Reviewers mostly praise Giggal.ai for accurate catch-all verification, low bounce rates in real campaigns, and results reliable enough for both list cleaning and live signup flows. Several say the API is easy to integrate, fast enough for production use, and backed by unusually responsive support when technical issues come up. The main complaints are about speed on larger jobs, limited concurrency, and a lack of deeper reporting, confidence scores, and clearer reason codes. A few later reviews appear mixed with TargetPulse feedback, so the clearest sentiment is around accuracy and support.
Hi Product Hunt! 👋 I'm Hassaan, maker of Giggal.ai
Every email verifier works fine on normal domains. The moment a domain is catch-all, and in B2B that's easily a third of your list, they stamp it "Risky" and charge you anyway. So you either delete leads that were actually fine or bounce off ones that were dead.
We built Giggal.ai around that exact gap: a deep multi-layered scoring engine that returns a real Valid or Invalid verdict, even for mailboxes sitting behind SEG gateways like Proofpoint, Mimecast and Barracuda.
So far we've verified 100M+ emails at 98.5% accuracy, and our users keep their bounce rates under 3%.
New with this launch: an MCP server, so you can verify emails without ever leaving Claude or ChatGPT. Ask your assistant to check a list, get verdicts back, keep working. There's a full developer API too if you'd rather build it into your own stack.
You can try it on 1,000 emails free, no card, and the credits never expire.
I'd love your honest feedback. What matters most to you when you're cleaning a list? And what would make Giggal a no-brainer in your workflow?
Thanks for checking us out! 🚀