Assindo – AI Assistant That Handle Your Phone Calls

Hacker News

Hi HN, I created Assindo , an AI-powered virtual assistant to automate phone calls for busy professionals and parents (MVP). Balancing work with personal responsibilities left me losing time to missed calls, hold times, and scheduling tasks. Hiring a human virtual assistant was costly, so I built Assindo to automate and manage these phone-based tasks, freeing up my focus while ensuring no important call or task slips through the cracks. ### How It Works - Outgoing Calls: Assindo can schedule appointments, handle customer service calls, and make reservations, navigating IVR menus (press "1" to ...), managing hold times, and leave voice mails. - Incoming Calls: It pickup on your behalf, transcribes the discussion, summarizes key info, and sets up follow-up tasks – so no critical call is missed. ### Next Steps I just applied to YC 2025 (fingers crossed!) and am looking for early adopters to try Assindo and share feedback, as well as suggestions on new features to improve the experience. Try it out at https://web.assindo.com/ or visit https://www.assindo.com for more info. Thanks, and I’d love any feedback! Android and iOS will be released in few days, you can join the waitlist: https://www.assindo.com/waitlist

Visit product View on Hacker NewsNov 12, 2024

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Launch Intel predictions

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

80%80% 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, ios · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, 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

40%40% 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

16%16% 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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