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·6 min·Mark Vivanco

AI for ASO: what actually works (and what is just LLM theater)

Every ASO tool launched a "AI Insights" button in 2024-2025. Most of them are theater. A few are doing real work. Here's how to tell which is which.

What real LLM-augmented ASO looks like

LLMs add genuine value to ASO in three places:

  1. Sentiment + topic extraction from reviews. Classifying thousands of reviews into positive/neutral/negative + extracting topics (crashes, value, ui) was previously a hand-coded NLP pipeline. LLMs do it better with zero training, at $0.001-per-review for batch processing. This is real work; the output meaningfully changes how teams triage.
  1. Subtitle and description rewrites with rationale. A good LLM (Claude Sonnet 4.6, GPT-4 class) can read your current copy + your competitors' copy + your top keywords, and write rewrites that integrate the keywords naturally. The output is suggestions a human evaluates — not an autopilot — but the time savings vs. blank-page writing are real.
  1. Narrative weekly summaries. "What changed across your apps this week" written as a paragraph, not a table. For multi-app teams, this transforms the Monday morning ritual from data-spelunking into a 3-minute scan. Cheap to generate (Claude Haiku, $0.001 per summary).

What LLM-augmented ASO theater looks like

Three patterns to be skeptical of:

  1. "AI-generated keyword suggestions" with no underlying data. If the tool isn't combining the LLM with actual rank data + competitor coverage + search volume, the output is generic SEO advice repackaged. You can get the same suggestions from ChatGPT for free.
  1. "AI score" gauges with no explanation. A 0-100 "ASO Score" with a green/yellow/red light tells you nothing actionable. The LLM was used to generate plausible-sounding numbers, not to do useful analysis. Look for tools that show the rationale — not just the score.
  1. Chat interfaces grafted onto existing dashboards. If the value proposition is "ask our AI anything about your app's rank," the tool is hiding its lack of original analysis behind a conversational UI. Real LLM utility for ASO is mostly one-shot generation (a recommendation, a rewrite, a summary), not chat.

How to evaluate an "AI-powered" ASO tool

Three questions to ask in a sales call or trial:

  1. Show me one keyword recommendation. Walk me through why the AI picked it. A good tool will reference your current rank, the keyword's volume, your competitors' coverage of the keyword, and the predicted rank delta. A theater tool will hand-wave.
  1. Show me what data the AI has access to. A good tool feeds the LLM your current metadata, recent rankings, and competitor metadata. A theater tool feeds it generic prompts.
  1. What's the cost per recommendation, and who pays? Real LLM use has marginal cost. Tools that expose this (or absorb it transparently into pricing) are doing real work. Tools that obscure it are usually doing fewer LLM calls than they claim.

What we built

Full disclosure: Rank Sonar's ASO Copilot uses Claude Sonnet 4.6 for keyword opportunities, subtitle/description rewrites, and screenshot critiques. Each recommendation includes:

  • The exact keyword/copy/critique
  • The rationale (what data drove the recommendation)
  • The predicted impact (rank delta, conversion lift estimate)
  • A "dismiss" button that improves future recommendations for your workspace

Sentiment classification on reviews uses Claude Haiku for cost ($0.001 per review). Weekly narratives use Haiku ($0.05 per app per week).

We surface the model used, the input tokens, and the output tokens for every AI-generated artifact. If we ever can't explain why a recommendation was made, that's a bug, not a feature.

Skeptical? Try the Copilot free for 14 days and dismiss every recommendation you don't agree with. [Start trial](/pricing).