Live transcripts from ChatGPT, Claude, Gemini and Perplexity when someone asks for your category — plus the deterministic Agentic Score to measure and fix the gap.
“What tools can help me track my AI referral traffic and share of voice?”
that's one question. your scan runs five, across all four models. how we ask
llms.txt, agent manifests, AI crawler access, structured data. Can agents find you?
Parseable pricing, self-serve signup, machine-readable offers. Can agents buy?
SSR content, meta completeness, terms & privacy. Will agents recommend you?
Twelve deterministic checks weighted by how much they change whether ChatGPT, Claude, Perplexity, or Gemini will surface and recommend your SaaS. Each check has a public methodology, an authoritative source, and a pass / partial / fail rule you can reproduce in a browser. Categories: Discovery (llms.txt, structured data, agent manifests), Crawler Access (robots.txt for AI bots, sitemap), Commerce (parseable pricing, self-serve signup), Metadata (title, description, machine-readable company info), Content Accessibility (server-rendered HTML), and Trust (Terms, Privacy, Security).
Founders and buyers now start category research inside chatbots. If your homepage is a JavaScript shell, if your pricing is behind “book a demo”, if your structured data is missing, or if you block AI crawlers, the assistants literally cannot see you and cannot cite you. The result: a competitor with a worse product but a machine-legible website wins the recommendation. AIScoring makes the gap measurable and gives you a prioritized fix list you can ship in an afternoon.
AIScoring sniffs through ChatGPT, Claude, Perplexity and Gemini so you know exactly how AI sees your startup — before your buyers ask.

Lola · sniffing four machine minds