Case study
From weeks to days:
How AI agent workflows transformed market research
The bottleneck in market intelligence isn't finding information - it's extracting meaningful patterns from the noise at the speed business requires.
Starting point
The market intelligence industry operates on a fundamental tension: the need for comprehensive, accurate insights battles against the reality of ever-accelerating market changes. Technology companies require deep understanding of competitors, emerging trends, and market shifts - but traditional research methods can't keep pace with the speed of innovation.
A leading market research company found themselves at the center of this challenge. Their analysts spent weeks manually processing information from websites, press releases, and industry publications to compile market intelligence reports. The company had world-class analysts and deep industry expertise, but their manual processes meant insights often arrived after the market had already moved.
The triple engine of automated intelligence
Approach
Rather than incrementally improving existing processes, we introduced AI Agent Workflows - our GenAI-powered tool built in-house - to fundamentally transform how market intelligence gets created. The solution automated the entire intelligence pipeline: from data collection through analysis to visualization.
The implementation focused on three key capabilities:
- Intelligent extraction: AI agents automatically gather and process unstructured data from diverse sources
- Relevance scoring: The system detects specific technologies, OEM initiatives, and early signals while ranking insights by importance
- Visual intelligence mapping: G.Studio, our graph-based visualization solution, transforms raw data into relationship maps highlighting key stakeholders and emerging trends
We didn't just automate existing workflows - we redesigned the entire intelligence creation process around AI capabilities, enabling analysts to focus on strategic interpretation rather than data processing.
Breaking The Linear
AI agents don't just collect data - they understand context, detect patterns, and assign relevance automatically. Analysts receive pre-processed intelligence with relationships already mapped and trends highlighted. They start their work where traditional processes end: with strategic analysis rather than data gathering. The system learns from analyst feedback, continuously improving detection accuracy.
Operating at market speed, not research speed
Summary
We transformed the company from an organization constrained by manual research processes into one empowered by AI-augmented intelligence capabilities. The shift goes beyond efficiency - it's about operating at market speed rather than research speed.
The client now approaches market intelligence fundamentally differently. Instead of asking "how quickly can we compile this report?" they ask"what emerging patterns should we investigate deeper?" Their analysts shifted from information processors to strategic advisors, and their clients receive insights when they're still actionable, not when they're already history.
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