Jedify Raises $24 Million to Give Enterprise AI Agents the Business Context They Need to Stop Hallucinating
Most enterprise AI deployments fail for a predictable reason: the AI doesn't actually understand the business. It knows a lot about the world in general, but it doesn't know what your company's specific metrics mean, how your data systems connect, or what your internal terminology refers to. Jedify raised $24 million in Series A funding on June 10, 2026 to solve exactly that problem - building what it calls context graphs that give AI agents accurate, live business knowledge.
Jedify raised $24 million in Series A funding led by Norwest, with a strategic investment from Snowflake Ventures. Existing investors S Capital VC and Cerca Partners also participated, along with new investor Oceans Ventures. The company previously raised an $8.5 million seed round in September 2023, bringing total funding to more than $33 million. Assaf Harel, a partner at Norwest, will join Jedify's board. Canada.ca
The Problem Jedify Is Solving
Enterprise data is fragmented. The numbers that matter live in different systems - a data warehouse here, a CRM there, financial systems somewhere else, plus unstructured knowledge in documents, Slack conversations, and meeting recordings. When an AI agent tries to answer a business question, it often processes irrelevant data, misinterprets metric definitions, or generates plausible-sounding but incorrect answers because it lacks the connective tissue that makes the data meaningful.
"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, Jedify's co-founder and CEO. "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." Canada.ca
Jedify said major AI companies including OpenAI, Anthropic and Google have expanded professional services offerings to help customers deploy AI systems, reflecting the challenges organizations face in implementing large language models. The company argued that relying on a single vendor for both AI models and data infrastructure can create concerns about flexibility and governance. Jedify's platform is model-agnostic. Canada.ca
How Context Graphs Work
Jedify's Semantic Fusion technology builds customer-specific context graphs by combining structured operational data with unstructured knowledge. The resulting graph captures metric definitions, entity relationships, permissions, business rules, and industry-specific terminology - essentially encoding the institutional knowledge of the business into a format that AI agents can use in real time.
Matthew Drooker, CTO of The Weather Company, described the practical result: "Enterprise AI agents can't reason accurately from stitched-together connectors and warehouses alone. Jedify's context graphs give our agents and analysts the business context they need to operate at Weather Company scale." Canada.ca
The token cost reduction angle is also significant. When an AI agent has accurate context available, it stops processing irrelevant data. That reduces the token consumption that has been driving the enterprise AI billing crises seen at Uber and Microsoft, where agents chewing through entire codebases and data warehouses on every query created unexpected cost spirals.
What This Means for Business Leaders
From four years advising executives on AI for business adoption, I have watched the same pattern repeat: companies deploy AI agents with high expectations, then discover that the agents produce unreliable outputs because they don't understand the business's specific definitions, rules, and terminology. The fix is usually expensive custom integration work.
Jedify is trying to make that fix a product rather than a consulting engagement. The model-agnostic approach matters - it means you can use the context layer with Claude, GPT-4, Gemini, or any open-source model, rather than being locked into a single vendor's ecosystem for both intelligence and data infrastructure.
The Snowflake Ventures participation is a useful signal. Snowflake's cloud data platform is where many large enterprises store their most important structured data. A strategic investment in Jedify suggests Snowflake sees context graphs as a valuable layer above its core business - and may eventually integrate this capability directly into its platform.
Cut Through the Noise
What is Jedify and what does it do? Jedify is an enterprise AI company that builds context graphs - structured semantic models of a business's data, definitions, rules, and relationships - to give AI agents accurate business knowledge at runtime. Without sufficient context, AI agents produce hallucinated or irrelevant answers and consume excessive tokens processing irrelevant data. Jedify's Semantic Fusion technology combines structured data from operational systems with unstructured knowledge from documents, Slack conversations, and meeting recordings into a continuously updated context graph.
Why do enterprise AI agents fail without business context? Enterprise data is fragmented across dozens of systems, each with its own definitions, formats, and terminology. When AI agents query these systems without a unified semantic model, they cannot accurately interpret metric definitions, understand entity relationships, or apply business rules. The result is incorrect outputs, hallucinations, and excessive token consumption from processing irrelevant data - the underlying cause of many enterprise AI deployment failures.
How much did Jedify raise and who invested? Jedify raised $24 million in Series A funding led by Norwest Venture Partners, with strategic participation from Snowflake Ventures. Existing investors S Capital VC and Cerca Partners also participated, along with new investor Oceans Ventures. Total funding since founding exceeds $33 million. Norwest partner Assaf Harel will join the board.
Is Jedify's context graph technology compatible with multiple AI models? Yes. Jedify's platform is explicitly model-agnostic, designed to work with OpenAI, Anthropic, Google, and open-source models. This prevents vendor lock-in and allows enterprises to change AI model providers without rebuilding their context infrastructure. The platform integrates with Snowflake Cortex AI products including Semantic Views and Cortex Analyst.
