Find out whether the AI answers
ever mention you
Your buyers ask ChatGPT, Gemini, Claude and Perplexity before they open Google. Montu puts the questions they actually ask to all four engines, counts how often you are named, which competitors are named beside you and how you are described — then repairs the site so an AI agent can read it, understand it and buy from it.
What AI visibility tracking gives you
Rank tracking tells you where you sit on a results page. It says nothing about the answer a model gives when nobody sees a results page at all. This measures that second surface, and unlike a monitoring tool it does not stop at the number — it writes the files that change it.
The four engines, asked directly
ChatGPT, Gemini, Claude and Perplexity are queried through their own APIs, not scraped or resold through a middleman, so the answers are the ones a buyer would actually be shown.
Citation share and sentiment
You get how often you are named, how often your domain is cited as a source, the brands named alongside you, and whether the mention is positive, neutral or negative.
Agent readiness, graded
Structured data, semantic HTML, agent discovery and transactable product fields are each graded, because those four decide whether an agent can describe your product and complete a purchase.
It writes the fix, not just the finding
Montu generates llms.txt and agents.md for you and patches the structured data and product fields — price, availability, currency, SKU — instead of handing you a list of things to go and do.
How AI visibility works
The buyer prompts are written
Montu derives the questions your customers really ask from your site and market rather than asking you to invent a prompt list, which is where most of these tools lose accuracy.
All four engines are asked
Each prompt goes to every engine we have a key for. An engine that is unavailable is simply not asked and the report says which ones it reached — no silent substitutions.
Mentions are counted and read
Answers are parsed for your brand, your domain as a cited source, the competitors named beside you, and the sentiment attached to each mention.
The site is repaired
Agent Readiness grades what a model can do with your pages, then Montu writes the discovery files and patches the schema so the next sweep has more to find.
Who this suits
Brands whose buyers research with an assistant before they ever search
Stores that want an AI agent to be able to read price, stock and currency correctly
Anyone watching organic clicks fall while their rankings have not moved
Marketers asked by a board whether the company appears in ChatGPT
Agencies who need evidence of AI presence for a client report
Businesses in a category where two or three names get recommended and the rest do not
AI visibility questions
Why does this matter if I already rank on Google?
Because a growing share of buyers get an answer instead of a results page, and that answer names two or three companies. Ranking fifth on a page nobody opens is not the same as being one of the names.
Are the engines really asked, or is this an estimate?
They are asked directly through their own APIs, and the run reports which engines it reached. Nothing is modelled, extrapolated or bought back from a reseller's index.
What is llms.txt and do I need it?
It is a plain file that tells a model what your site is and which pages matter, alongside agents.md for agent behaviour. Montu writes both for you rather than telling you they are missing.
Can I actually influence what the models say?
Partly, and the levers are unglamorous: clear structured data, semantic HTML, product fields an agent can act on, and content that answers the question directly. Those are what this measures and repairs.
Is this an expensive add-on?
Elsewhere it is sold separately at around ninety-five dollars a month. In Montu it comes out of the same token balance as everything else, on every plan.
Related tools
Other Montu AI tools that pair well with this one.
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