Track your B2B brand visibility on LLMs
Parklab Visibility is a consulting service for technical B2B sectors. It keeps watch over how ChatGPT, Claude, Gemini and other AI models talk about your company, with a dedicated dashboard and a path of analysis and action.
Your customers ask AI about you before they ask Google
A growing share of B2B informational research now happens inside a conversation with a language model. When a buyer asks ChatGPT “which is the most reliable supplier of X in Italy”, they get a concise answer with two or three brands cited, sources picked by the AI, a qualitative assessment.
That answer shapes the shortlist long before a click reaches your site.
Today you have no way to see what the models answer when the market asks about you.
- You do not know whether you are cited
- You do not know alongside whom
- You do not know with what perception
- You do not know which sites the LLMs are reading to build that answer
Parklab Visibility, in short
Parklab Visibility is a consulting service for the positioning of technical B2B brands on large language models (LLM, Large Language Models). It is a path of analysis and action rather than a licensed software: it relies on a dedicated dashboard, accessible at visibility.parklab.eu with your own credentials.
We monitor
Your brand presence across eight AI models, with up to 50 prompts relevant to your sector.
We interpret
The data on visibility, citations, perception and competitive positioning, translating it into action priorities.
We act
On the site content, on the schema.org markup, on the sources that LLMs consult to talk about you.
To build, over time, a visibility asset on the new layer of informational research.
The four dimensions we monitor
The service observes your brand positioning on LLMs along four distinct axes, each with dedicated metrics.
Monitoring covers the main generative AI models – including ChatGPT, Claude, Gemini and other systems active on the market. Each one returns different answers to the same question. Working from an aggregate view is the basis for a reliable picture.
AI visibility
Where your brand appears when the market asks the model informational questions. We measure the Visibility Score (how often your brand appears in response to a prompt), the Presence Rate by question category, the trend over time and the comparison across models.
Citations and sources
Which sites, articles, reviews and content the LLMs consult to talk about you. We identify the most frequently cited sources, their level of authority, and any gap between sources that cite you and sources that cite your competitors.
Brand perception
The image the LLMs return of you. Sentiment, associated attributes, value positioning. A qualitative reading that the model builds from training data and from sources consulted in real time.
Competitive positioning
Share of Voice on AI models versus the players in your sector, Win Rate in head-to-head comparisons, a competitive map by model and by topic. The picture of who appears, how often, and with what perception.
How we work together
The service unfolds across four phases, the last three of them on an ongoing basis.
Starting audit
We map the prompts your market actually puts to the LLMs, the recognisable players in your niche, and a visibility baseline measured on the models. It is a research phase, not a reporting one: it serves to define the scope of the work.
Monitoring activation
We configure up to 50 prompts on the dedicated dashboard. We give you access credentials for visibility.parklab.eu, a usage guide and a short onboarding session. From here on, the data updates continuously.
Monthly analysis and recommendations
Each month we read the data with you, isolate the relevant patterns, and set the action priorities. The report is more than a dashboard filed away in a folder: it is an operational summary with three or four actions to take in the following month.
Visibility actions
Updating the monitored prompts, changes to the site pages, schema.org markup implementation, content published on the sources that LLMs cite for your sector. When needed, we step in and do it ourselves.
What we mean by “prompt”
A prompt is a real question that a user puts to an LLM. It differs from a keyword: it is longer, more articulated, often comparative, always contextual. “Which are the three most reliable companies in Italy for [specific category]” is a prompt; “[category] companies” is a keyword.
Prompts fall into four recurring categories.
“what is X”, “how does Y work”
“X vs Y”, “alternatives to Z”
“which are the best X solutions for Y”
“how to choose X”, “checklist for Y”
Identifying the right prompts is the first real activity of the service. These are not the Google queries of your sector: they are the way the market asks its questions in conversation.
What you see in your dashboard
Access to visibility.parklab.eu gives you a summary view and a series of thematic deep dives.
- Visibility Scoreaggregate score of your visibility on AI models
- Share of Voiceshare of voice in your sector versus competitors
- Mentions e Citationstotal mentions and sources cited for the answers about you
- Perception Scorequalitative perception of the brand
- Competitive Gaphead-to-head comparison with the main players in your niche
- AI Crawler activitywhich AI model bots are reading your site and how often
Every metric is explorable by model, by topic, by time window. The dashboard does not replace the analysis: it enables it.
Which brands it is built for
Parklab Visibility works well where three precise conditions are present. These are the conditions that make LLM visibility monitoring a measurable investment rather than a style exercise.
A B2B market with an informational decision process
The buyer does research before contacting suppliers. The quality of the answer that ChatGPT or Claude return shapes the shortlist even before the first click on your site.
A vertical niche with recognisable players
The number of competitors the model can cite is finite. The more vertical the niche, the more every positioning move counts: the relevant prompts are few and recurring.
An editorial and technical asset base to act on
Company site, content, semantic markup. Visibility on LLMs is the reflection of the editorial oversight and the technical solidity already in place or yet to be built.
Our scope is that of technical B2B brands: industrial companies, vertical B2B software, specialist professional services, niche manufacturing. Markets where the quality of the AI answer shapes the shortlist and where every positioning move counts. This service suits these brands rather than mass-market consumer ones or anyone after a pure vanity metric.
Why monitor LLM visibility today
Classic SEO continues to hold the search with transactional intent. Informational research, the kind that precedes and shapes the decision, has largely moved inside the conversation with an AI model.
The terms GEO (Generative Engine Optimization) and LLMO (LLM Optimization) name the positioning practices dedicated to this new layer of visibility. They are young disciplines, with metrics and levers still being codified, but with one fixed point.
What is not measured cannot be governed.
Monitoring LLM visibility today means two things. Understanding where you stand and what the models say about you, that is, having awareness of your own positioning. Building over time the conditions for that position to improve, that is, acting where it counts. Without the first step, the second is a blind exercise.
Frequently asked questions
Which AI models can we monitor my brand on?
The service covers the main generative AI models active on the market, including ChatGPT, Claude and Gemini. Each one returns different answers to the same question. We work from an aggregate view, but you can always explore the data by individual model.
What are prompts and how many can we monitor?
Prompts are the real questions your market puts to the LLMs. The service configures up to 50 of them, chosen during the initial audit. The prompts can be updated over time to follow how the sector evolves.
How long does it take to see results?
Monitoring data is available from day one. The first measurable improvements in visibility arrive within three or four months, along a non-linear curve that depends on the pre-existing content base, on how often the sources in your sector are updated, and on the models’ indexing time.
How do you optimise my presence in LLM answers?
We work on three levers: the technical markup of the site (schema.org, semantic structure, accessibility to AI crawlers), the quality and clarity of the published content, and the brand presence on the third-party sources that the models consult to talk about your sector. There are no shortcuts: visibility on LLMs is the reflection of how solid your editorial and technical oversight is.
Do you also work on very vertical sectors?
Yes. Our long-standing scope is precisely that of technical and specialist niches. The more vertical the sector, the more useful monitoring on the right prompts becomes, because the number of citable players is limited and every move counts.
How does this service relate to traditional SEO?
As a continuation, not an alternative. Classic SEO continues to hold the search with transactional intent and part of the informational research. LLM monitoring holds the conversational layer, where the AI synthesises the answer before the click. The two efforts reinforce each other: a strong editorial presence serves both.
Start with a preliminary analysis of your AI visibility
If you want to understand where you stand today on AI models and what is reasonable to build over the coming months, we can start from a conversation and a first audit of your positioning.
Clients of the service receive a usage guide dedicated to the dashboard, available after activation.
