Segment for Payment

Hacker News

Segment for Payment

While it’s hard not to love Stripe and its operational excellence, they're often not the sole payment processor a (big) company uses. Companies with scale tend to integrate with a handful of payment processors, gateways, and banks to manage payment inflows and outflows through a distributed architecture. On top of integrating across many payment systems, companies need to manage cross-border conversions, payment failures, transaction immutability, and many other issues. We are building a cloud-native service serving as the undergirding infrastructure for payments. If you are handling payment for a company using multiple processors / gateways and you are facing problems, ping me at maxime.manseau (at) hotmail dot com Would love to have a chat with you and show you what we ve built so far.

Visit product View on Hacker NewsDec 16, 2020

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best fitHighest predicted score across all platforms for this description.

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

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

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

42%42% 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

31%31% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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