LensAI as a marketing tool that grabs every customer's attention

Hacker News

Hi, Y Combinator Community! I am Pavel, a co-founder of LensAI. I want to introduce you to a new affiliate marketing platform I am currently building with my team of engineers that turns image and video content into an interactive shopping experience. We are building the LensAI product to give Advertisers an innovative way to turn around the current tendency of their ads being ignored. LensAI Technology's prime focus is to oppose causes that minimize the chances of the advertisement being viewed. We aim to boost any advertising strategy as we fight ad blindness and ad irrelevance and provide Advertisers with a tool that helps them to sell their products in the times of the world's overproduction. Studies show that desire to shop often comes from the visual content we explore, that is why we created a new ad format that is hyper-relevant to the content and being delivered in that perfect moment of the content inspiration. We taught AI to perform both content and context analysis. We also enabled it with a function to automatically deliver our AI-powered ads, and they are embedded straight into the detected product from image or video content in real-time! Here is the project link https://lens-ai.com Please, make sure you stop at Slide #2 at lens-ai.com to see how my technology works on the famous "Diana" movie trailer. We are on ProductHunt: https://www.producthunt.com/upcoming/lensai Let me know your thoughts!

Visit product View on Hacker NewsApr 21, 2020

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

88%88% 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 · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, way · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% 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 · Strong signals: active · 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 · Strong signals: introduce · 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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