Validate your app idea against real market data.
Before you spend a month building, find out whether anyone actually wants it. Our engine weighs four signals against live data from the App Store, Reddit, GitHub, and Stack Exchange, and returns an honest score with the evidence cited. Here is exactly how it works, and a real example.
What the engine weighs.
Not a vibe check. Four market signals, each anchored to data collected the moment you run a validation, plus a feasibility grade on top.
Competition
Who already owns this space, and where are they weak?
We search the App Store for every app near your idea, count them, read their ratings and review volume, and pull the top competitors and their prices. A blue ocean scores high, a market with five 10,000-review incumbents scores low. The point is not just how crowded it is, but where the leaders are thin.
Reads: App Store search, competitor ratings and pricing
Demand
Are real people actually asking for this?
This is the heaviest signal. We mine Reddit discussions, GitHub issues that say "wish there was", unanswered high-view Stack Exchange questions, and one and two-star reviews of the leading apps. Unmet-need evidence, in people’s own words, is what separates a real problem from a nice idea.
Reads: Reddit, GitHub, Stack Exchange, App Store review mining
Timing
Why now, and not two years ago or two years from now?
We weigh the macro trend behind the idea: is the underlying behaviour, platform, or technology rising, stable, or fading? Good ideas built too early or too late fail for reasons that have nothing to do with the idea itself.
Reads: Trend direction across the collected signals
Monetization
Will anyone pay, and how much?
We look at what competitors charge and which models they use, then estimate a realistic revenue band and the model most likely to fit. If the whole market is free with no willingness to pay, that is a finding, not a footnote.
Reads: Competitor pricing, model analysis
Build difficulty
On top of the four market signals, every report grades how hard the idea is to actually build, from a weekend project to a team effort, with a rough timeline and a suggested stack. A great market you cannot build for is still a no.
What one report looks like.
This is an example of a real output from our engine, shown in full. It is an illustration, not a live result: your report is generated from fresh market data the moment you run one.
The idea
A meal-planning app that builds a grocery list from your dietary restrictions
Overall
61/100
Crowded. Several strong incumbents with big review counts, but most handle strict allergy filtering badly.
Strong. Recurring Reddit threads and one-star reviews complaining that existing apps ignore allergies and cross-contamination.
Stable. Steady interest, no sudden tailwind, no decline. A durable need rather than a trend.
Workable. Competitors charge $4 to $10 a month; a focused allergy niche can support a subscription.
The read
A real, durable pain that the big players serve poorly for people with strict dietary needs. The opportunity is the niche the incumbents treat as an afterthought. The risk is that the market is crowded, so the wedge has to be sharp: own allergy and cross-contamination handling before broadening.
The questions people actually ask.
- How does idea validation actually work here?
- When you run a validation, the engine pulls live market data from real sources: it searches the App Store for competitors and their pricing, mines Reddit, GitHub, and Stack Exchange for people describing the problem in their own words, and reads one and two-star reviews of the leading apps for unmet needs. It weighs four signals from that data, competition, demand, timing, and monetization, grades how hard the idea is to build, and returns an overall score with a specific, cited analysis rather than generic advice.
- Is the validation free?
- You need a free account to run one, because each validation makes a batch of live API calls and an AI analysis pass on real data, and that has a real cost. There is no charge to sign up and your first validations are free. We do not run a fake anonymous demo that returns a canned result, which is why this page shows you the method and a real example instead.
- What makes this different from asking an AI chatbot if my idea is good?
- A chatbot will guess from its training data and, more often than not, tell you your idea is great. This engine refuses to invent numbers. Every score is anchored to data collected at the moment you run it: actual competitor counts, actual review complaints, actual unanswered questions. When the evidence is thin, it says so and scores the idea lower, rather than flattering you.
- Will it just tell me every idea is a winner?
- No, and that is the point. The verdict scale runs from BUILD IT down through PROMISING and RISKY to SKIP, and plenty of ideas land in the bottom half. An honest low score is worth more than a flattering high one, because it saves you the months you would have spent building the wrong thing. Some strikes land common. That is what honest grading looks like.
- What sources does the demand signal use?
- Demand is the heaviest signal and it draws on the widest evidence: Reddit discussions across relevant communities, GitHub issues that match pain phrases like "wish there was", unanswered high-view Stack Exchange questions, and one and two-star reviews of the leading apps in your category. Unmet-need evidence in people’s own words is weighted heavily, because it is the hardest signal to fake and the most predictive of a real market.
Score your idea on live data.
Create a free account and run a validation on your own idea. Real competitors, real complaints, real score, generated the moment you ask. Better to learn it is a SKIP now than after a month of building.
Free account. Your first validations are free.