AI keyword suggestions (ASO)

• Generates many candidate phrases quickly, including synonyms and long-tail variants.
• Drafts localized versions of a list as a starting point.
• Rewrites text to fit a limit, such as a 30-character subtitle.

A model does not know how many people search a term in a specific storefront, who ranks for it or how strong the top 10 is. Those answers need ranking data. LocalASO's volume and difficulty are 0 to 100 estimates derived from rating counts of ranking apps, not Apple search counts, but they are real observations of the store, which a model's guess is not.

1. Is it relevant to what the app does? Irrelevant terms are prohibited.
2. Does it include a competitor or trademarked name? Remove it.
3. Does it have search volume in the target country?
4. Is keyword difficulty acceptable?
5. Does it fit the byte budget with the rest of the field?

A hypothetical habit app asks a model for 50 ideas. The model proposes "mindful morning planner". Checking shows low volume and a weak top 10, so it becomes a long-tail keyword candidate. Another idea contains a well-known rival's name, so it is dropped.

LocalASO uses the AI you bring (Claude Code, an Anthropic API key, Ollama, Codex or Gemini CLI) and attaches your own ranking data to its suggestions. The upload to App Store Connect is a draft, and it never submits an app for review. See the AI provider docs.

Connect your own tools through the Model Context Protocol or follow the localization with AI workflow.

Asking an AI to read your board is possible via the Model Context Protocol.