Echowin – AI Powered Call Management

Hacker News

Hey Everyone, Just wanted to share something me and my team have been working on for the last 6 months. I quit my software engineering job at Amazon, and my co-founder quit his aerospace engineering job to build echowin, and it feels amazing to finally get to a phase where we can share it with the world. echowin picks up phone calls on behalf of your business, and answers the caller's questions and performs business tasks. Our goal is to help businesses stay efficient by saving time, money and resources by automating call handling. We are using multiple models like GPT3, Flan-T5 and others behind the scene to make the magic happen. We have focused on building a solid platform that is extensible and customizable to cater the needs of any type of business. Here are some of the key capabilities of our platform: - Full natural language understanding. - Personalized dynamic responses for caller's queries. - Free form text knowledge-base that the system uses to answer any questions. - Growing library of "skills" to add more functionality. - Live call transcripts and logs. We are now available in US and Canada, and are planning on expanding to other countries in the future. I would be happy to answer any questions you may have, and would appreciate any feedback or comments as well. - K Subedi

Visit product View on Hacker NewsFeb 21, 2023

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, month, answers · Missing: mobile apps, ios, entrepreneurs

65%65% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, efficient, calls · Missing: plus, intuitive, reviews

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

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

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

12%12% 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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