AppSniper applies an underwriting framework, borrowed from insurance and investment diligence, to App Store opportunity evaluation. Every verdict is evidence-based, adversary-tested, and tracked over time.
Evidence is weighted by observability (can we directly measure it?), reliability (how often is it wrong?), and decay rate (how quickly does it become stale?).
Each underwriting run scores the opportunity across six weighted criteria. The weighted composite becomes the Opportunity Score, which drives the BUILD / WATCH / KILL verdict.
Can new entrants rank for high-intent keywords without extraordinary ASO investment? We score keyword difficulty, brand lock-in, and non-branded term volume.
Does the category support sustainable subscription or IAP revenue? We evaluate ARPU proxies, paywall patterns, and willingness-to-pay signals from review sentiment.
How entrenched are the top-3 players? We analyze review moat depth, brand term ownership, update cadence, and switching cost indicators.
How many credible competitors exist at each price point? We map the competitive density and identify structural gaps a new entrant could occupy.
Is category demand growing, stable, or declining? We assess keyword volume trends, install velocity curves, and seasonal patterns over 12–18 months.
Does the operator's current distribution, team expertise, and existing portfolio create an asymmetric advantage in this specific category?
The Adversary Review is a mandatory step where we build the strongest possible case against the opportunity. This is counter-diligence, not devil's advocacy.
Every Decision Memo includes an Adversary section that must be addressed before a BUILD verdict can be issued. A WATCH verdict means adversary risks are real but not disqualifying. A KILL verdict means adversary evidence is decisive.
AppSniper maintains a calibration log. When outcomes are known, we record the result against the original verdict: which BUILDs launched, which KILLs were skipped. This is the learning loop most teams never build.
What % of BUILD decisions led to successful launches? What % of KILL decisions were later validated by market events?
Which evidence tiers most reliably predicted outcomes? Which signals were noisy? The model is retrained on actuals.
When we issued BUILD and the operator passed on it: what did they find after launching anyway? These cases are the most valuable for calibration.
AppSniper is evidence-based. The verdict is a thesis rating, not a forecast or guarantee.
If you build native apps and want higher conviction before committing a sprint, AppSniper is built for that problem. Join the waitlist and we will reach out if you are a fit.