LLMic is a native macOS AI SEO auditor built for the new kind of search where AI systems read pages, extract facts, and answer questions directly. It helps ensure content is easy to extract and less risky to cite by crawling sites locally and measuring critical trust signals.

Key features include auditing Token Economy to remove template bloat so AI reaches answers faster, assessing Hallucination Risk to catch weak claims and missing proof before models guess, checking Schema coverage for structured-data gaps, improving Speakability to make content easier to quote, evaluating Retrieval Readiness for headings and extraction-friendly layout, and enabling Competitor Compare to see why others get cited.

The tool works by scanning a site or URL list, extracting content and computing trust signals, prioritizing issues by impact, and exporting fixes for improvements. It operates through a simple scan, review, fix, re-scan workflow that is fast and private.

Benefits include making sites more AI-citable, improving AI visibility step by step, and enabling audits without sending content to cloud dashboards. Use cases involve optimizing websites for better AI summarization and quoting, and identifying areas for content improvement to reduce hallucination risks.

Target users are website owners and SEO professionals focused on AI search optimization, using a native macOS application that works offline and prioritizes privacy.