We

We made an AI shopping consultant for online stores

Hacker News

We made an AI shopping consultant for online stores

Hi HN! We are excited to introduce you to Octocom! Octocom functions as a shopping consultant for e-commerce stores, providing product recommendations and answering customer inquiries - exactly like a shopping consultant in a brick-and-mortar store. My brother and I previously owned an e-commerce store specializing in sailing equipment. A significant portion of our time was consumed answering customer support questions. We realized that no matter how much information we provided on our website or how clearly it was displayed, customers still sent emails because many of them didn't bother to read the website. When OpenAI released their API, we decided to try develop our own chatbot for our store, and the results were amazing. Upon noticing our bot, some of our customers started reaching out to us, curious about who made it. After some more time passed, we decided to create the bots for them as well and transition to working full-time on Octocom. You can test out one of our customer’s bots (they sell smart business cards) here: https://www.octocom.ai/try/tolink Our goal with Octocom is to replicate the experience of consulting with a sales assistant/shopping consultant in a physical store. You just ask a question and receive a direct answer, eliminating the need to read through product descriptions or do a research to make a buying decision.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
88%88% 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: email, openai, physical · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, introduce, smart · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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