Founder Signal
Real-time startup intelligence powered by AI automation
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problem
Startup insights are fragmented across blogs, news, and feeds, making it difficult to detect meaningful trends or emerging opportunities quickly. Users often spend hours scanning sources without a structured view of what matters.
solution
I designed and built a signal detection pipeline that automatically collects data, classifies events, and presents them in a clean dashboard with filtering and insights. The system combines automation workflows, structured storage, and a user-friendly interface to turn raw information into actionable intelligence. Built as an intelligence layer first and the interface exists to make the pipeline usable, not the other way around.
Click here: FounderSignal dashboard
Founder Signal transforms noisy startup activity into a structured intelligence layer.
Architecture Highlights
Automated ingestion via RSS pipelines
Signal classification using rule-based + AI logic
Data normalization and deduplication
Persistent storage for historical tracking
Real-time dashboard for signal exploration
Automated digest generation
My Contributions
Designed the system architecture and data flow
Built workflow orchestration using automation tools
Implemented signal detection and filtering logic
Structured storage for scalable querying
Integrated front-end dashboard for visualization
Ensured reliability through deduplication and validation
Validation
Shared with a small group of early testers, who used it to track startup activity across their areas of interest. Feedback confirmed the core value by surfacing relevant signals without manually scanning scattered sources and is now shaping the next round of refinement.
Key Learnings
This project reinforced principles of building production-style AI systems, including handling real-time data, designing resilient pipelines, and bridging backend intelligence with usable interfaces.
year
2026
timeline
Just a weekend job
tools
n8n, Automation pipelines
category
Autonomous AI
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