Crimson Aegis – Data Loss Prevention for Slack

Hacker News

Hi HN community! I just launched my first paid SaaS, https://crimsonaegis.com . Crimson Aegis is a data loss prevention (DLP) integration for Slack. It checks your Slack messages to make sure you aren't posting sensitive information like credit card number and social security numbers and gives the option to remove messages. Use the promo code HN20 for a 20% discount on your subscription when you sign up! About the stack: * 100% Elixir with Phoenix * Postgres for DB * Hosted on Google Cloud Any feedback is greatly appreciated!

Visit product View on Hacker NewsJul 16, 2017

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, google, code · Missing: mac, agents, macos

58%58% 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.

TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas, subscription · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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