macOS AI Autocomplete Assistant

macOS users waste time typing repetitive text and code. Create a system-wide AI autocomplete tool that works in any app (email, code editors, documents) and learns from the user's writing style. Target developers, writers, and power users who want faster typing.

Apple's native autocomplete improvements in macOS Sequoia and the rapid adoption of tools like GitHub Copilot and Raycast AI have raised baseline user expectations for in-context suggestions, making a system-wide layer feel timely rather than premature. The closest incumbent is Espanso combined with something like TextSoap or, more directly, the text expansion features baked into tools like Raycast — none of which offer true LLM-style personalized autocomplete across all apps. The $500–3k/mo revenue band is realistic only at the lower end: developers will pay $8–15/mo for this, but the addressable pool willing to install a system-level accessibility daemon from an indie dev is small, which caps growth quickly. The single most likely failure mode is the macOS permission model — users are increasingly hostile to apps requiring accessibility and input monitoring access, and one bad App Store review about "keylogger vibes" can kill conversion entirely.