Zero-Cost AI Market Intelligence & Crisis Radar Engine
Developed an auto-updating AI Market Radar to replace manual daily industry research and trend tracking. The pipeline continuously monitors high-intent industry developments across public news vectors, triages signals using an LLM, and instantly pushes high-priority alerts to instant messaging channels while cataloging routine updates in an internal database. ⏱ Cuts manual research time by 85–90%, saving 10–12 hours/week while giving leadership real-time visibility into industry shifts and critical technical events without manual monitoring.
11 Jul 2026
Keywords
AI Usage
AI Integration & Workflow Architecture: • Automated Context Processing: Integrated a Google Gemini model via an n8n AI Agent node to dynamically read, analyze, and triage unstructured text signals from live market data streams. The AI handled the classification of sentiment (Positive/Negative/Neutral) and crisis severity levels based on strict contextual parameters. • Structural Control: Prompt-engineered the LLM to output pure, deterministic JSON objects, removing conversational prose to ensure a clean data stream. Manual Execution & System Engineering: • Zero-Cost Ingestion Setup: Manually designed and configured an RSS engine using custom URL-encoded Google News search parameters to filter exclusively for high-intent automation keywords without reliance on expensive third-party scraping APIs. • Data Alignment & Engineering: Wrote a custom JavaScript node to flatten asynchronous row-split data structures, manually binding the original source metadata (URLs, titles, dates) back to the AI-generated sentiment properties. • Routing Logic: Manually configured Switch nodes and multi-branch data pipelines to partition data streams between Airtable logging repositories and high-priority instant messaging webhook alerts.
