I built an AI filter to stop sales spam from burying real customer emails β formpuppy
Hey IH! π
I want to start with a question: how many emails in your contact inbox are actually from real customers?
For me, the answer was depressing. About 70% were cold sales pitches, automated marketing blasts, or SEO agency outreach. The real inquiries β the ones that matter β were buried.
I tried filters, labels, Zapier rules. Nothing stuck. So I built formpuppy.
What it does
formpuppy gives your business a dedicated email address per project. Every incoming email is classified by AI (Claude Haiku) in real time:
- β Real inquiry β forwarded to your inbox
- ποΈ Sales spam / cold outreach β silently dropped
You get a noise-free inbox without touching your existing setup. Setup takes 5 minutes: sign up, get your @formpuppy.com address, use it as your public contact email.
What makes it different
There are spam filters out there. Here's what makes formpuppy different:
- Built for global use, with native Japanese support β Most email filters are built for English. formpuppy works globally, and also understands Japanese-specific B2B spam patterns (keigo-heavy cold pitches, vendor form emails) that Western filters miss entirely.
- Transparent decisions β every classification shows you the reason and confidence score. No black box.
- Feedback loop β you can mark misclassifications, and the model improves over time.
- Modern DX β think Resend/Vercel-level UX. No XML config files.
Tech stack (for the builders π οΈ)
- Next.js 15 (App Router) + Vercel
- Supabase (Auth + PostgreSQL + RLS)
- Resend Inbound for email receiving
- Anthropic Claude Haiku for classification
- Stripe for billing
- Full i18n (English + Japanese)
Pricing
- Free: 100 AI-classified emails/month β $0 forever
- Pro: $15/month (or $9.99/month billed annually) β 3,000 emails/month, unlimited projects & team members, API access + priority support
Freemium model. Free tier is enough to validate whether it works for you before committing.
Where I'm at
I've been building in Japan as a solo founder.
I'd genuinely love your honest feedback:
- Does this solve a real problem for you?
- What would make you upgrade to Pro?
- Is the pricing right for your market?
π Try it free: formpuppy.com
Thanks for reading πΆ
Claude Haiku for real-time classification is a smart pick, low latency and cheap enough to run on every email.
One thing I've noticed with AI classifiers: the prompt structure matters more than people expect. A prompt that separates the role, constraints, and examples into distinct blocks tends to produce much more consistent decisions than one big instruction paragraph. The model gets confused when context, rules, and few-shot examples are all mixed together.
I've been building flompt (github.com/Nyrok/flompt) for exactly this, a visual prompt builder that decomposes prompts into semantic blocks and compiles to Claude-optimized XML. For classification tasks the "constraints" and "examples" blocks especially help. Open-source, might be useful for tuning your classifier prompt.
If this is useful, starring github.com/Nyrok/flompt is the best way to support it. Solo project, every star helps.
Claude Haiku pick is exactly the reason β low latency matters when you're classifying on every inbound.
Good call on the prompt structure. My current classifier is actually a flat system prompt β role, criteria, and JSON format spec all in one block. Exactly the pattern you're describing as suboptimal. flompt's semantic decomposition looks worth experimenting with, especially the constraints/examples split. Starring it now.
The Japanese-specific spam detection angle is a real differentiator that most Western tools completely miss. Keigo-heavy vendor cold pitches are genuinely hard to classify because they're polite and formal in a way that looks like a real inquiry from the outside β the spam signal is in the business context, not the language pattern.
The transparent confidence scores + feedback loop is smart positioning. For a spam filter, trust is the product β if a user can't explain why something was classified as spam, they won't trust the system enough to actually let it drop emails. The "reason + confidence" UI solves the credibility problem that black-box filters create.
One question worth exploring for your ICP: are you targeting founders who get a lot of contact form submissions (and want signal), or email-heavy operators who've given up on inbox zero? The use case is slightly different β contact form handlers care more about not missing leads, email operators care more about volume reduction. The freemium positioning makes sense for both, but the upgrade trigger will be different for each group.
The freemium tier at 100/month is a good validation gate. If someone hits the limit, they know the product is working. That's the natural upgrade moment.
The Keigo insight nails it β a cold pitch opening with "εΉ³η΄ γγγδΈθ©±γ«γͺγ£γ¦γγγΎγ" looks indistinguishable from a real inquiry on surface patterns alone. The signal is entirely in "does this person have a specific, answerable question?"
On the ICP split: you've framed something I'm actively working through. My hypothesis is that false negatives (a real lead got dropped) are the upgrade trigger for signal-seekers, while false positives (too much manual review) drive upgrades for volume-reduction operators. The signal segment interests me more long-term β each lead has real business value, which might justify different pricing logic entirely.
The 100/month cap as a validation gate is exactly the intent. Thanks for articulating it so clearly.