Ac

Across – Supercharge your customer experience in Slack

Hacker News

Across – Supercharge your customer experience in Slack

Hi HN! We're Parthi and Kunal from Across (https://acrossapp.com). Across allows you to scale high touch experience you provide your customers when you create shared Slack channels with them. With Across, you can track customer requests, automate responses and message customers across Slack channels. We've built and shipped 7 products recently and we always create shared Slack channels with our customers. This is great for creating a tight feedback loop between us, and making sure we're extremely responsive to customer requests. The problem is that this experience doesn't scale. When you have 20+ Slack channels with customers, it becomes impossible to remember who to follow up with, who on your team is responsible for which customer request, etc. And there aren't great integrations between Slack and existing task management and bug tracking tools today (most just allow you to create a ticket). So we hacked together a solution for ourselves. With Across, we've built a ticketing and task management system on top of Slack, so you can keep your team organized and on top of all customer requests. You can reply directly from a ticket and we'll automatically post the response to the right channel, customer, and thread, so you never lose track of a conversation again. This is just the start! We're looking into building a whole suite of tools and integrations on top of Slack to make customer engagement better. Looking forward to hearing what you'd want us to build next. Thanks for checking us out!

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Actual performance

14points
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack · Missing: mac, agents, macos
93%93% 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 · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
55%55% 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
41%41% 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
36%36% 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
17%17% 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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