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. 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.

Cloud technology giant ServiceNow has notified some of its enterprise customers that a software bug on its platform was allowing anyone on the internet to access their data.

A knowledge base article, which ServiceNow has hidden behind a login wall but has been shared on Reddit, says the company on June 5 patched some customer instances to fix a bug that had allowed unauthenticated users to “gain greater access” to ServiceNow-hosted data than intended.

The bug allowed potentially anyone to access data stored in customer instances without requiring credentials, such as a password. 

ServiceNow tells TechCrunch that the security incident was not a hack, but the work of security researchers who were looking for vulnerabilities that they could submit for a bug bounty program.

“Alongside our own investigation, we have been in contact with the security researchers who initially reported this issue and can confirm that evidence of the observed activity came from those security researchers and customer research teams, not bad actors,” said ServiceNow spokesperson Courtney Johnson. “The security researchers have advised their activity was solely for bug bounty submissions and no data was used or retained.”

When asked by TechCrunch, ServiceNow did not immediately name the security researchers, nor say how many ServiceNow customers’ data was accessed.

Given that the security incident appears to stem from a data-exposing bug, it’s unclear if customers could have protected themselves from improper access prior to the incident.

ServiceNow is a cloud computing giant that allows thousands of its enterprise customers to automate their internal business processes. Companies use the tech giant’s platform to build workflows that connect to various apps and databases, such as IT and HR systems, which can be used to automatically handle repeat tasks, like onboarding staff, resolving tech support tickets, and for chatbots.

As such, companies like ServiceNow can be high-value targets for hackers thanks to the amount of sensitive data that they store, such as customer support tickets, which can include passwords, keys, and credentials.

ServiceNow said the issue relates to customer instances running its Australia releases, but several people on Reddit say they have identified evidence of external access to ServiceNow instances running other versions of its software.

Network defenders shared an IP address, 51.159.98.241, said to be an indicator of potential data access if found in a customer’s logs.

Corrected the seventh paragraph to update references to the Australia releases, unrelated to geography. Updated to include comment from ServiceNow.

theWarner Music Music (WMG) announced on Wednesday that it’s acquiring AI attribution startup Sureel AI. Sureel’s patented technology creates “AI DNA” for songs and breaks them down into component parts to trace how AI models use those elements.

Through the acquisition, WMG aims to better track when its artists’ and songwriters’ work is used in AI-generated content or for training AI models.

“Bringing Sureel into WMG strengthens our capability for protection, control and monetization and ensures that the creative community remains in control of its intellectual property, name, image, likeness, and voice,” said WMG chief executive Robert Kyncl in the press release.

The financial terms of the deal were not disclosed.

Founded in 2022, Sureel also offers intellectual property provenance, audit and compliance reporting, model optimization, and AI business intelligence. The startup also has a name, image, and likeness (NIL) attribution suite to track how artist voices, likenesses, and performance identities are used in AI training and generation. This includes voice clones, AI-generated avatars, and style replication. 

The startup will continue to operate as a stand-alone platform serving the broader music and AI ecosystem, WMG says.

“Rightsholders deserve to know how AI interacts with their work, and to share fairly in the value it creates,” Sureel founder and chief executive Tamay Aykut said in remarks. “Sureel was built to make that possible, and with WMG’s backing, we can deliver on our mission at scale, building a more transparent and fair future and driving value growth for the whole music and entertainment ecosystem.”

WMG has embraced AI after initially opposing it, as the company originally sued music-generation startup Suno in 2024 and later signed a licensing deal with the company last year. WMG said at the time that artists and songwriters would have full control over whether and how their names, images, likenesses, voices, and compositions are used in new AI-generated music.

It’s worth noting that Sony Music Entertainment and Universal Music Group are still pursuing massive copyright infringement claims against the AI music startup.

WMG last year also settled its lawsuit against AI music startup Udio and reached a licensing deal with the company.