Public store listings
Apptonomy reads live listing data from the iOS and Google Play on every audit.
The methodology
We show our work.
Apptonomy is senior human ASO expertise encoded as software. Eleven specialized engines, cross-validated AI across multiple frontier models, and the reasoning a senior ASO consultant would give for every recommendation.
The ASO work a senior consultant would do, repeated on every app, every day.
Senior-grade ASO at the price of a tool.
§2
AI is the muscle. Expertise is the brain.
Each engine encodes the way senior ASO consultants think about a specific slice of the store listing: keyword opportunity, semantic clusters, screenshot messaging, icon legibility, sentiment drivers, competitor positioning, policy compliance, AI discoverability, intent coverage, search-term performance, and localization fit.
Recommendations are cross-validated across multiple frontier models so a single model's blind spot does not drive the score. The output is a ranked action plan with the signal that triggered each item and the projected lift, the way a senior consultant would deliver it.
§3. The loop, expanded.
Eleven engines under Insights. Publishing under Actions. ASC and Play ingestion plus score-trend under Analysis.
1Insights
Eleven specialized engines run in parallel on every audit.
Every audit runs the full set of specialized engines across keywords, store text, screenshots, icon, reviews, competitors, policy, AI discovery, intent, search terms, and localization. Cross-validated outputs feed a single ranked view of what to fix and why it matters.
11 engines run in parallel on every audit.
2Actions
Ranked, explainable recommendations with projected lift.
Insights converge into a prioritized action list. Each item names what to change, the signal that triggered it, the projected lift, and the reasoning a senior ASO consultant would give if they were reading the dashboard with you.
Publishing is a roadmap capability; recommendations ship today.
3Analysis
Schedule re-audits and watch whether the actions worked.
Apptonomy re-audits your listing on your cadence and tracks how your ASO Readiness Score and its pillars move after each revision. Audit history sits side by side so you can confirm whether the change you shipped actually moved the needle before you commit to the next round.
Pulls App Store Connect and Play Console signals when linked, otherwise relies on public listing data and the daily re-audit cadence.
The eleven engines under Insights.
Storetext Engine
See which intent themes you dominate and which you're handing to competitors.
Read the methodology →Screenshot Engine
Scores every screenshot on whether it actually drives installs.
Read the methodology →Competitor Discovery
Your real competitive set, filtered and ranked, not your assumptions.
Read the methodology →
§4. Worked example
How the Keyword Engine actually runs.
- 1Pull live store data and merge it with category benchmarks for your top ten competitors.
- 2Extract literal keywords from your metadata and infer additional candidates from app context using a frontier LLM.
- 3Score every candidate on volume, difficulty, relevance, and trend direction. Filter branded terms.
- 4Cross-validate the ranked list against a second frontier model, flag disagreements, and resolve them with the senior-ASO heuristic encoded for that case.
- 5Surface up to seventy prioritized keywords with reasoning that names the signal behind each recommendation.
The same shape repeats for every engine. Each one ships with its own published methodology so the reasoning is auditable, not vibes-based.
§5. Data sources
Where the signal comes from.
Keyword and search-volume data
Multiple data providers feed search volume, difficulty, and trend signals into the Keyword Engine and Search Term Engine.
AI discoverability surfaces
Direct queries against ChatGPT, Claude, Gemini, and Perplexity with category-relevant prompts. Recommendation share-of-voice is measured, not estimated.
App Store Connect and Play Console
When linked, pulls search-term performance, source attribution, and console analytics so recommendations reflect the queries actually sending traffic.
§6. What kind of AI
Frontier models, expert prompts, structured outputs.
- Cross-validated frontier models. Every engine runs against multiple frontier LLMs and reconciles the answers, so a single model's blind spot does not become the score.
- Expert-crafted prompts. Over a hundred prompts authored from the way senior ASO consultants would actually phrase the question if they were sitting next to the data.
- Structured outputs. Each engine emits typed, schema-validated data, not free-form prose. That is what lets the dashboard cite the specific signal behind every recommendation.
§8
We don't ask you to trust us.
The methodology is published. The reasoning sits next to every recommendation. The score moves on your own data after the change you ship. Run the loop and let the evidence answer the question.
Defensible ASO. Verifiable on your own data.
Apptonomy is senior human ASO expertise encoded as software. Read the methodology, run the loop, and let the score on your own listings tell you whether it works.
Per-engine methodology, in the open.
Each of the eleven engines has a written methodology you can read before you trust the score. The reasoning that drives every recommendation traces back to a specific engine signal.
Plain-language reasoning, not opaque scores.
Every action names what to change, the signal that triggered it, and the projected lift on the ASO Readiness Score. The kind of reasoning a senior ASO consultant would give if you asked them why.
Recommendation Impact, on your own data.
Schedule a re-audit and watch your ASO Readiness Score and pillar scores move after the change you shipped. Audit history sits side by side so the lift is verifiable on the apps you actually run.
DIY frontier LLMs vs Apptonomy.
A clever prompt against a frontier model is not the same as the loop. Coverage, cross-validation, ranked actions, and continuous re-auditing are why the loop reads where DIY breaks down.
See it on your app.
Paste any App Store or Google Play URL to start a free audit. Curious how this stacks up against a clever prompt against a frontier LLM? Read the DIY LLMs vs Apptonomy comparison.
Get a free quick audit for your app.