LLM Visibility Checker

What is LLM visibility?

LLM visibility is the measurable presence of a domain, brand or source inside AI-generated answers. It covers direct domain mentions, brand references, recommendations and explicit source citations.

Traditional SEO measures rankings and clicks from search result pages. LLM visibility measures whether AI assistants understand your domain well enough to mention or cite it when users ask category-level questions.

A useful LLM visibility checker should not simply ask an AI model about a domain and count the answer as success. That only proves that the model can respond when the brand is already present in the question. Real GEO visibility is closer to a recommendation test: when a buyer asks for the best companies, tools, clinics, agencies or services in a category, does the AI assistant include your company naturally?

This page is built around that product principle. It uses the submitted domain to create one control prompt, then shifts to natural business prompts where the domain is not named. The result is more useful for SEO and GEO teams because it reflects how prospects actually use AI search during research, comparison and purchase planning.

What this checker measures

The checker is action-first: it tests a submitted domain against a small set of prompts and reports whether configured AI providers mention the domain, mention the brand, cite source URLs and surface competitors.

The result should be read as a snapshot of available provider responses, not as a permanent ranking. Its strongest use is interpretation: which answer mentioned the brand, where it appeared, which sources were cited and what next step would make the domain easier to recommend.

What the checker does not measure

A one-off checker does not prove long-term AI visibility, does not verify every provider account has coverage, and does not replace continuous monitoring across a full prompt portfolio.

Missing or failed provider data should be treated as incomplete measurement, not as zero visibility. For a reproducible methodology, read the AI Visibility Measurement guide and compare the snapshot with the LLM Visibility Software workflow.

Why LLM visibility matters

Buyers now ask AI systems to compare vendors, explain categories and recommend trusted sources. When your domain is missing from these answers, competitors can capture demand before a search result is ever shown.

Visibility in AI responses also acts as an authority signal. A cited domain earns trust at the moment of recommendation, especially when the response includes a source that users can inspect.

The traffic impact is still emerging, but the strategic impact is already clear. AI-generated answers sit above or outside traditional search results, and they often compress the user journey into a single answer. If an assistant recommends three competitors and omits your company, the lost opportunity may never appear in analytics as a missed click. It is invisible demand.

For many companies, the first practical step is not a complex dashboard. It is a simple benchmark: which prompts mention us, which prompts mention competitors, which sources are cited, and whether we appear as an expert or only when the model is directly asked about our domain.

How the checker works

The free checker validates the submitted domain, runs one domain control prompt and four natural prompts against configured providers, then analyzes returned answers for domain mentions, brand mentions, position and source citations.

Providers are never shown as tested unless the backend actually calls them. If no real provider is configured, the API returns a clean provider_not_configured status instead of fabricated data.

The control prompt asks the provider to explain the submitted domain and its services. That response helps confirm whether the AI system recognizes the company and gives the checker a first signal about the likely business category. The control prompt has low weight because it is favorable by design.

The natural prompts are the important part. They cover a business query, a commercial-intent query, a comparison query and an expertise query. For a campervan rental company, the checker can produce prompts such as “What are the best campervan rental companies in France?” and “What are the alternatives to WeVan for campervan rental in France?” For a veterinary clinic, the same pattern becomes pet-care and veterinary recommendation prompts.

The checker then reads the responses for the submitted domain, brand mentions, cited source URLs and competitor domains. Competitor detection is intentionally simple in V1: it counts domains found in responses and sources. This still gives immediate value because it shows which competitors are occupying the AI answer surface for the same prompts.

What the score means

The V1 score is intentionally simple: the domain control prompt contributes 20%, and natural business, commercial, comparison and expertise prompts contribute 80%. The score is designed to answer whether AI systems recommend the company when users ask for a service, not only whether they recognize a domain when it is named directly.

A low score usually means the tested AI responses did not associate the domain with the prompts. A moderate score often means early mentions exist but citations are weak. A strong score means the domain is appearing consistently and has citation signals to build on.

Position matters as much as the raw mention. Being recommended first or second in a generated answer is a stronger signal than being buried after several competitors. That is why the response preview labels a positive natural result as “Recommended #2” or “Recommended #4” when a position can be inferred from cited sources or the structure of the answer.

Source citations are interpreted separately from mentions. A model can mention a brand without citing it, and it can cite an external page that compares multiple companies. Owned source citations are especially valuable because they show that the AI system is using your pages as evidence, not only repeating a learned brand association.

Competitor visibility in AI answers

A standalone score is useful, but it becomes much more actionable when it is placed next to competitor visibility. If your domain appears once and a competitor appears four times, the issue is not simply low visibility. It is a competitive answer-share gap. The AI system has already learned or discovered other companies that satisfy the same intent, and those companies are occupying the recommendation set.

The competitor table in this tool is intentionally pragmatic. It extracts domains found in cited sources and response text, then counts how often those domains appear across the tested prompts. In a campervan rental example, this can surface domains such as WeVan, Roadsurfer, Blacksheep Van, Indie Campers or local rental providers when they are present in the AI answer. In a software category, the same logic can reveal vendors such as Semrush, Ahrefs, Profound, Peec AI or other platforms.

This V1 count is not a perfect ranking model. It does not yet normalize by brand aliases, merge every country-level domain, or distinguish a passing mention from a strong recommendation in every case. But it gives a fast first read on who appears around your category. That is often the highest-value insight for a free tool because it turns an abstract visibility score into a concrete competitive map.

The next layer is comparing why those competitors appear. If an AI assistant cites their location pages, comparison pages, review profiles or category guides, those URLs reveal the content formats and authority signals your own domain may need. A free checker can expose the gap; a full GEO workflow should turn that gap into briefs, internal links, schema updates and new source pages.

How to improve LLM visibility

Start with clear, authoritative pages around your main topics. Add definitions, comparison sections, FAQ blocks and structured data so AI systems can understand the entity, category and claims on each page.

Then build external validation. Mentions from trusted sources, consistent brand descriptions and citation-ready reference pages help LLMs connect your domain to the topics where you want to be recommended.

The most useful content usually answers the same prompts the checker runs. If the AI is comparing rental companies, publish a clear comparison page. If the AI is looking for experts, publish expert guides, service pages, author credentials and original advice. If the AI is citing competitors, inspect the cited pages and build a stronger, more specific source on your own domain.

Internal linking also matters. Your commercial pages, guides, FAQs and comparison pages should reinforce the same entity: who the company is, what category it belongs to, where it operates and why it is trusted. Senvarel uses this structure in its broader platform to connect AI visibility tracking with content planning, citation tracking and publishing workflows.

The free checker is a snapshot. Full monitoring should repeat the same prompt families over time, record provider changes, compare competitors and identify which new pages move the score. That is why the upgrade path points to account creation and pricing: the real value compounds when visibility is tracked week after week rather than tested once.

From one check to AI visibility monitoring

A single LLM visibility check is a diagnostic snapshot. It tells you what happened for a small set of prompts at one point in time, with the providers that were configured for the test. That is enough to validate whether your domain is recognized, whether it appears naturally, which competitors are present and which sources are being cited. It is not enough to manage AI visibility as a growth channel.

Monitoring requires repetition. The same prompt families should be tested over time so you can see whether new content, stronger internal linking, external mentions or schema improvements are changing the answer set. A domain that moves from no natural mentions to one comparison mention has made progress. A domain that starts receiving owned source citations has made a stronger move, because the AI system is now using its pages as supporting evidence.

Monitoring also needs segmentation. A national service business may need prompts by city, service line and competitor. A SaaS company may need prompts by use case, category, feature, alternative and integration. An ecommerce brand may need product category prompts, buyer guide prompts and comparison prompts. The free checker establishes the pattern, but a production workflow should generate and schedule these prompt sets automatically.

The goal is not to manipulate AI answers with one trick. The goal is to become the clearest, most useful and most cited source for the topics where your buyers ask for recommendations. That requires source pages, topical authority, entity clarity, external validation and consistent measurement. This is why free tools, AI citation tracking, ChatGPT visibility checks and GEO audits should share the same backend architecture: they are different views of the same answer engine optimization problem.

FAQ