The Problem
Every sales team needs local business leads, but manually searching Google Maps and copying data is painfully slow. I needed a way to extract hundreds of leads in seconds.
What I Built
I built GMapsScraper AI — a tool thatuses AI to extract business data from Google Maps at scale.
Tech stack:
- Next.js 14 (frontend)
- Cloudflare Workers (deployment)
- Supabase (auth + database)
- Go-based scraper (backend on Google Cloud Run)
How It Works
- User enters a keyword + location (e.g. "dentist, New York")
- Backend spins up a headless browser to crawl Google Maps
- AI processes and structures the raw data
- Returns clean results: name, phone, email, website, rating,
reviews - One-click CSV export for CRM import
Key Technical Challenges
Rate limiting — Google Maps aggressively blocks automated requests. Solution: rotating proxies + randomized delays + browser fingerprint rotation.
Data accuracy — Raw scraped data is messy. Used AI to normalize phone formats, validate addresses, and deduplicate results.
Speed — Users expect results in seconds. Implemented parallel scraping with a job queue system to handle concurrent requests.
Results
- 200+ leads extracted per search
- Average response time under 30 seconds
- Supporting 18 languages and global locations
Try It
If you're building outreach tools or need local business data, check it out: gmapsscraper.io
Free trial available — no credit card required.