises handing their data to the same vendors selling them tokens face misaligned incentives, a reference to recent moves by OpenAI Group PBC, Anthropic PBC and Google LLC to offer customers forward-deployed engineers and professional services. Jedify says an independent context layer avoids that conflict and the single-vendor lock-in that clashes with most large organizations' governance requirements. The technology underpinning the platform, which Jedify calls Semantic Fusion, is patent-pending. The company says each interaction makes a customer's context graph more accurate, turning it into a proprietary asset that compounds over time. Jedify is also working with Snowflake Inc. to integrate with its Cortex AI tools, including Semantic Views and Cortex Analyst. The funding will go toward product development, go-to-market expansion and hiring. Norwest led the Series A round, with a strategic investment from Snowflake Ventures and participation from existing backers S Capital VC and Cerca Partners alongside new investor Oceans Ventures. Assaf Harel, a partner at Norwest, will join Jedify's board. "Jedify is solving a foundational problem by autonomously fusing structured and unstructured data into a context graph that gets smarter with every interaction," Harel said. "Its compounding value and model-agnostic approach give enterprises flexibility rather than lock-in, which is exactly the kind of durable infrastructure layer we look for." Jedify raised an $8.5 million seed round in September 2023 led by S Capital VC, bringing total funding to more than $33 million.