Automatically summarizes public App Store metadata such as categories, rating volume, star ratings, release dates, and pricing for competitor research and product planning. The DeepSeek section is generated on demand when the API is configured; otherwise only rule-based analysis is shown.
Runs on demand so the initial page load stays fast.
Review the rule-based cards first, then generate the AI readout. It usually takes 10-40 seconds.
Category and portfolio mix
Education is the main category at about 50% of apps, while the portfolio spans 2 categories. This is a moderately concentrated portfolio strategy.
Scale and monetization structure
The public rating sample is thin, so reputation analysis should be treated as directional.
Weighted average and head effects
Most apps do not have enough rating data, so aggregate star ratings should be treated as directional.
Low ratings and thin samples (API has no review text)
The iTunes Search API does not provide review text. This card uses low star ratings with enough review volume as a proxy, and separately highlights apps with thin samples.
Listing age span
The earliest listed app is "Banz & Bowinkel AR", while the newest or most recent listing signal is "Banz & Bowinkel App". The span is a proxy for long-term iteration capacity.
Reusable research angles
These takeaways are based on public metadata and are useful for competitor research or portfolio planning. Deeper conclusions require downloads, revenue, and review text.
Rule-based cards use iTunes Search API data. DeepSeek generates its readout from the same public data and should be treated as directional research.
Runs on demand and avoids external calls by default.
Aggregates news about the team and core products, up to 20 items.
