Brand visibility in AI-assisted buying decisions

Buyers ask ChatGPT who to choose. Find out whether the answer names your company.

We measure whether ChatGPT, Gemini, Claude and Perplexity name your brand in buyer questions, who they recommend instead of you and where their arguments come from. Then we implement changes and measure again on the same question set.

A free category assessment before any proposal
Public pricing: a single-category Snapshot from PLN 1,500 net
A proprietary measurement system and published methodology instead of screenshots

Assess your category See pricing and scope Prefer a call? Book 15 minutes

We start with the category and real buyer questions. We do not promise rankings or recommendations.

Free category assessment

0 PLN

We usually reply within one business day: whether your category is suitable for measurement and which step makes sense.

4 AI models

ChatGPT · Gemini · Claude · Perplexity

Full answers

Not single screenshots

Published methodology

Public definitions and limitations

Public pricing

Snapshot from PLN 1,500 net

Read the transparent methodology

Two buying paths

One change in buyer behaviour. Two kinds of decision.

We work where buyers compare several options and an AI answer can influence which ones they investigate next.

The buyer asks: “who should we choose?”

You sell services

We assess whether models understand your specialisation, how they justify selecting competitors and what evidence is missing from your online presence.

„Which law firm specialises in shareholder disputes?”

„Who should implement ERP for a manufacturing company?”

„Which software company understands fintech?”

A clearer understanding of your specialisation and a stronger chance of entering the shortlist.

Explore the service-business solution

The buyer asks: “what should we buy?”

You sell products

We examine which products AI recommends, which attributes it cites, whether the information is current and which sources it uses to compare options.

„Which CRM should a B2B sales team choose?”

„Which variant works best for a small office?”

„What should we buy within this budget?”

Better product-to-use-case fit and a stronger rationale for selection.

Explore the product-company solution

Decision point

An AI answer may be generated before the buyer first visits your website.

The buyer describes a problem, budget or use case. AI organises the options and explains the reasons for choosing them. Only then does the buyer visit selected websites, make contact or proceed to purchase.

We do not assume AI makes the decision for the buyer. We measure its role in research and connect it with later stages where suitable data is available.

Sessions by source / medium

Sessions from AI assistants next to Google in GA4

Session source / mediumSessions

google / organic

63,896

google / cpc

38,794

(direct) / (none)

6,683

chatgpt.com / ai-assistant

5,053

gemini.com / assistant

4,890

claude.ai / ai-assistant

3,097

copilot.com / ai-assistant

326

Illustrative reference view, not client data. Results depend on scope, period and analytics configuration.

Sessions by source / medium

Sessions from AI assistants next to Google in GA4

Session source / mediumSessions

google / organic

63,896

google / cpc

38,794

(direct) / (none)

6,683

chatgpt.com / ai-assistant

5,053

gemini.com / assistant

4,890

claude.ai / ai-assistant

3,097

copilot.com / ai-assistant

326

Illustrative reference view, not client data. Results depend on scope, period and analytics configuration.

Buyer question

“Who should implement ERP for a manufacturer?”

We measure

AI answer

The model names 3-5 companies and explains the choice.

We measure

Shortlist

The buyer checks only the options named.

We measure

Website visit

Traffic from chatgpt.com, gemini and claude.ai in GA4.

Client data

Enquiry or purchase

The decision is made among the companies from the answer.

Client data

AiVisible measures the first three stages. Website, CRM and sales data require client-side access.

What you get

Not a chart. An answer to who is recommended instead of you, and why.

Every finding in the report leads back to the question, the model's full answer, the brands named and the sources. This is what a single system record looks like.

System record · illustrative excerpt

ChatGPT · PL market · wave T0

Buyer question

„Which company should implement ERP for a mid-sized manufacturer?”

Companies named in the answer

1Competitor A2Competitor B3Competitor CYour brand: absent

Model rationale

manufacturing experience
documented implementations
MES and WMS integrations

Cited sources

competitor specialisation page
industry directory
expert article

Finding

The model knows the brand but does not connect it with manufacturing. Competitors have a specialisation page and documented projects that you lack.

Priority: A manufacturing specialisation page, two documented projects with numbers and a listing in the directory the model cites.

Illustrative data. In the report every finding carries a measurement ID, model, date and the full answer for review.

Buyer-question map

Which questions buyers use to compare options and where your brand disappears from the answer.

Who is recommended instead of you, and why

Competitors, the model's arguments and the sources it draws on.

Priorities and a measurement plan

What to change first, who implements it and how we verify the difference on the same question set.

Full scope

Strategic AI Visibility Audit

Buyer research, a baseline across four models, competitor benchmark, source analysis and a 90/180-day roadmap.

2 900-4 900 PLN netto

See the full audit scope

Services by readiness

Start at the level that matches your situation.

Every stage ends with a decision: stop, work internally or take the next step. Continuous monitoring is not a mandatory destination.

Starting point

AI Visibility Snapshot

1 500-2 900 PLN netto

When you want to assess one category and whether further investment is justified.

You receive

Brand presence, three to five competitors, examples of reasoning and a recommended next step.

Not included

Does not include a full roadmap, complete audit or implementation.

Review Snapshot scope

Strategic decision

Strategic AI Visibility Audit

2 900-4 900 PLN netto

When you see a symptom but do not yet know the causes or priorities.

You receive

Buyer research, baseline, competitors, sources, gap analysis and a 90/180-day roadmap.

Not included

Does not include automatic delivery of the entire roadmap or a guaranteed recommendation.

Discuss the audit scope

First implementation

AI Recommendation Sprint

8 900-19 900 PLN netto

When you have a diagnosis and a team ready for the first set of changes.

You receive

Selected implementation work, documentation, follow-up measurement and the next decision.

Not included

No universal fix package or guaranteed improvement.

Check Sprint readiness

Continuous development

Continuous AI Visibility

PLN 2,900-7,900 net per month

When AI is a meaningful discovery channel and you want to run further experiments.

You receive

Recurring measurement, competitors, sources, alerts and a quarterly decision review.

Not included

No full attribution where the necessary client data is unavailable.

Discuss continuous monitoring

Prices are net ranges. Before we start, we confirm the scope, outcome, exclusions and price. Not sure which level is right? Start with the free category assessment.

See full pricing

Engagement process

From buyer questions to the next marketing decision.

Every project starts with a buying situation, not a checklist of technical fixes.

Buyer research

Who buys, what they compare and which criteria matter.

Baseline

The baseline across agreed models, languages and segments.

Competitors

Brands appearing alongside or instead of yours and the reasons for their selection.

Sources

Domains and evidence types used in answers.

Strategy

Priorities across positioning, content, data and sources.

Implementation

An agreed series of changes delivered together or by your team.

Measurement

Comparison of the control core after an appropriate observation window.

Iteration

Decide whether to keep, revise or reject the hypothesis, or stop the project.

Iteration time depends on scope, implementation speed, indexing and model variability. We do not promise a universal eight-week result.

Mechanism

We do not try to persuade an algorithm. We build the evidence AI can use when recommending your brand.

A recommendation depends on how consistently the brand explains its role, answers buyer questions and appears in credible sources.

Brand positioning

We define the problem, category and buyer type the brand should be associated with. An unclear message makes accurate matching harder.

Servicesspecialisation, expertise and delivery type

Productsuse case, segment and differences between variants

Decision-support content

We organise the material buyers use to compare options: offer pages, use cases, case studies, guides and documentation.

Servicesprocess, proof of work and expert profiles

Productsselection guides, specifications and comparisons

External authority

We analyse sources that support expertise, use cases and reputation - from publications to partners, reviews and industry directories.

Servicescitations, references and expert publications

Productsreviews, distributors and testing

Measurement and experiments

We maintain a control question set, preserve answers and compare change. Every action has a hypothesis and a way to evaluate it.

Servicespresence in specialisation questions

Productsproduct fit for the use case

Proprietary technology

A system that turns variable AI answers into decision-ready evidence.

A single screenshot is not a diagnosis. The system preserves questions, answers, sources, competitors and measurement history to distinguish a one-off result from a repeatable pattern.

Read the methodology

Question sets

Categories, intents and a control measurement core.

Results by model

Presence and variability across several models.

Competitors

Brands appearing alongside or instead of the measured brand.

Cited sources

Domains and evidence types used in answers.

Change history

Comparable waves rather than an artificial position.

How the brand is described

Attributes, specialisation, rationale and errors.

Observation

What repeats in the data?

Finding

Why does it matter to the business?

Experiment

What do we change and how do we test it?

Evidence and research

Every finding should lead back to a question, answer, model and measurement date.

We show the evidence standard behind recommendations and implementation decisions. Every finding should lead to its source material and measurement conditions.

Evidence trail

From question to finding without losing context

Active system structure

Prompt set

category · intent · version

Model run

model · date · market

Answer

full answer · sources

Coding

brands · arguments · type

Finding

observation · limitation

Measurement ID
Unique run identifier

Analysis-rule version
Criteria used in the measurement

Reviewability
Full answer behind every finding

Methodology

Explicit measurement rules

We publish how question sets are built, how metrics are defined, what is compared and where interpretation is limited.

versioned method
metric definitions
limitations and variability

Read the methodology

Evidence trail

Verifiable source material

The system preserves the context needed to audit a finding instead of reducing analysis to one score.

question and full answer
model and date
sources and coded brands

See what we capture

Research programme

Public category studies

We build comparable studies showing which brands appear in buyer questions and how AI justifies their selection.

defined sample and dates
services and products measured separately
results without judging company quality

Suggest a research category

Every implementation project is documented from the baseline.

Published material must include the sample, dates, scope and limitations. No conclusive change is also a valid result.

Discuss your category

This is a fit when…

buyers compare several providers or products
the purchase requires trust or analysis
the company has strengths that can be documented
customer or category value justifies the investment
the team can implement changes

Other foundations should come first when…

the offer or product is not yet stable
buyers do not research before purchase
there is no implementation owner
availability, quality or reputation is the real problem
a guaranteed result or lead volume is expected

Technology and methodology have a named owner

Rafał Fuchs - creator of the AiVisible system.

AiVisible began with a simple problem: one question in ChatGPT is not enough to assess a company's visibility. You need a system that preserves questions, answers, competitors, sources and change history - and a method that turns the data into business decisions.

Rafał owns technology development and the analysis standard. He focuses on what can be measured, where measurement is limited and which findings are strong enough to justify action.

Meet Rafał Read the methodology

FAQ

Frequently asked questions.

No. Model answers are variable and no external company fully controls their rules. We guarantee the agreed research scope, transparent method, contracted delivery and comparable measurement - not a recommendation, deadline or fixed number of leads.

SEO focuses mainly on pages appearing in search results. AI Visibility examines whether and how a brand is included in a generated answer, for which questions and with what rationale. They share foundations but use different measures and are not interchangeable.

It can when location is an important selection criterion, the purchase has sufficient value and buyers compare several options, such as clinics, law firms or specialist services. We qualify the category first.

Yes, especially when products require comparison by use case, specification, variant or budget. We can analyse the category, brand, product family, product data and sources, starting with one commercially important category.

We begin with buyer research: customer segments, problems, selection criteria and the language used during purchase. Questions are grouped by discovery, shortlist, comparison, use case, budget and trust. A control set remains stable for comparison.

Models generate answers rather than reading a fixed ranking. Results can depend on the model, updates, date, conversation context and question wording. We therefore analyse question sets, several models and consecutive measurement waves.

We define a baseline covering questions, models, market, language and date. We measure brand presence, competitors, rationale, attributed characteristics and sources, then repeat a comparable control core after implementation.

Timing depends on research scope, the number of categories and markets, implementation speed and the observation window. We confirm it after understanding the category; there is no universal eight-week promise.

Not necessarily. The gap may be a handful of missing facts, inconsistent positioning, weak evidence, poor product data or absence from relevant sources. The audit establishes what is actually missing.

Yes. The Strategic AI Visibility Audit produces a roadmap that can be implemented by your team, AiVisible or another partner. We can limit our role to consultation and follow-up measurement.

We maintain control question groups and compare brand presence, competitors, sources and descriptions over time. Monitoring should lead to a decision: respond, run an experiment, expand the category or make no change.

Where suitable analytics are available, we can compare AI Visibility with referral traffic, forms, CRM data or e-commerce sales. Because not every contact can be attributed to one AI answer, we separate observed visibility, user behaviour and business outcomes.

Before the buyer decides

See which companies and products AI shows to your buyers.

Start with one category and questions that matter to sales. We assess fit and scope first - without an automatic subscription.

Prefer a conversation? Let's discuss the category