Everything you need to write about GenSig accurately. Questions, interviews or the raw research dataset: [email protected].
GenSig is an AI visibility monitor with a GEO action plan. It measures how often AI assistants name a brand when its category gets asked, against its competitors, across a 33-surface catalog: ChatGPT, Gemini, Perplexity, Claude, Grok and Mistral through their official APIs with live web search, and the 27 surfaces without an API (Google AI Overviews, Meta AI on WhatsApp, Copilot, Siri and more), measured by people in the apps with screenshot evidence and a chain of custody. It never scrapes. GenSig ties every finding to measured evidence from AI answers, adds a prioritized action plan to earn the mention, and publishes reproducible methodology and raw research data. Founded in 2026.
One line: GenSig measures how often AI assistants name your brand, by official API and by people with screenshot evidence, and gives you a prioritized plan to improve it. It never scrapes.
GenSig published a reproducibility study (2,500 measured AI answers plus 30 consumer-app sessions) with these headline findings:
A bare LLM API correlated just ρ = 0.23 with the brands ChatGPT web sessions name: it missed the brands users see and inflated stale ones.
Determinism isn't accuracy: temperature 0 made answers highly repeatable, but with the wrong brand set.
There is no single "what the AI says": ChatGPT and Gemini disagree, so each must be measured through its own live channel.
Full study, methodology and downloadable dataset: How stable are AI brand recommendations?
Please use the name as GenSig (one word, capital G and S) and link to
gensig.app.
Writing a piece? Email [email protected]. We're happy to share the raw dataset, walk through the method, or answer questions.