Reduce painful ASO workflows with an AI-driven Mac-native keyword assistant

Executive Summary

App store optimization workflows remain painful: roughly 8 million active app publishers stitch keyword research together from spreadsheets, ad-hoc rank trackers, and manual localization notes, and many teams lack the time or process to iterate rapidly. This hits indie and SMB developers, in-house product marketers at mid-market studios, and small ASO agencies that collectively spend about $525 per year on ASO tools and need lower-friction, repeatable processes rather than enterprise suites. The practical result is slow hypothesis cycles, poor cross-locale coordination, and prioritization decisions driven more by intuition than measured impact. You could build an AI-driven, Mac-native keyword assistant that converts store data and analytics into prioritized hypotheses, draft metadata (titles, subtitles, long descriptions), and locale-aware experiments, all inside an opinionated, keyboard-first app that integrates with App Store Connect, Google Play, and common analytics sources. A hybrid LLM architecture—cloud-hosted for heavier inference with optional on-device models for privacy-sensitive customers—would support iterative prompts, automated A/B test setup, and one-click exports back to store consoles or CSVs, reducing the hours per release currently spent on manual keyword triage. The timing is favorable: the ASO market is roughly a $4.2B opportunity driven by LLM-enabled workflows that let tools move beyond static tables, continued indie/SMB growth, and rising returns from cross-locale optimization. To stand out, focus on Mac-first UX and deep Apple ecosystem integrations, practical pricing for SMBs (for example <$20/month or usage-based tiers), and a product that measures uplift per release; be realistic about challenges though—data integrations, model tuning, and a medium-competition landscape will require disciplined engineering and a tight go-to-market plan.