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Jedify customers can build agents to use its context graphs Jedify’s Data Agents help an organization build autonomous AI agents that can can monitor business metrics, investigate changes and run recurring analyses, without requiring users to produce detailed code or workflows. Traditional business intelligence (BI) requires analysts to define questions and build dashboards in advance. Newer BI has generic AI assistants infer business meaning from raw tables and schema. Jedify is an AI context graph startup and reckons that enterprise data is fragmented across on-prem, public cloud, and SaaS app silos and systems, structured and unstructured datasources, definitions, permissions, and workflows. Its software reads these distributed. disparate knowledge fragments and uses them to populate a live context graph that AI agents can use to produce more accurate request responses. The graph embodies the semantic context, the relationships between the business entities and items an AI agent deals with in its processing for an organization. Jedify co-founder and CTO Adi Elimelech said: "Enterprises are moving analytics from answering questions to doing meaningful work. With Data Agents, teams can build an analytical workforce tailored to how their business operates and give employees access to those agents wherever they already work." The Data Agents product features: - Plain-language agent building. Describe the analytical outcome you want, and Jedify creates and refines the agent conversationally, with no code or workflow builder. - Flexible triggers. Agents run on demand, on a schedule or when a defined condition is met, triggering recurring analysis without someone asking for it each time. - Root-cause investigation. Grounded in Jedify's context graph and research engine, Data Agents monitor metrics, investigate anomalies and identify likely drivers behind a change. - Proactive, not reactive. Data Agents surface a gap, an anomaly, a market that's underperforming, the moment it emerges, instead of waiting to be discovered in a report. - Permission-aware by default. Agents inherit the requesting user's existing data permissions, so an agent cannot reveal or access a row, column or metric that the user isn't entitled to see. - Built for efficiency at scale. Because agents reason from Jedify's pre-resolved context graph instead of exploring raw schema at query time, they use far fewer tokens per question than a schema-inferring agent. - Works where teams already work. Through MCP, a Data Agent is exposed as a tool teams can call from Claude, ChatGPT, Slack or any agent platform they run. Results can also be delivered directly through Slack and email. Building AI agents using natural language would seem like a common sense idea and is table stakes, just not in the storage and data protection world. Think Microsoft Copilot Studio, Salesforce Agentforce, ServiceNowSI Agent Studio and others. Commvault is one of the few data protection companies with a product here, as its AI Studio has an AI agent builder. Data Agents is available now, accessible through Jedify's Agents Hub. Learn more here. Bootnote Jedify raised $24 million in an A-round in June this year. It was started up in 2013 and took in an $8.5 million seed round later that year.
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