Market Intelligence Agent
Overview
Earlier in my career working in strategy and competitive intelligence, I spent hours scanning news feeds, product launches, and industry updates just to stay on top of what was happening in the market. I built this system to automate that process continuously collecting signals across the AI ecosystem and turning them into structured research insights.
Problem
Important industry signals are scattered across news sites, research papers, product launches, and community discussions.
Analysts end up juggling tools like Feedly, Google News, LinkedIn, and newsletters — manually tracking updates and compiling executive summaries. By the time insights reach leadership, the information is often already days or weeks old.
Results
• 10× faster signal processing compared to manual research
• 50–100 signals analyzed per day through automated ingestion workflows
• 90% reduction in manual monitoring across newsletters, news sites, and product launches
• Automated research summaries instead of manual tracking
How it Works
The system collects signals from news sites, launch feeds, and research sources
n8n workflows ingest and normalize the data
Claude analyzes updates and extracts key insights
Signals are stored in a structured Supabase research database
The system generates trend summaries and research reports
Tech Stack
n8n (Automation) • Claude (AI Analysis) • Supabase (Database) • GitHub (Version Control)
