Jedify 完成 2400 万美元融资，由 Norwest 领投，S Capital VC、Cerca Partners、Oceans Ventures 及战略投资者 Snowflake Ventures 参投。该公司致力于帮助企业为其 AI 智能体提供业务上下文信息。
原文 · 未翻译
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.
Jedify 完成 2400 万美元融资，由 Norwest 领投，S Capital VC、Cerca Partners、Oceans Ventures 及战略投资者 Snowflake Ventures 参投。该公司致力于帮助企业为其 AI 智能体提供业务上下文信息。
原文 · 保持原样，未翻译
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.
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. 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 could 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.
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. 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 could 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.