FlawPulse - Turn real complaints into your next product.
byβ’
Type any app, product, or competitor. FlawPulse reads thousands of real reviews and hands you the loudest pain, a revenue range, and an opportunity score β with a real quote behind every point. In about 60 seconds.
Validate before you build. First report $7.
Replies
Hey Hunters π
Honest story: I'm a software engineer, shipped 30+ apps for clients, and wanted to finally build something of my own that makes money.
Problem was β I didn't have a problem to solve. So I flipped it: instead of waiting for an idea, why not find problems people already pay for and still complain about?
That's FlawPulse.
It scrapes real reviews across App Store, Google Play, G2, and Capterra, then uses AI to cluster them into actual pain signals β what people hate, which products, and where the gap is. Search a category, get a report in under a minute.
Basically the tool I wished I had: find validated gaps before wasting weeks building something nobody wants.
Your first reportβs on us. Get 5 credits on signup for free.
Would love your honest feedback π
I share all my work here β harikeshsingh.com
β Harikesh
Hey @FlawPulse, very useful product and clear UI. I really liked how I was able to read through hundreds of review about a product from multiple sources in 1-2 mins.
I would like to learn how you calculate your score and weightage thanks.
@ajay_sharma83Β Great question Ajay.
The score isn't one vibes-based number β it's a few weighted signals:
- Pain intensity (~35%) β how sharp complaints are, and how often the same one repeats.
- Willingness-to-pay (~30%) β is this a problem people already pay to solve?
- Frequency/reach (~20%) β does it show up consistently across sources, or is it one outlier?
- Evidence quality (~15%) β the honesty layer: if we don't have real data, we mark it unknown instead of faking a zero. Low-evidence searches get flagged, not scored.
So a high score = sharp pain + people already paying + shows up repeatedly + we actually have the receipts.
Also planning to integrate further more platforms to make it more valuable and accurate.
Thanks for the kind words on the UI π
@harikesh07Β Thanks, looks great.
the reviews you're scraping are written by people who cared enough to complain in public, which is already a filtered group. the bigger chunk of churn is usually silent - people who just quietly stopped paying and never left a G2 review. does the opportunity score account for that gap somehow, or is it inherently biased toward problems vocal users complain about vs problems that just make people leave
@galdayan atm, we're scraping reviews from platforms where users actively leave feedback, analyzing them, and generating reports based on that data. So I'm not sure if it's possible to identify problems from users who have never shared or complained about their experience.
That said, I'm actively working on improving it by integrating more data sources so the insights become more accurate and precise over time.
@harikesh07Β fair, that's an honest limitation to admit rather than paper over. one idea - churn timing data (if a company can pull cancellation dates) could be cross-referenced with review dates to see which complaints correlate with actual cancellations vs just venting. that'd at least weight the opportunity score toward stuff that predicts real revenue loss
@galdayan That's a really good idea. Correlating reviews with actual churn would make the score much more predictive of revenue impact. Right now we're limited to public data, but it's definitely on the roadmap once we can plug into first-party customer data. Thanks for the thoughtful feedback!
@galdayan Thanks for checking it out! Have you had a chance to try the product yet? I'd genuinely love to hear your feedback whenever you get a chanceβit would mean a lot and really help me improve it.
Zintlr
Sounds Intresting
@challa_navaneeswar_reddyΒ Yes it is, Pls give it a try. Would love your valuable feedback.
Finally got a chance to run a quick test on a niche tool I know well, and the "loudest pain" ranking felt scarily accurate, way more useful than skimming reviews myself. The cited quotes under each point sealed it for me, that part alone is worth the few bucks.
@veyselmentodzj Thank you, it really means a lot.
The cited quotes were the one thing I refused to ship withoutβa pain score without proof is just a guess. I'd love to hear your feedback on it.