Audit Engines
Every Apptonomy audit runs a set of specialized engines in parallel. Each engine analyzes a different dimension of your app store listing and produces scored findings with prioritized recommendations. The results are synthesized into a single unified report with an ASO Readiness Score (0-100).
Engines
Section titled “Engines”| Engine | What It Analyzes |
|---|---|
| Keyword Engine | Keyword gaps and opportunities across your app and 10 competitors |
| Search Term Engine | Predicted natural-language queries real users type when looking for an app like yours |
| Screenshot Engine | Screenshot effectiveness across clarity, coverage, and consistency |
| Icon Engine | Icon quality across visual clarity, brand clarity, color psychology, and consistency |
| Sentiment Engine | User review sentiment, topics, feature-level mapping, and trends |
| Store Text Engine | Title, subtitle, and description optimization with semantic keyword clustering |
| Competitor Discovery Engine | Automated identification of your 10 most relevant competitors |
| Policy Checker | Store policy compliance including prohibited terms, misleading claims, and trademarks |
| AI Discovery Engine | App visibility across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews |
| Intent Engine | User intent coverage gaps and competitive intent mapping |
| Localization Analysis Engine | Store listing localization coverage across 22 commercially significant languages |
How Engines Work Together
Section titled “How Engines Work Together”The Audit Engine orchestrator spins up all engines in parallel and synthesizes their findings. Several engines feed data to others:
- The Competitor Discovery Engine identifies 10 competitors used by the Keyword Engine, Screenshot Engine, Icon Engine, and Sentiment Engine for benchmarking.
- The Keyword Engine provides keyword data used by the Store Text Engine for semantic clustering and per-field analysis.
- The Sentiment Engine extracts user intents from reviews that feed into the Intent Engine.
- The Intent Engine provides intent data used by the AI Discovery Engine for prompt generation and coverage mapping.
Each engine produces an individual score (0-100) that contributes to the overall ASO Readiness Score.
From engine scores to the readiness score
Section titled “From engine scores to the readiness score”The ASO Readiness Score (0-100) is a weighted average of the scored engines’ individual scores. Each engine carries its own weight — the discoverability, conversion, and trust drivers carry the most, with the rest weighted below them. When an engine can’t produce a score for a given app (for example, a brand-new listing with too few reviews for the Sentiment Engine), its weight is redistributed proportionally across the engines that did score, so the overall stays on a 0-100 scale rather than being penalized for missing data.
Alongside the overall score, engine results are grouped into four pillars for reading — a presentation layer that tells you where your ASO stands, not a separate calculation:
- Discoverability — keyword-driven and search-driven visibility.
- Conversion — turning store visits into installs (visuals and copy that close).
- Trust — review sentiment and signal quality.
- AI Discoverability — presence in LLM-powered search and recommendations.
The pillars are derived from the engine scores for display; the overall readiness score is computed directly from the engine weights, not by averaging the pillars. Localization is a scored engine that feeds the overall score directly rather than a fifth pillar.
For the full breakdown — exact engine weights, how each pillar is composed, and how to interpret a score — see Understanding your report.