Hey PH 👋

I'm Damian, Solo founder, 8 months building DataBridge on nights and weekends.

Why I built it

kept seeing email lists where 10-20% of the rows were broken. Typos like gmial.com, role accounts, disposables, dead domains. The cleaners I tried treated it as a black box - upload a list, get back "84% valid!" and a bill. No reasoning, no way to fix what was fixable. I wanted one that showed its work.

What it does

Runs 8 validators per address: syntax, MX records (does the domain actually have a mail server?), typo detection, role accounts, disposables, duplicate detection, no-reply patterns, risky TLDs. Each rejection comes with a sourced reason so you can see exactly why before deciding what to do.

The part I'm most proud of is auto-repair. Instead of throwing out gmial.com addresses, we suggest the fix (gmail.com) and you approve in one click. Recovers 3-8% of typical lists that other cleaners just delete.

Stack
Next.js 15 + Tailwind v4 + Supabase, async validation worker on Railway, Paddle for billing. The validation logic was the easy part. CSV parsing nearly killed me - BOM markers, mixed encodings, escaped commas, header rows that aren't really header rows. Rewrote the parser 3 times before it stopped crashing on real customer files.

Pricing

Free tier 1,000 rows/month no card. Pro $29/mo for 100k rows + auto-repair.

Brutal feedback welcome, especially from anyone running newsletters or B2B email. Two things I'd love feedback on: what audit fields you'd want that I don't have yet, and what would actually make you trust an "invalid" verdict enough to delete the row.

(Especially curious about the second one - it's the trust gap I haven't fully figured out yet.)

Thanks for reading 🙏