Jackrabbit Ops – AI that responds to leads (RFQs) and book meetings

Hacker News

Hi HN! My name is Ahmed, I am a developer based in Canada. Over the past years, building custom software, I noticed a real flaw in how RFQs are handled by companies. By the time companies respond to an incoming lead it would have already went cold. Adding an automated email response, was never adequate in actually qualifying a lead and there were never enough trained team members to respond fast enough. That is why we decided to build Jackrabbit Ops - the AI that will respond to leads and book meetings , just like your best sales person. > 100% of RFQs get personalized replies within an hour. > Qualifies leads and identifies the customers with intent to buy. > Books Meetings for you. check it out: https://www.jackrabbitops.com

Visit product View on Hacker NewsJun 22, 2023

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best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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