Jedify launches Data Agents for autonomous AI analysts
Jedify Inc. has launched Data Agents, a product that enables business teams to create autonomous AI analysts using plain language. The tool leverages a context graph to integrate structured data and documents, aiming to simplify analytics workflows and reduce the token costs associated with AI queries.
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What Data Agents does
Jedify Inc. has formally launched Data Agents, a product designed to let business teams create autonomous AI analysts using plain language descriptions. The tool aims to address a common limitation in traditional business intelligence software: the need for analysts to pre-build dashboards based on anticipated questions.
Data Agents runs on Jedify’s context graph, which integrates structured data and documents into a unified model of how a business operates. This model includes metric definitions and record-level permissions, allowing users to describe questions in natural language without worrying about schema mapping or joins.
Agents can be scheduled to run on demand, at set intervals, or when specific conditions are met. Data access is permission-based, ensuring results reflect only what the user is authorized to see.
How it reduces costs and complexity
Jedify claims its agents use significantly fewer tokens per question compared to AI systems that explore raw schema at query time. This efficiency stems from the context graph’s pre-integrated model of business operations, which eliminates the need for the AI to interpret schema from scratch for each query.
The product also supports the Model Context Protocol, enabling teams to integrate agents with platforms like Claude, ChatGPT, and Slack. This allows users to interact with Data Agents outside Jedify’s interface.
Early adoption and funding
RubyPlay Ltd., a gaming and digital media company, is using Data Agents to automate daily reports and flag anomalies without manual intervention. Avi Baruch, RubyPlay’s Vice President of Data and Business Applications, highlighted the tool’s ability to identify drivers of anomalies, such as underperforming markets.
Data Agents is available through Jedify’s Agents Hub, with a free tier for initial use. Jedify has raised over $33 million in venture capital, including a $24 million Series A round led by Norwest.
Why this fits into the enterprise AI landscape
The launch of Data Agents reflects a broader trend of enterprises seeking to move beyond task-specific AI tools toward systems that reengineer workflows for measurable ROI. Earlier reporting from Lazyfounder highlighted the "valley of despair" companies face when AI investments fail to deliver financial payoff, often due to a focus on automating individual tasks rather than redesigning processes.
Jedify’s approach—integrating structured data and documents into a single model—aligns with efforts like TOTVS’s LYNN, which also emphasizes governed operational data integration. Both products aim to reduce the friction of deploying AI in enterprise environments, though Jedify’s focus on autonomous agents and plain-language interactions sets it apart.
What this means
Lazyfounder analysis — our interpretation, not reported fact.
For founders and operators, Jedify’s Data Agents represents a shift in how businesses might deploy AI for analytics. Unlike generic AI assistants that rely on raw schemas, Data Agents’ context graph could reduce the time and cost associated with querying complex datasets. This may appeal to companies struggling to extract value from AI investments, particularly those in industries with heavy data operations, such as gaming, digital media, or e-commerce.
The product’s permission-based data access and integration with external platforms like Slack could also address compliance and workflow challenges that often stall enterprise AI adoption. However, its success will depend on whether businesses are willing to adopt a new model for analytics rather than relying on traditional dashboards or standalone AI tools.
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Sources
- SiliconANGLE · 2026-10-28
Jedify formally launches Data Agents for building AI analysts in plain language
This story is an original summary drafted with AI by Lazyfounder from the reporting listed above and checked by automated validation. Facts are attributed to their original publishers; sections marked as analysis are Lazyfounder's. Where a source is in another language, facts were machine-translated and quotations are reported, not reproduced. Read the original coverage via the links, and see our AI policy and corrections policy.
About the author
Editor, Lazyfounder
Tarun Mottlia edits LazyFounders, covering Indian startups, funding rounds, AI and product launches. Every story on the site is AI-assisted and checked against its cited sources before publication.
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