MailAI – Personal AI agents in secure sandboxes for email automation

Hacker News

Hey HN, We built MailAI - personal AI agents that run 24/7 in secure sandboxes to automate email workflows. MailAI is CASA-verified for enterprise security. The Problem: Every "AI email tool" just gives suggestions. We wanted agents that ACTUALLY DO THE WORK. Technical Innovation: - Agents run in isolated, secure sandboxes - MailAI is CASA-verified for enterprise security - Complete isolation (your data never leaks) - 24/7 execution (not just suggestions) What It Does: - Auto-responds to customers using your knowledge base - Tracks invoices and sends payment reminders - Coordinates meetings (checks your calendar) - Handles custom workflows in plain English Real Example: "Every morning at 9am, summarize all unread emails marked important" "Track all invoices and remind clients 3 days before due date" "Auto-respond to customer support emails using our FAQ docs" Why Sandboxes Matter: Unlike other tools that run on shared infrastructure, our sandboxes provide complete isolation. Your agent can only access what you explicitly grant - nothing more. This prevents data leaks, unauthorized access, and cross-contamination between users. Try It: 7-day free trial: https://mailai.live We'd Love Feedback On: 1. Security concerns (we're paranoid about this) 2. Use cases we're missing 3. Technical implementation questions Built by two founders and one founding member who were drowning in emails. Happy to answer any questions!

Visit product View on Hacker NewsOct 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, users, para · Missing: mobile apps, ios, entrepreneurs

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

27%27% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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