-- As generative AI becomes an increasingly common part of the customer decision journey, organizations are beginning to ask a new question: what do AI platforms actually say about their brand?
Research from Omnisend's 2025 Shopping Report found that nearly 60% of shoppers use generative AI during product research, reflecting a broader shift in how consumers discover and evaluate products and services. Rather than comparing pages of search results, many users now ask platforms such as ChatGPT, Perplexity, and Google's AI-powered search experiences for recommendations and often base their decisions on a single response.
This change has created growing interest in measuring AI visibility audit performance—the process of evaluating how AI answer engines mention, compare, recommend, and describe a business when responding to customer questions. Unlike traditional search rankings, AI-generated answers are conversational, personalized, and difficult for businesses to monitor, making it challenging to understand how potential customers are encountering their brand.
HiBot has launched a human-run AI visibility audit designed to help organizations understand how leading AI platforms represent their business. The service evaluates responses across major AI engines through structured testing carried out by trained specialists, providing businesses with a detailed assessment of their AI presence.
The company says the shift toward AI-assisted purchasing means organizations can no longer rely solely on traditional SEO metrics, website traffic, or online reviews to understand their digital visibility. AI systems increasingly summarize information from multiple sources before presenting a recommendation, comparison, or explanation to users. Inaccurate descriptions, outdated information, or omitted brand mentions can therefore influence purchasing decisions without businesses being aware of it.
To evaluate these interactions, HiBot developed the ANSWER methodology, a framework that measures six aspects of AI-generated responses that commonly influence purchase decisions:
- Appearance – Whether a brand is mentioned when customers ask broad category questions.
- Nomination – Whether the brand is recommended for a specific use case.
- Showdown – How the brand is presented when compared directly with competitors.
- Worth – Whether pricing and value are represented accurately.
- Expertise – Whether AI correctly explains the company's products, services, and capabilities.
- Reputation – Whether AI reflects current public perception and available information fairly.
Responses collected during the audit are evaluated to produce an overall AI Visibility Score ranging from 0 to 100. The report also includes a breakdown by category and AI engine, identifies factual inconsistencies, highlights citation patterns where available, and prioritizes recommended improvements.
According to HiBot, one of the distinguishing characteristics of its approach is that each audit is conducted manually, rather than relying entirely on automated collection methods. The most important reason for this is because automated tools that scrape results or hit LLM APIs do not see what a real customer sees. When an AI engine detects automated activity, they route to a lighter or degraded model and return a stripped-down answer that no real person would ever receive.
Furthermore, AI-generated responses can vary depending on conversation history, user context, geographic location, and the platform being used. By conducting structured, logged-out sessions that simulate how prospective customers interact with AI systems, the company aims to provide a more representative assessment of real-world responses.
The resulting report is intended to help organizations better understand how AI platforms currently represent their brand and where improvements may be needed. Potential recommendations may include addressing factual inconsistencies, strengthening authoritative content, improving entity clarity, and ensuring publicly available information remains accurate and up to date.
Interest in Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) has grown alongside the adoption of AI-powered search experiences. As more consumers rely on conversational AI for recommendations, many organizations are expanding their digital marketing strategies to include monitoring and improving AI-generated brand visibility.
HiBot has also published a complimentary whitepaper explaining the methodology behind its audit process, including the evaluation framework, scoring model, and testing approach used to assess AI-generated responses across multiple platforms.
Businesses interested in understanding how AI platforms currently represent their brand can learn more or request an audit through the company's website.
About HiBot
HiBot helps businesses measure and understand how AI answer engines represent their brands across platforms including ChatGPT, Perplexity, Claude, Grok, and Google's AI experiences. Using a human-led audit methodology, the company evaluates AI-generated responses, identifies opportunities for improvement, and provides organizations with practical insights into their AI visibility.
Contact Info:
Name: David Tang
Email: Send Email
Organization: HiBot
Website: https://hibot.com
Release ID: 89197385
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