Capalyze

Capalyze is an AI data analysis tool from Hong Kong's Dreamnum Inc., the international version of Biaoda (biaoda.me). It brings web scraping, natural language Q&A, and data visualization into one workspace. With no coding required, everything from scraping Amazon competitor data to generating visual reports is driven by natural language, built for e-commerce operations and market research teams.

Core Capabilities

• Real-time web scraping: Covers 10+ platforms including Amazon, eBay, LinkedIn, TikTok, and Reddit, with no configuration needed
• Natural language analysis: Ask in English or Chinese to run sentiment analysis, trend identification, and competitor comparison—no formulas or code
• Interactive dashboards: Supports word clouds, line charts, heatmaps, and more, generating shareable reports in one click
• Source transparency mechanism: AI answers link to the original data, reducing hallucination risk and keeping results traceable
• Agent orchestration: Complex tasks are automatically broken into scraping, enrichment, analysis, and visualization subtasks that run in parallel
• Multi-source data import: Supports Excel/CSV uploads and URL pasting, with AI automatically converting them into structured spreadsheets

Use Cases E-commerce sellers analyzing competitor reviews, market researchers tracking industry sentiment, and content operators mining Reddit/YouTube trends. Small and mid-sized teams and individual researchers without coding skills are the best user groups.

Unique Advantages While Octoparse can only scrape and Julius AI can only analyze local files, Capalyze connects the complete chain from web data to insight reports, priced at just one-fifth of Octoparse.

Editor's Review Product Hunt #1 of the day and week, with a 4.6/5 user rating (45 reviews), described as a "cheat code for spreadsheet power users." It is built on the open-source Univer engine (27,500 GitHub stars), so the technology is solid. The lack of an API is the main shortcoming, but for no-code users the value for money stands out.