Main topic
Generative engine optimization
Understand how GEO connects SEO, brand authority and AI answer quality.
Klairia FAQ
AI visibility, GEO and LLM tracking questions answered.
A practical FAQ for teams learning how to monitor brand mentions in AI answers, compare competitors, interpret weekly scans and improve their visibility across generative search.
Core workflow
Weekly LLM scans
Track model answers repeatedly so visibility changes become measurable.
Business outcome
Competitor intelligence
See which brands AI assistants recommend before buyers reach your site.
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AI visibility and GEO basics
Core definitions for teams learning how ChatGPT, Claude, Gemini and other AI assistants affect discovery.
What is Klairia?
Klairia is an AI visibility monitoring platform for B2B teams. It scans how large language models mention your brand, competitors and category, then turns those answers into weekly visibility scores and operational gaps.
What is AI visibility?
AI visibility is the degree to which your brand appears, is described accurately and is recommended inside AI-generated answers. It covers whether the model names you, where you appear, what context surrounds the mention and which competitors appear instead.
What is GEO?
GEO stands for generative engine optimization. It is the practice of making your brand, product and expertise easier for AI answer engines to understand, cite and recommend in generated responses.
How is GEO different from SEO?
SEO usually optimizes pages for search result rankings, snippets and organic clicks. GEO optimizes the source signals that AI assistants use when they generate direct answers, including brand clarity, category association, third-party mentions, topical authority and factual consistency.
Does GEO replace SEO?
No. GEO builds on many SEO fundamentals: clear pages, crawlable content, structured information and trusted mentions. The difference is the outcome you measure. SEO asks whether searchers click your result; GEO asks whether AI systems understand and recommend you.
Why should a SaaS company track AI visibility?
Buyers increasingly ask AI tools for shortlists, comparisons and recommendations before they visit vendor websites. If your competitors appear in those answers and your brand does not, part of the buying journey is happening without you.
What questions can Klairia help answer?
Klairia helps answer questions such as: does ChatGPT mention us for our category, which competitors appear most often, what language does the model use to describe us, where do we first appear in a progressive prompt sequence and how does that change week over week.
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Scans, prompts and models
How Klairia collects repeatable model responses for brand, category and competitor analysis.
How does a Klairia scan work?
A scan runs a controlled set of prompts against selected AI models, stores the public responses, detects brand and competitor mentions, then calculates visibility metrics for comparison over time.
How often does Klairia scan?
Klairia is designed around weekly scans. A weekly cadence is frequent enough to catch category movement while keeping results comparable and easier to interpret.
Which AI models can Klairia track?
Klairia focuses on major assistant-style models used in buyer research, including ChatGPT, Claude, Gemini, Mistral and similar LLM surfaces as supported by the product configuration.
What is progressive prompting?
Progressive prompting means testing a topic from broad category questions through more specific buying, alternative and comparison prompts. It shows whether your brand is visible early, or only when the model is pushed toward a narrow answer.
Why not run only one prompt per topic?
One prompt can be noisy and incomplete. A progressive prompt stack gives a clearer view of how models connect your brand to category intent, alternatives, use cases and competitor shortlists.
Can I choose the topics Klairia scans?
Yes. Teams use topics to represent categories, pain points, alternatives and buying situations they care about. Higher plans can store more research directions while weekly scans stay focused on active topics.
Why does Klairia cap active weekly scan topics?
The cap keeps weekly reporting readable and comparable. Scanning a focused set of active topics makes it easier to see whether changes in content, positioning or market signals are improving visibility.
Can Klairia scan multiple languages?
Yes. Klairia supports multilingual AI visibility tracking so teams can compare how brand visibility changes across markets such as English, French and German.
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Scoring and interpretation
What the metrics mean and how teams should read movement inside an AI visibility report.
What is a visibility score?
A visibility score summarizes how often and how prominently your brand appears across the scan. It is meant to make weekly movement easy to compare, not to replace reading the underlying model answers.
What is first mention level?
First mention level shows how early your brand appears in a progressive prompt sequence. Appearing in broad category prompts is stronger than appearing only after a very specific prompt names your niche or attributes.
Does Klairia track mention position?
Yes. Mention position helps separate being named first from being buried in a later list. That matters because AI answers often compress a buyer's initial vendor shortlist.
Does Klairia measure sentiment?
Klairia can surface the context and tone around brand mentions so teams can see whether the model describes them accurately, neutrally, positively or with missing or outdated claims.
What does a low visibility score mean?
A low score usually means the model does not strongly associate your brand with the scanned topic, or competitors have stronger signals. The next step is to inspect the exact prompts, responses and competing names.
What does a high visibility score mean?
A high score means your brand appears reliably for the tested topic set. It should still be checked against answer quality, competitor context and whether the prompts match actual buying questions.
How should we use weekly changes?
Treat weekly changes as directional intelligence. Look for repeated movement across prompts, models and languages rather than overreacting to a single generated response.
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Competitors and category intelligence
How to use AI search data to understand who models recommend instead of you.
Can Klairia show which competitors appear in AI answers?
Yes. Klairia detects competing brands in model responses so you can see who appears for category prompts, alternative prompts and buyer-style questions.
Why do competitors appear when our brand does not?
Competitors may have clearer category positioning, more trusted third-party mentions, broader documentation, stronger comparison pages, more public reviews or better coverage in sources the model has learned from or can retrieve.
Can Klairia help with competitor comparison pages?
Klairia can reveal which competitors models already associate with your category. That evidence can guide comparison pages, alternatives pages, content refreshes and partner or review-site priorities.
Does Klairia tell us exactly why a model chose a competitor?
Klairia shows the response evidence and recurring patterns, but model recommendations are not perfectly explainable. The practical value is seeing which claims, sources and competitor names repeat often enough to act on.
Can we track emerging competitors?
Yes. Recurring scans can reveal unfamiliar brands that begin appearing in model answers before they become obvious in traditional SEO or sales conversations.
How do competitor mentions affect the score?
Competitor mentions provide context for your visibility. If competitors appear frequently, earlier or with stronger recommendation language, that is a signal to improve category association and source coverage.
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SEO, content and action plans
How teams turn Klairia findings into better source material for search engines and AI systems.
Can Klairia improve our SEO rankings directly?
Klairia is not a traditional rank tracker and does not directly change rankings. It gives evidence that helps prioritize content, positioning, source coverage and comparison work that can support both SEO and GEO.
What content should we create from a Klairia report?
Common actions include strengthening category pages, adding clearer use-case pages, publishing comparison content, fixing outdated product claims, improving documentation and earning third-party mentions where models already find competitors.
Should every answer become a blog post?
No. Some gaps need product-page copy, documentation, schema, review coverage or partner mentions rather than another blog article. The right action depends on the prompt intent and the sources competitors benefit from.
How does structured data help GEO?
Structured data helps crawlers and search systems understand page entities, relationships and page type. It is not a magic ranking lever, but it supports clarity when paired with visible, accurate content.
Why does factual consistency matter for AI visibility?
AI systems build answers from many signals. If your website, documentation, profiles, reviews and comparison pages describe the product differently, the model has a weaker and less stable understanding of your brand.
Can Klairia help us brief writers or agencies?
Yes. Reports give writers concrete prompts, missing claims, competitor names and language-specific gaps instead of vague instructions to write more AI content.
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Privacy, setup and operations
Practical details for teams evaluating how Klairia fits into their workflow.
What data does Klairia need to start?
A workspace starts with your website and the topics, models and languages you want to monitor. Klairia analyzes public model responses about your brand and category.
Does Klairia store customer data?
Klairia is built around public brand and category visibility. The landing site states that Klairia does not store your customer data, only public LLM responses about your brand.
How long does the first scan take?
The homepage promise is that the first scan starts in under five minutes after registration and setup. Exact completion time depends on the selected topics, models and languages.
Who should use Klairia?
Klairia is most useful for founders, marketers, SEO teams and revenue leaders at B2B SaaS companies where AI recommendations can influence pipeline, category perception or competitive shortlists.
Is Klairia built for agencies?
Current plans are designed around a single workspace per account. Multi-workspace agency support may require a different workflow than the current public plans.
Can I use Klairia from an LLM or agent?
Paid workspaces can use API and MCP access to connect Klairia workflows with agent-style tools, including creating keys, managing scan settings and working with visibility data from a preferred assistant.
How is billing handled?
Klairia uses LemonSqueezy for checkout and billing. After checkout, Klairia stores subscription status, the customer portal URL and plan limits when the billing webhook arrives.
Can I run a free scan?
Yes. Klairia's main call to action is to start a free scan without a credit card so teams can see initial AI visibility evidence before choosing a paid plan.
Ready to measure it
Run a first scan and see where your brand appears.
Use Klairia to turn AI visibility questions into weekly evidence: prompts, answers, competitors, languages and score movement.