Norwest leads the round with strategic participation from Snowflake Ventures, as Jedify addresses the AI context problem that major model providers can’t solve
NEW YORK, June 10, 2026 (GLOBE NEWSWIRE) — Jedify, the autonomous context graph for data-intensive agentic applications and workflows, today announced $24 million in Series A funding led by Norwest, with a strategic investment from Snowflake Ventures and participation from existing investors S Capital VC and Cerca Partners, as well as new investors Oceans Ventures. Jedify previously raised an $8.5 million seed round in September 2023, led by S Capital VC with participation from Cerca Partners, bringing total funding to just over $33 million. Assaf Harel, Partner at Norwest, will join Jedify’s board of directors.
The funding will be used to accelerate product development, expand go-to-market and expand its workforce as Jedify addresses one of the most critical and underserved challenges in enterprise AI: giving AI agents the deep, trusted context they require to move from prototype to production. Without runtime business context, agents either hallucinate because they lack the right context or waste tokens because they process too much 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 Assaf Henkin, co-founder and CEO of Jedify. “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.”
The Enterprise AI Context Problem
Despite billions of dollars invested in large language models, most enterprise AI initiatives fail due to a lack of proper context. While models can generate fluent answers, they cannot determine things like which definition of revenue to use, which customer record is current, or which operational assumptions matter unless that context is available at runtime. Enterprises are sitting on vast, complex data spread across dozens of SaaS tools, data warehouses, CRMs, financial systems and unstructured sources like documents, Slack and meeting recordings. Manually pulling that data together into a reliable, AI-ready context is slow, costly, and typically has to be rebuilt from scratch for every new agent or workflow.
The severity of this challenge is not lost on the major model providers themselves. Players such as OpenAI, Anthropic and Google have all recently moved to offer enterprises forward-deployed engineers and professional services teams, an acknowledgment that their models alone are not enough to get AI initiatives across the finish line. But this approach raises its own concerns: enterprises that hand over their data to the same vendors selling them tokens face inherently misaligned incentives. Those vendors benefit from the least efficient, most token-intensive solutions, creating a clear conflict of interest. And when the company building your context layer is also the one charging you per token to use it, the economics rarely favor the customer.
Further, single-vendor dependency conflicts with the governance and flexibility requirements most large organizations operate under.
Jedify was built as an independent, model-agnostic context layer that provides agents with the relevant business meaning they need at runtime, without locking enterprises into a single model vendor.
“Enterprise AI agents can’t reason accurately from stitched-together connectors and warehouses alone,” said Matthew Drooker, CTO of The Weather Company. “Jedify’s context graphs give our agents and analysts the business context they need to operate at Weather Company scale, providing the missing infrastructure for agentic workflows, and a faster path to answers for our teams.”
A Context Graph That’s Built for Enterprise AI Agents
Jedify’s platform autonomously builds a customer-specific context graph, powered by its patent-pending Semantic Fusion™ technology, on top of an enterprise’s existing data and knowledge infrastructure. By connecting structured operational data from data warehouses, CRMs, financial systems and BI tools with unstructured knowledge (documents, playbooks, Slack, meeting recordings), Jedify creates a continuously updated (live), AI-ready semantic model that deeply understands how a business actually works.
The context graph captures metric definitions, entity relationships, lineage, permissions, business rules, operational assumptions and domain-specific terminology, giving AI agents the runtime context needed to generate more accurate, grounded responses. Unlike legacy BI tools, metadata catalogs, and semantic layers that operate on static tables, Jedify’s context graph enables agents to reason across unlimited dimensions, reducing the scope to only the most relevant entities. This cuts token waste, limits hallucinations, and delivers consistent, grounded answers. The platform is model-agnostic by design, so enterprises can leverage the AI providers of their choice without being locked into a single vendor’s data infrastructure.
Every interaction makes the customer’s context graph smarter, creating a compounding, proprietary asset that grows more valuable over time.
“Jedify is solving a foundational problem by autonomously fusing structured and unstructured data into a context graph that gets smarter with every interaction,” said Assaf Harel, partner at Norwest. “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. We’re excited to partner with the Jedify team as they enable enterprises to continuously generate actual value from their proprietary data.”
Bringing Trusted Business Context to Snowflake AI Workflows
Jedify is collaborating with Snowflake to help enterprises unify business context across data, analytics, and AI workflows. By integrating with Snowflake’s AI and semantic capabilities, Jedify enables organizations to build intelligent applications and agents that can access consistent business meaning across enterprise systems, helping improve decision-making, reduce fragmentation, and accelerate production AI initiatives.
“Enterprises are increasingly looking for AI systems that can reason reliably across both structured and unstructured business contexts,” said Harsha Kapre, Head of Snowflake Ventures. “Jedify’s Semantic Fusion technology and deep integration with Snowflake Cortex AI, including Semantic Views, Cortex Analyst, and Snowflake CoWork, help customers operationalize trusted business semantics directly within Snowflake, accelerating the adoption of enterprise AI and agentic applications at scale.”
To learn more about the company’s vision for the next phase of growth, read Assaf’s blog post. For additional details on the platform and its capabilities, visit the website and book a demo with the Jedify team.
About Jedify
Jedify is the autonomous context graph for enterprise AI, powering modern agentic applications and workflows with connected enterprise context. By connecting an organization’s structured operational data, including data warehouses, CRMs and financial systems, with its unstructured knowledge, such as documents, playbooks, Slack and meeting recordings, Jedify automatically builds a proprietary context graph powered by Semantic Fusion™. This context graph enables production-grade AI agents and applications that are more accurate, more consistent, and free of the hallucinations and token waste associated with context-free approaches. Jedify is model-agnostic, ensuring enterprises can build on the AI platforms they choose without single-vendor lock-in. For more information, visit www.jedify.com and follow us on LinkedIn.
Contacts
BOCA Marketing for Jedify
jedify@bocamarketing.com
A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/9d5a2e6b-4f52-4c3c-9c2d-99cea9c2d4ad
