When customers research your brand, they may ask an AI assistant. We review how your brand is described, which information it is judged on and where it is misrepresented. We verify that information against its sources, plan the improvements and measure the change on a regular basis.
First we define your brand, your target market and the scope of the review together.
In this scenario the current product document shows that the feature was added to the enterprise plan.
AI Reputation Management is the process of monitoring, assessing and improving how your brand is represented in AI answers. It reviews the accuracy, currency and sources of information about the brand, and addresses the issues found through content, data and work carried out with the relevant teams.
When your brand appears in an answer it can be compared with an outdated product, confused with another company or described with the wrong conditions. That is why, alongside visibility, what is said where you are visible also has to be assessed.
| Workstream | The question it answers |
|---|---|
| AI visibility and GEO | How often do you appear in the relevant answers? |
| AI reputation management | How accurate is what is said about you, and in which context? |
| Agentic commerce readiness | Are your product and transaction details suitable for agent assessment and transaction flows? |
Online reputation management remains the foundation of this work. News, reviews, company records and customer experience keep their importance, and how they are used in AI answers is added to the review.
We shape the review around your business model and the questions your customers ask. An error in product information is not handled the same way as a genuine customer complaint.
A new feature, your current pricing model or your service scope may be described with old information. We identify what changed and which sources should carry the current explanation.
Similarly named businesses, former executive roles or details belonging to other branches can be conflated. We check brand identity and the related records together.
Different product generations or conditions in different markets can be compared as if they were the same. We review which product, date and use case an assessment rests on.
Statements about your brand can be produced without verifiable grounds. We record the claim, assess the evidence and determine the appropriate correction routes.
Warranty, delivery, support or return conditions can be presented incomplete or out of date. We compare the commercial details that shape a customer decision against their sources.
A negative description can rest on a real product or service problem. We assess the customer experience with your team and support a correct, current account of the resolution.
Depending on your target market we build a review scope across environments such as ChatGPT, Gemini, Google's AI search experiences, Perplexity, Claude and Copilot. Platform, language, country, question set and measurement frequency are defined at the start of the project.
Review results belong to the selected questions and conditions. Answers given to different users or at different times can vary.
We review the questions and answers about your brand and identify examples of inaccurate or incomplete representation. The initial report shows findings, sources and priorities together.
Deliverable Initial report and prioritised issue list.
We check the consistency of company, product, executive and branch information. We match key facts with their evidence and currency dates.
Deliverable Verified information record and list of conflicts.
We review the sources and claims shown in answers. We separate information that is outdated, attached to the wrong source or stripped of context.
Deliverable Claim, source and assessment record.
We plan corrections to the pages and data you control and implement them with your teams. Where needed we support documented correction requests to external sources.
Deliverable Applied changes and open action list.
We coordinate the assessment of important findings with communications, product, customer experience and, where needed, specialist teams. Who owns which step is defined clearly.
Deliverable Priority, owner and intervention record.
We review the selected question set at regular intervals. Source changes, improvements in answers and recurring issues are reported separately.
Deliverable Periodic assessment and follow-up report.
We define your brand, target market, products and priority questions together. We agree which platforms are reviewed and how often.
We store the question, the answer, the sources and the measurement conditions together, so the context of a finding stays visible.
We compare the information with current documents and separate inaccuracy, outdated information, ambiguity and subjective judgement.
We bring forward the issues that can affect a customer decision and define the action and the team responsible.
We carry out content and data updates and follow up the necessary source corrections. Publications and external requests go through the agreed approval process.
We re-assess the same questions under comparable conditions. A corrected source and a changed answer are tracked separately.
We set the work plan and delivery dates together. Update and response times of AI systems vary by platform.
The illustrative examples below show how we assess different issues. You can review a claim, its evidence and the follow-up step together.
Illustrative scenario · not a real brand or a live AI result
Example Brand, a fictional software provider
In this scenario the earlier help page describes the old plan scope. The newer product document states that single sign-on was added to the enterprise plan.
Example Brand / Enterprise plan / Integration information
In this fictional product version the enterprise plan supports the single sign-on connection. The relevant identity provider has to be configured for the feature to be used. The wording “not supported” in the earlier help text does not apply to this version.
This is a fictional document prepared to explain the review method.
Example Store, a fictional retailer
The policy in this scenario states that return requests can be started at listed pickup points for certain orders. The answer rules out that conditional option entirely.
Example Store / Return request at a pickup point
Customers who see the pickup-point return option in their order details can start a request at the listed points. Not every pickup point offers this service. Product and order conditions are shown on the request screen. A request being accepted does not mean the return is automatically approved.
This is a fictional business policy, not a statement of statutory return rights or periods.
Example Service, a fictional service provider
The support records in this scenario confirm delays on some requests. The statement is not treated as inaccurate merely because it is negative.
Example Service / Support process review
In the fictional records reviewed, some requests wait during handover between teams. The priority is to assign owners to open requests and keep customers informed. The effect of the process change will be assessed with the next period's support records.
This is a fictional scenario. Real customer records are not published on this page.
Updating a source does not mean every AI answer changes at the same time. We separately check whether the same claim recurs in later measurements.
How often your brand is mentioned, how it is described and how accurate the information about it is are different indicators. Our reports treat these separately and state the question set, the period and the measurement conditions used.
We define the questions around your market and your customers' needs. We re-assess answers under the selected conditions and put important claims through human review. Information that cannot be verified is shown separately. Results belong to the sample reviewed and are not assumed to represent the answers every user receives.
An AI assistant or agent comparing products on a customer's behalf may weigh attributes, price, delivery and after-sales conditions together. An error in that information can make the assessment of your brand incomplete or wrong as well.
We identify information issues in these areas and follow up the improvements with the relevant teams. When catalogue, payment or order integration is required, we define that technical scope separately.
Accurate representation and readiness to transact belong together.
For teams that want to understand where the brand stands today. We review priority questions and platforms and present findings with their evidence and recommended steps.
For teams that want to address specific information issues. We implement content and data updates on selected topics and run the source actions and re-measurements.
For teams that want brand representation and information currency followed regularly. We assess new findings and report open actions and period-on-period change.
Scope and fee are set according to brand, product, market, language, review frequency and implementation needs.
We combine the Webtures consulting approach with Brantial, which supports AI visibility and answer analysis. Which tools are used and how wide the review runs are decided per project.
Technology supports collecting findings and tracking change. Our specialists assess the important claims, set priorities and coordinate implementation. Your product, communications and customer experience teams own the accurate information and the corrections that follow.
Studies of AI answers show that it is not enough to look at whether an answer exists; the information and the sources inside it have to be assessed as well. We keep the service approach current with official platform documentation and independent research.
In the 2025 BBC-EBU study, 45% of the 2,709 core news answers assessed contained at least one significant issue. That result belongs to news questions and should not be read as an error rate for your brand or as current model performance.
Review the scope of the studyA source being shown does not always mean it supports the claim beside it. We assess the relationship between source and claim separately.
Read the verifiability researchFor technical access, content and structured information we work from the current documentation of the relevant platforms.
Read Google's AI search guidanceIt is the process of reviewing and improving how your brand is represented in AI answers. It assesses the accuracy, currency and sources of information, plans the necessary content and data work and measures the results again.
Online reputation management covers search results, news, reviews and social channels. AI reputation management also focuses on how those sources are used in AI answers. The classic sources keep their importance; the review area widens.
They work together. GEO puts visibility in the relevant answers first. AI reputation management additionally assesses what is said about you, how accurate it is and in which context. We define the split of work along with the scope.
We do not directly control the answers AI systems produce. We improve the information and sources you control, support appropriate correction requests and review whether later answers change. A specific answer or recommendation outcome is not guaranteed.
A scope can be set across environments such as ChatGPT, Gemini, Google's AI search experiences, Perplexity, Claude and Copilot. Platform, language, market and measurement method are agreed at the start; no two programmes have the same scope.
First we assess the claim and its grounds. Then the appropriate action is planned for the relevant page, product document, business record or external source. We track the change made and the later AI answers separately.
Fair criticism is not labelled as inaccurate information. We assess the recurring customer issue with your team and support a clear account of the resolution and the current state. The aim is a truthful representation of your brand.
Correcting a source or reporting to a platform does not directly erase everything a model has learned. Update processes differ between systems. That is why we re-check the same claim in later measurements.
The audit and implementation timeline is set with the scope. Change in an AI answer depends on the sources being updated, their accessibility and how the platform works. Delivery date and the moment AI output changes are assessed separately.
We track information accuracy, currency, source support, representation, visibility and issue recurrence separately. We report the questions used, the period and the measurement conditions. A single sample answer is not treated as the outcome for the whole brand.
Reports rest on explicit criteria and reviewable findings. If a summary indicator is used, its calculation method is stated. That indicator is not presented as a universal trust score from the platforms' internal assessment.
We can review the information issues agents encounter when assessing products and sellers. Catalogue, payment, order or return integrations are separate technical work and are scoped separately when needed.
Language and market scope are chosen together. Because a brand's products, conditions and sources can differ by country, results are assessed market by market. Translation alone does not replace local verification.
Brantial can support the answer and visibility analysis used in the project. Assessing important claims, setting priorities and coordinating implementation are the responsibility of the Webtures team. The tools and modules used are described in the scope.
Brand and site details, target market, priority products or services and any issues you have observed are enough to start. In the next stage we identify the relevant documents and team owners together. You do not need to send sensitive customer data or confidential documents with the first request.
We need your teams to verify product and company information. When content, data or customer experience changes are required, owners are assigned. The plan is built around your approval and implementation capacity.
Brand, product, country, language, review frequency and implementation needs are assessed together. Scope and fee can be defined separately for the audit, the correction programme and continuous work.
It suits organisations whose brands, products or executives are researched through AI and that want to be represented with accurate information. Work is more concrete with teams that can reach the relevant data and are ready to make the necessary corrections.
Work that assesses your visibility in the relevant AI answers and your presence as a cited source.
ServicesWork that frames measurable visits, requests and commercial outcomes correctly.
ServicesWork on how product information and transaction flows interact with AI agents.
ServicesTalk through your goals in a free 30-minute call. We review the opportunities in your search and AI visibility, then set the priority steps for your growth.
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