At EONIQ, we are convinced that AI agents can bridge the gap between powerful enterprise software and the people who use it. A recent customer project shows exactly how.
The Challenge: From Specialist Tool to Everyday Usability
One of our software company partners had built a highly flexible data aggregation tool designed for dynamic data accumulation across complex, customer-specific models.
The problem? The tool was so powerful that only administrators with deep technical expertise could use it effectively. Management-level users — the people who needed the insights most — struggled with the complexity.
The company wanted to change that: making the tool more intuitive, intelligent, and broadly accessible.
Our Solution: An AI Copilot for Data Aggregation
Working closely with the customer, we implemented a CoPilot-like AI Agent that transforms the way users interact with the system. Instead of clicking through complex menus, users can now ask questions naturally.
Here’s how the agent works:
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It assesses the query – is it a simple lookup or does it require multiple sources?
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It plans the optimal workflow – for example, combining API calls, documentation cross-references, or targeted web searches.
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It executes the steps autonomously and delivers clear, actionable insights within seconds.
This adaptive reasoning, similar to “deep research” approaches in generative AI, ensures that the agent can handle anything from quick lookups to multi-step investigations — without the user worrying about the underlying complexity.
The Impact: Precision, Adaptability, and Adoption
By adapting dynamically to customer-specific data structures, the agent removes the need for manual configuration and unlocks entirely new use cases.
The result?
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Broader adoption of the software beyond admins — managers and decision-makers now rely on it for daily insights.
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Faster workflows with technical hurdles removed.
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A shift from “specialist tool” to strategic asset across the organization.
The software company’s CEO summarized it best:
“It is impressive to see the agent analyzing the task, splitting it into a plan, and then working on it step by step. The precision and adaptability of the solution exceed our expectations.”
Our own CEO, Matthias Berlit, reflected on the project:
“This project exemplifies how AI can democratize access to powerful but complex systems. By automating the heavy lifting of data navigation, we’re enabling users to focus on insights rather than technical hurdles.”
Why This Matters
This success story is more than just a customer project — it shows how AI copilots can modernize user interfaces in enterprise software. Instead of redesigning systems from scratch, companies can use AI agents as an intelligent interaction layer, unlocking value and improving user experience.
