A corporate card for life science companies

Hacker News

Hi HN, Henrique here from Brex. We just launched a corporate card specially tailored for life sciences companies ( https://brex.com/industry/life-sciences ), and every time we launch something new we get a lot of questions about how we chose to prioritize this feature or product, so I thought it’d be interesting to share here with the HN community. While I’ve previously posted about launching our initial tech startup card ( https://news.ycombinator.com/item?id=17418813 ), the decision to launch Life Sciences was much different in that we already had two existing products in the market with their own long list of improvements we wanted to make to them. Here’s my blog post going into much more detail (couldn’t fit it here): https://brex.com/blog/how-we-chose-our-third-vertical . Would love to get your thoughts and feel free to ask any questions you might have.

Visit product View on Hacker NewsJun 18, 2019

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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% 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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