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Asking – A simpler customer support inbox for small teams

Hacker News

Asking – A simpler customer support inbox for small teams

Hi HN — I’m William, a frontend engineer from Brazil, and for the past few months I’ve been building Asking. Asking brings customer conversations from different channels into one place. It is intended for small teams that need a proper support workflow but don’t want the cost and complexity that usually come with larger platforms. To be transparent, Asking is built on top of Chatwoot. I’m not trying to present its messaging infrastructure as something I created from scratch. I’m using it as a foundation and building a more opinionated product around it, with simpler onboarding, inbox configuration, subscriptions, email setup, and a less overwhelming experience overall. The project started with a question: how much customer support software does a small business actually need? Many products are initially simple, but become expensive as the team grows. Others provide every feature imaginable, but require small companies to adopt workflows and concepts they may never need. With Asking, I’m trying to find a better balance: enough structure to manage customer support properly, without turning it into an enterprise system. One question I’m still exploring is whether small teams genuinely want fewer features, or whether they want the same capabilities with much less friction. The product is still early, and I’d genuinely appreciate direct feedback — especially about what Asking would need before you could consider replacing your current support tool with it.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: inbox, email, using · Missing: mac, agents, macos
86%86% 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: created, started · Missing: supports, reddit linkedin, podcasting
57%57% 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: ide, io · Missing: https docs, excited, just released
45%45% 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: month · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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
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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