Ac

Across – manage all your customer support in Slack

Hacker News

Across – manage all your customer support 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 love creating shared Slack channels with our customers, but 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. Across has ticketing and task management 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.

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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: slack · Missing: mac, agents, macos
91%91% 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
84%84% 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
51%51% 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 · Missing: mobile apps, ios, personal
45%45% 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
18%18% 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.

Incorrect prediction on native model

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