Don't guess what to build. Mine it from reviews. | NeedRadar

Unlike basic review aggregators that only show star ratings or word clouds, NeedRadar uses LLMs to deeply understand pain points, feature requests, and intent from hundreds of thousands of app reviews. It covers not just App Store and Google Play, but also Chinese stores like Huawei, Xiaomi, vivo, OPPO, Coolapk, and TapTap. Each opportunity gets an ROI score based on frequency × severity × user value × competitor gap.

Framer — Launch websites with enterprise needs at startup speeds.

Launch websites with enterprise needs at startup speeds.

I spent years as a product manager reading thousands of app reviews manually. It was exhausting — 90% noise, buried signal. Worse, we kept building features users didn't ask for, while their biggest frustrations (like offline mode) went ignored for months because nobody had time to mine reviews across multiple app stores. Then I tried existing tools. They showed word clouds and sentiment scores, but never told me what to build next. So I built NeedRadar. The first version only analyzed English reviews from App Store. Then we added semantic clustering, then ROI scoring. The hardest part was training the LLM to distinguish “nice to have” from “urgent pain” and to handle 8+ app stores (including Chinese ones with different review formats). Now, anyone can enter an app name and get a ranked list of feature opportunities — backed by real numbers, not gut feelings. We're in closed beta. Join the waitlist and lock early-bird pricing.

Spent time digging through reviews before, so this resonates. Like that it turns feedback into clear next steps instead of just charts. Just upvoted and rooting for you, this would have saved me a lot of time.

Framer — Launch websites with enterprise needs at startup speeds.

Launch websites with enterprise needs at startup speeds.