Buyers no longer start on your product page, they ask an LLM. Klariton measures how ChatGPT, Perplexity, Gemini and Claude answer early buyer-intent questions in your category, shows where competitors win and your brand is missing, and turns each gap into concrete content, BIQs and product-data actions.
Buyers begin with a problem, not a product name. The LLM answers the early question and builds the relevant set before anyone visits your shop. If you're not in that answer, you're out of the decision before it starts.
Every metric is a count of citations, answers and sources, per engine and per buyer phase. No blended index. Figures below are illustrative demo data; in the product they show your real numbers.
LLM discovery shapes the relevant set most where the decision is considered. Klariton is built for categories where buyers research a problem before they know the product, clear targeting, not a claim of fit for everything.
Honest scope: if buyers don't research, there's little discovery to win, and we'll say so.
This is the core of Klariton: a closed loop from LLM scan to attributed impact. Every output is created or recommended, reviewed, and approved by you, never published on its own.
Queries and answers captured per engine and intent phase.
→Brand missing or a competitor cited, gap classified by type.
→The matching knowledge action is created or recommended.
→Source-backed and checked for claim drift before anything ships.
→The approved answer appears in the advisor, on product and in the magazine.
→Clicks and order signals are traced back along the chain.
Early questions want orientation; late ones want a decision. Klariton routes each gap to the surface that fits its intent phase, and links them into a cluster, so problem content reaches product without reading like an ad.
Source-backed buyer questions right on the product, for buyers close to choosing.
Compare · DecideGuides on problems and solutions, the answer the LLM looks for in the early phase.
Discover · ExploreConnected articles around one buyer problem, depth single pages can't deliver.
Problem-aware · Solution-awareEmbedded answer and recommendation blocks exactly where the purchase happens.
Buying contextVisibility in answers starts one step earlier, with the question of whether a model can capture the facts cleanly at all. Discovery Intelligence therefore measures not only whether your brand is named, but also what is machine-readable about you and who actually fetches it.
Klariton provides more than standard markup: a curated set built from scanned pages, product data and checked answer knowledge. It is served server-side as JSON-LD, retrievable per product. A model then reads the dimensions, height, width or power rating of an espresso machine as a fact instead of estimating them from prose.
The audit checks what a crawler really sees: whether content appears only after JavaScript, whether access rules and language markup are clean, whether the page is reachable. The result is a list of concrete steps, not just a score.
The reach beacon makes visible which AI crawlers fetch your content, which provider they belong to, and which pages and touchpoints they reach. That is measured retrieval, not an extrapolation from rankings.
"I am looking for a family car." The assistant asks back about budget, number of people and usage, gets an answer, asks again. Only at the end of that chain does a recommendation appear. Measuring the first answer alone misses the moment where the choice is actually made.
Today Discovery Intelligence measures single questions across engines. Measuring multi-step conversations is being built: where in the chain a brand shows up, whether it stays in the conversation across the follow-up questions, and where it drops out. We say plainly that this part is not live yet.
Every question asked in a Klariton touchpoint is evidence of what is currently unclear in the market. That is why those questions do not end up in a log, they go into the loop.
Next to the single-question view there is an aggregated one, where recurring and closely related questions are grouped. Twenty variants of the same uncertainty turn into one topic you can work on.
A buyer asks differently than an engineer, and differently again than a managing director. Questions are therefore captured not only by topic but assigned to the persona they matter to. That shows which objections come up with whom.
A frequent question is promoted to a backed answer in one step, or discarded. You curate it, with source and approval. The circle closes: measured demand turns into checked knowledge that feeds back into answers.
If someone mentions personal details in a conversation, an email address, a postal address or a phone number, those are replaced through a pseudonymisation gateway before any call to an external model. They are not passed to the model provider in clear text. For the analysis in the backend, the question counts, not the person behind it.
Klariton does not act on its own in your shop. It measures, explains and suggests, you stay in control.
Klariton changes nothing in prices, stock or content without your approval. The scope is clearly defined and traceable, not a black box.
Every output is built from approved material and checked before it's published. Unsupported claims and claim drift are flagged before they go live.
Data is hosted in the EU and minimized and protected before model processing. The architecture is designed for the requirements of the AI Act, trust by default.
Klariton connects visibility to impact: from the first LLM read, through the closed gap, to the product click. A traceable measurement chain, we track impact, we don't promise it.
The LLM answers an early buyer question in your category.
Your brand is missing or a competitor is cited.
Klariton closes the gap with source-backed, LLM-readable content.
The answer appears in the chat advisor, on product and in the magazine.
Product click and order signal are mapped back to the chain.
One scan shows your LLM discovery gaps across all four engines. It turns into a concrete content and BIQ plan, controlled, source-backed, approval-gated.
Live over the same API that runs on your own site. No search index, no invented claims. Every sentence shows which document it comes from.
Klariton is an answer engine optimization platform. It complements your existing shop or website without replacing anything or migrating data. From your own knowledge it produces checked, source-based answers (BIQs) that AI assistants and your visitors use directly.