Case Study: Data Analysis Engine for GE College
Overview
We developed and actively maintain a powerful, agentic, LLM-driven data analysis engine for GE College—an internal system designed to dramatically speed up how their team works with data, insights, and AI-powered strategy.
GE College already uses AI tools to analyse performance and plan direction. Our solution doesn’t replace their process; it supercharges it, reducing hours of data gathering and analysis to just seconds.
The Challenge
GE College collects data from multiple sources: marketing channels, analytics pipelines, and various internal systems. Even with existing AI tools, preparing the right dataset still requires manual effort—deciding where to pull data from, filtering it, and formatting it before analysis.
They needed a system that could:
- Automatically determine which data sources to use
- Reduce the time spent gathering and preparing data
- Allow anyone to query data in natural language
- Speed up their existing AI workflows
- Centralise insights across marketing and operational channels
Our Solution
We built an agentic-first Data Analysis Engine, designed specifically to integrate with GE College’s AI stack and data ecosystems.
Key Features
Agentic Data Retrieval
When someone asks a question, the engine doesn’t just search one location—it decides where to fetch information.
It evaluates the intent of the natural-language query, then automatically selects the right datasets from marketing platforms, analytics sources, CRMs, and internal systems.
Deep Marketing & Analytics Integration
The engine connects directly to their marketing channels and analytics pipelines, continuously syncing and structuring data.
Natural Language Querying
Team members can ask questions like:
“Show me which campaigns performed best among postgraduate leads last month.”
The engine handles the entire process—interpreting the request, selecting data sources, fetching the data, and giving back usable insights.
Accelerated AI Insights
Because the system prepares the right data automatically, GE College’s LLMs can analyse and produce insights instantly.
What once took hours of setup now happens in seconds.
Impact
Since implementing the engine, GE College has achieved:
- Much faster analysis and planning cycles
- Higher accuracy in marketing and operations decisions
- Accessible insights for non-technical staff
- Reduced manual effort in preparing datasets
- Stronger utilisation of their existing AI tools
Conclusion
The agentic-first Data Analysis Engine has transformed how GE College uses their data. By letting AI decide what data to fetch and where to fetch it—then enabling natural-language querying—it turns slow, manual processes into rapid, conversational insight generation.
Sulta Tech continues to maintain and enhance the system, ensuring GE College’s AI-powered decision-making remains fast, efficient, and ahead of the curve.