When the market asks an assistant about your category: who gets named, and are you in the answer?
Klariton measures your share of answer in ChatGPT, Perplexity and others: which questions you show up for, which competitors get named, and what is missing for the answer to point at you. Every gap turns into a concrete task for content, data and approved answers.
- You do not know which purchase-relevant questions get your brand named, and where a competitor owns the answer instead.
- Classic SEO rankings no longer tell you how an assistant summarises your category and who it ends up recommending.
- Answers about you rest on scattered, partly outdated or wrong information, without you noticing or being able to correct it.
- Approvals for claims, statements and wording live in people’s heads and in email, not in a rulebook that also governs AI answers.
- Traffic from AI assistants shows up as direct traffic in analytics, with no link to revenue or to the recommendation behind it.
Why a general-purpose chat assistant is not enough here
A chat assistant on your own site answers people who are already there. Your visibility is decided earlier, in answers that form elsewhere and that nobody reports back to you. That is where Klariton starts: measure what is said about you without you, turn the gap into a task, and publish the answer only after approval. Nothing goes live on its own.
The relevant question changes with the decision phase.
Which approaches can help us reduce recurring presales questions?
A reliable answer is generated from the company’s approved sources. This example intentionally does not use invented answers or sources.
Test with your own sources →Questions that decide your visibility
- Which purchase-relevant questions in our category get us named, and which ones only name competitors?
- How does an assistant describe our brand right now, and does that description match our positioning?
- Which competitors appear next to us or instead of us, and with which argument?
- Which content, evidence and product data are missing so that the answer points at us?
- Do AI crawlers read our pages at all, or do they fail on JavaScript and missing markup?
Which Klariton modules matter here
Shows question by question whether you appear in the answers in your category, how share of answer moves over time, and which competitors get named.
Uses the reach beacon to prove which AI crawlers actually read your pages, so visibility is measured against real bot reads instead of guessed.
Holds claims, permitted statements and approvals as a rulebook, so published answers stay on brand and legally sound.
Connects visits and revenue from AI assistants back to the recommendation, so visibility is not only measured but shown to pay.
What improves
- You know your share of answer per question cluster and see where you lose ground to competitors, instead of guessing.
- Knowledge gaps become prioritised tasks for content and product data, so answers to purchase-relevant questions point at you.
- Published answers stay on brand through rulebook and approvals, even when many questions are served automatically.
- AI-driven traffic is tied to revenue, so visibility becomes visible as a contribution to the result.
Fits into your existing stack.
- Contentful AppApp ready · Marketplace review pending
- Public Touchpoint DeliveryLive
- Schema Export APILive
- Reach Beacon SDK (@klariton/beacon)Live
- Next.js Middleware HelperLive
- Schema HelperLive
- Conversion & event ingestionLive
- Website / URLLive