Beaconn

Beaconn’s core workflow is a scan: you submit your brand and target prompts, and the tool queries Perplexity, ChatGPT, Gemini, and Claude live — not a cached corpus — then scores your visibility from zero to ten per engine, maps competitor share of voice as a percentage of AI mentions, and logs each answer with engine, date, exact wording, and cited sources as auditable evidence. The Google-vs-AI gap view overlays your traditional search rankings against your AI-answer presence, making it possible to isolate prompts where you have SEO traction but zero AI citation — the category of loss that conventional rank trackers never surface.

The most concrete differentiating feature is what the vendor calls the AI traffic classifier: a single snippet that tags incoming visits by the AI assistant that referred them, using referrer signals and utm_source, without recording non-AI visits or setting cookies. For teams whose analytics currently lumps AI referrals into ‘direct’ or ‘other,’ this closes a measurement gap that makes it impossible to attribute pipeline to AI-answer presence.

Beaconn fits marketing and growth teams in the research and diagnosis phase — teams who need to make the case that an AI-search gap exists before committing engineering or content resources to closing it. The free scan covers the visibility score and competitor map. The content fix layer — where the tool drafts the exact comparison pages and FAQ schema that AI engines cite for a given query — is a paid-only feature, so the free tier delivers the problem statement but not the solution. Teams that need the fix layer, ongoing scheduled monitoring across dozens of prompts, or programmatic access to scan results will find no API available and no self-hosted option, and the vendor’s current scale (57 brands scanned at time of writing) signals an early-stage platform rather than an enterprise-grade monitoring system.