Ksenai helps companies understand how AI systems describe their company, whether they appear for relevant client questions, and what needs to change to become clearer, more visible, and more trusted in AI search.
Many companies are trying to create new lead-generation channels because traditional Google search, referrals, or paid traffic no longer produce enough qualified demand. AI search becomes part of this new discovery layer — but only if the company is visible, clear, and trusted in AI-generated answers.
Organic traffic may still exist, but it does not create the same volume or quality of leads. Companies need to understand whether AI search can become an additional discovery channel.
When clients ask AI systems for recommendations, competitors may appear while your company is absent, unclear, or described too weakly.
The website may contain useful information, but services, expertise, proof points, and category positioning are not structured clearly enough for AI systems to interpret.
Without a repeatable AI visibility audit, it is difficult to know which questions matter, where the company appears, which competitors dominate, and what should be fixed first.
AI search visibility is not only about ranking pages. It is about helping AI systems correctly understand what your company does, when it should be mentioned, and why it can be trusted.
We test real client questions and examine whether your company appears, how it is described, and which competitors are recommended instead.
We identify where your website, FAQ, service pages, brand positioning, and proof signals are unclear for AI systems.
You receive a practical roadmap for stronger visibility, clearer answers, and better digital knowledge structure.
After the audit, Ksenai can help brief, guide, review, or support selected implementation work if needed.
A concise summary of the current AI visibility situation, key risks, and most important opportunities.
A structured question set grouped by client intent: brand, non-brand, comparison, informational, and decision-making scenarios.
Results from tested questions, including visibility, omissions, competitor mentions, source patterns, and answer clarity.
Assessment of positioning, category clarity, answer-ready structure, proof points, FAQ opportunities, and semantic clarity.
Business-level recommendations for headings, internal links, schema opportunities, canonical clarity, and AI-accessibility signals.
Priority Q&A themes, answer-ready blocks, proof assets, and recommended on-site / off-site actions.
The audit is the starting point. After it, you choose one of three practical paths based on what the findings show, your team, resources, and priorities.
Use the audit roadmap as a clear brief for your internal team, website specialist, developer, marketer, or content owner.
Ksenai can help clarify priorities, prepare tasks, brief a developer, improve selected blocks, or support selected implementation work.
After selected changes are live, Ksenai can review implementation and run repeat measurement using representative questions to compare visibility, answer quality, and competitor mentions before and after the improvements.
Ksenai is designed for companies that need more than a generic SEO report: a focused diagnosis of how AI systems understand the company, explain the offer, compare competitors, and evaluate trust signals for the questions your clients actually ask.
The work connects AI visibility findings to the real task: more qualified leads, clearer product explanation, stronger trust signals, or better positioning against competitors in AI-generated answers.
Ksenai checks whether your expertise, proof, use cases, differentiators, positioning, and decision logic are clear enough for AI systems to understand and reference.
The question set reflects your category, audience, geography, competitors, services, and decision-making scenarios — not a generic prompt checklist.
The roadmap can guide website, content, FAQ, proof, schema, and developer work. Ksenai can also support handoff, implementation review, and repeat measurement.
The first step is a focused diagnostic and roadmap engagement. Post-audit implementation support is optional and scoped separately.
Approx. 2 weeks after client inputs and question-set approval.
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The audit is a focused first step. It gives your team a clear baseline, a practical roadmap, and a decision point for what should happen next.
The audit checks how AI systems describe your company, whether your brand appears for relevant client questions, which competitors or alternatives are mentioned, and what website, content, FAQ, proof, schema, or external visibility improvements should come first.
The typical audit takes approximately 2 weeks after client inputs and the question set are approved. Timing may change if the scope, number of questions, or required review depth changes.
You receive a concise findings snapshot, tested question set, AI visibility baseline, competitor and source observations, website readiness review, technical and schema recommendations, FAQ or answer-block opportunities, and a prioritized action roadmap.
Yes. If you already have a strong website, content, marketing, or development team, the audit can become their implementation brief. The recommendations are written to help your team understand what should be changed and why.
Yes. Post-audit support can be scoped separately based on the findings. This may include clarifying tasks, briefing a developer, reviewing implementation, improving selected content blocks, preparing FAQ sections, or setting up repeat measurement.
Repeat measurement is not included in the base audit fee. It can be added after selected improvements are live, using representative questions to compare visibility, answer quality, and competitor mentions before and after implementation.
No. The audit does not guarantee that AI systems will include or recommend your company. It identifies what makes your company easier to understand, evaluate, reference, and connect to relevant client questions.
Audit price: EUR 900.
Implementation support, repeat measurement, and additional delivery work are scoped separately after the audit.
These changes help AI systems interpret the organisation’s expertise, connect related ideas, understand service relevance, and reference the company more confidently in generated answers.
The first step is a focused AI visibility review: we test real client questions, identify how AI systems describe your company, and turn the findings into a practical improvement roadmap.