Jedify Inc. has officially launched Data Agents, an AI-driven solution that allows business teams to build autonomous analysts by expressing analytic needs in natural language, addressing bottlenecks in traditional BI software.
- Data Agents use Jedify’s context graph for unified business data modeling.
- Users create AI analysts by describing tasks in natural language without coding.
- Integrates with Claude, ChatGPT, Slack, and supports scheduled reporting.
What happened
Jedify Inc., an enterprise AI context startup, has launched Data Agents to empower business users and data teams to create autonomous AI analysts by simply describing what they want done using plain language. This product bypasses the traditional reliance on pre-built dashboards and manual BI queries by translating user requests directly into AI-driven analysis based on Jedify’s unified data context graph.
The Data Agents run on a context graph that models an entire business’s data and documents, handling schema joins and permissions internally. Users can trigger agents on demand, by schedule, or when predefined conditions are met. The product is also designed to integrate seamlessly with conversational AI platforms like Claude and ChatGPT, as well as workflow tools such as Slack and email.
Why it matters
Data Agents represent a significant shift in how enterprises approach analytics, moving beyond static dashboards to proactive, AI-powered analysis tailored to the specific needs of business teams. By enabling users to build AI analysts without data science expertise, Jedify broadens analytic capabilities across finance, sales, and operations roles while preserving security through permission-based data access.
Additionally, by controlling token usage through pre-modeled schema and graph context, Jedify offers a more cost-efficient solution compared to generic AI assistants that parse raw schemas on the fly. This efficiency, combined with multi-platform compatibility, encourages widespread adoption and automation of repetitive data interrogations, freeing analysts to focus on higher-value tasks.
What to watch next
Enterprise adoption of Data Agents will be key to validate its ability to scale AI-powered analytics in diverse business functions and environments. Case studies like RubyPlay Ltd. already demonstrate how agents can autonomously monitor markets and generate useful insights without manual intervention, an encouraging sign of practical impact.
Looking ahead, Jedify’s progress in expanding integrations with major AI and workflow platforms, refining natural language understanding, and enhancing real-time anomaly detection will determine how broadly Data Agents transform enterprise intelligence workflows. Tracking new partnerships, funding milestones, and client deployments can reveal the momentum behind this next-gen BI automation approach.