APP DISCOVERY
Predicts the natural-language queries users actually type, classifies the underlying intents as functional, emotional, or social, and scores how well your listing covers them against competitors.
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Most keyword tools do one or the other. This engine models the messy natural-language queries users type when they first feel the need your app solves, then maps the underlying intents behind those queries, so search behavior and motivation are read together.
Every extracted intent is classified across three dimensions: the functional job, the emotional payoff, and the social context. That classification surfaces high-relevance intents your listing misses, including ones your competitors articulate and you do not.
Intents are pulled from your listing, your reviews, and your competitors' listings, then deduplicated into canonical intents and shown side by side, so you can see exactly which intents you own and which you concede.
"Users do not search for features. They search for the need they feel."
The engine models the actual natural-language queries users would type to find an app like yours. It covers both action framings and problem framings, and it is platform-aware, so the predicted battery reflects how people search on the App Store and Google Play rather than a generic keyword list.
Intents are pulled from three places, then deduplicated into canonical intents and classified across three dimensions:
Coverage of the predicted queries and extracted intents is scored across four axes: breadth, depth, gap impact, and competitive differentiation. Source-quality weighting governs how much each source contributes.
Findings convert to recommendation rows, each one naming the intent it addresses, the sources that surfaced it, and the projected coverage-score delta if you adopt it.
Data sources
Live listing text; review pool (via User Sentiment); top competitor listings; Apple Search Ads and Google Play search context where available; versioned intent taxonomy.
AI's role
AI generates the predicted-query battery, classifies and deduplicates the intents, and scores articulation strength against the listing. The taxonomy of functional, emotional, and social intents, the four-axis coverage model, and the source-quality weighting are human-authored and kept current.
This is the shape of a recommendation row in the Weekly Revision Plan. The reasoning, the sources, and the projected impact are part of the row — not buried in a tooltip.
Add a 'for couples' hook to your subtitle or first description line.
Analysis is on every plan; draft generation and Publishing are on Pro, Elite, and Enterprise.
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