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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 startupJedifyis 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 ascompanies 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.
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Eros Brings Cultural AI Ecosystem to UK with $355 Mn Investment
The initiative will strengthen collaboration between India and the UK in artificial intelligence, storytelling, and creative industries.
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Big Tech Cloud Infra is Breaking the Illusion of India's Sovereign AI Ambitions
Hyperscalers such as Google are actively investing in India. However, they primarily serve their own enterprise clients.
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Piper Serica Moves to Fix Deep Tech Startups’ Biggest Funding Gap
The Bharat Tech Fund supports companies at advanced technology readiness levels that have spent years developing their products but struggle to scale due to insufficient funding.
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The Real Reason Why AI Agents are Failing in Organisations
Genpact’s Ajay Vatsal believes the biggest returns from agentic AI will come from better operational outcomes and employee augmentation.
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AI is Eating Away at Indian IT's Old Business. Can it Rewrite Enterprise Playbook?
From Mistral tie-ups to proprietary agentic platforms, India's technology services majors are racing to reinvent themselves for the AI era.
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Google Rolls Out Gemini 3.5 Live Translate for Real-Time Multilingual Conversations
Google on Wednesday rolled out its latest speech-to-speech translation model called Gemini 3.5 Live Translate. Google claims it is designed to enable more natural multilingual conversations. As per the company, the new AI model can detect more than 70 languages and generate translated speech. The result is claimed to preserve a speaker's tone, pacing, and intonation while continuously delivering near real-time translations. Gemini 3.5 Live Translate is rolling out across Google Translate, Google Meet, Google AI Studio, and the Gemini Live API for developers.
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Meta signs first AI data center deal in India with Reliance
As tech companies race to secure the computing power needed to train and deploy AI systems, Meta is making its first AI infrastructure bet in India, striking a data center partnership with conglomerate Reliance Industries in a market that is rapidly emerging as a hub for AI infrastructure. The partnership,announcedon Wednesday, will see Meta collaborate with Reliance on a 168-megawatt AI-enabled data center in Jamnagar, Gujarat, expanding a relationship that has evolved from Meta’s multibillion-dollar investment in Reliance’s Jio Platforms to a$100 million joint venturelaunched last year to develop enterprise AI solutions for customers in India and overseas markets. The deal comes as India cements its status as a natural destination for AI infrastructure investments, with tech giants seeking new geographies for data centers amid soaring demand for computing power to train and deploy AI models. Companies includingMicrosoft,Amazon,Google,OpenAI, andUberhave recently announced AI and cloud infrastructure investments in the country, which has rapidly expanded its data center footprint in recent years. The rush into India extends beyond global technology firms. Earlier this week, Blackstone-backed AirTrunk announced plans toinvest $30 billion to build 5 gigawatts of data center capacityin the country by 2030, while Indian conglomerates includingAdaniandTata Consultancy Serviceshave also unveiled major data center expansion plans aimed at supporting AI workloads. New Delhi has sought to attract such investments through policy incentives, includingtax exemptions through 2047for foreign cloud providers on services sold overseas, so long as those workloads are run from Indian data centers. India’s installed data center capacity has risen fromabout 375 megawatts in 2020 to around 1.5 gigawattsin 2025, according to government data. Industry estimates project that figure could growmore than fivefold to over 8 gigawattsby the end of the decade, driven by cloud adoption, AI workloads, and rising demand for local data processing. The Meta-Reliance agreement marks the latest chapter in a relationship that has steadily deepened since Metainvested $5.7 billionin Jio Platforms in 2020. Since then, the companies have expanded their collaboration across digital services, enterprise AI, and now the infrastructure underpinning next-generation AI systems. As part of the partnership, Meta is leasing capacity at Reliance’s new Jamnagar facility, which the companies said will be powered by renewable energy and cooled using desalinated seawater. Meta has committed to covering the entire cost of the energy and water required to support its operations there. Reliance said the 168-megawatt facility will ready within two years and can be expanded over time. Further, the data center will also support Meta’s global infrastructure and AI computing requirements, plugging India more directly into the company’s worldwide network of AI facilities. Under the agreement, Reliance said it would provide end-to-end services ranging from design and construction to renewable power, connectivity, and ongoing operations, a sign of the conglomerate’s ambitions to become a one-stop shop for AI infrastructure among global technology companies. Separately, Meta said it had contracted nearly 1 gigawatt of new renewable energy capacity in India through agreements with CleanMax and Fourth Partner Energy, which will supplement the renewable power supporting the Jamnagar facility. The companies did not disclose the value of the agreement, the type of AI workloads that will run from the facility, or whether Meta plans additional AI infrastructure investments in India.
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Haleon to Invest ₹2,000 Crore in First India Manufacturing Facility in Madhya Pradesh
The site is expected to generate up to 500 direct jobs, along with indirect employment opportunities.
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Shunya Labs Launches Voice AI Platform Supporting 216 Indian Languages and Dialects
The platform combines speech recognition, natural language understanding, and generative AI to enable human-like conversations in regional languages and dialects.
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LTM Unveils Outcome-Based Pricing Model as AI Agents Reshape IT Services Contracts
The BlueVerse Currency framework will link pricing to business outcomes rather than effort spent, reflecting a broader shift in how IT services firms are monetising AI-led transformation initiatives.
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Bengaluru-Based HyperNorm AI Raises $2.2 Mn to Bring Explainable AI to Wealth Management
The startup plans to use the funding to enhance product development, grow its engineering and AI research teams, and expand in the US and other international markets.
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