Extract & Search ProductHunt Data with Bright Data MCP & Gemini AI

This workflow enables automated discovery and analysis of new tech products on ProductHunt. It uses Bright Data's Model Context Protocol (MCP) to mimic real-user behavior for reliable data extraction, avoiding blocks common in traditional scraping. An AI agent then performs contextual Google searches for each product to gather use cases, reviews, and related info, followed by Google Gemini structuring the results into human-readable insights.

Key benefits include time savings on manual research, reduced errors, and scalable processing for multiple categories. Outputs are versatile: save to disk, update Google Sheets, or trigger webhooks for alerts/integrations. Ideal for startup analysts, VCs, marketers, recruiters, product managers, and AI enthusiasts needing real-time product intelligence.

Start by inputting a ProductHunt category or search term in the Input Field Node. The Agent Node handles extraction, enrichment, and analysis seamlessly. No more repetitive manual validation—get structured data ready for decision-making.