AI Visibility Audit in Practice: What Five Systems Found on the Same B2B Website
- Oksana Gulyk
- Mar 23
- 3 min read
This post describes the results of an AI visibility audit conducted on a real client website. The company agreed to be named and to have the findings shared publicly.
Table of Contents
LinesLogic is a startup building an AI-powered platform for electrical engineering documentation in energy and construction projects. The platform centralises project data across single-line diagrams, specifications, bills of materials, and CAD files, and uses AI to identify discrepancies and errors before they become costly.
From the beginning, the team wanted their product to be discoverable not only through search engines but also through AI assistants. The question we set out to answer was specific: how do different AI systems interpret the same website, and where do those interpretations diverge?
How the AI Visibility Audit B2B Was Conducted
The website was evaluated using five AI systems: ChatGPT, Gemini, Claude, Perplexity, and Microsoft Copilot. A Google AI Mode simulation was also included.
Each system was asked to interpret the company based on public website content only. No prior context was provided. The evaluation focused on four questions: what does this business appear to do, who does it appear to serve, what problem does it appear to solve, and when would an AI assistant recommend this company.
Where AI systems agreed
In most cases, the interpretation was consistent. All five systems identified LinesLogic as an AI platform for analysing and reconciling engineering documentation in energy and construction projects. The core function, helping teams detect errors in project data and reduce rework, was understood across every system tested.
This result was expected. The website uses precise engineering terminology throughout: SLD, EPC, BOM, IEC, IEEE, DWG. These terms anchor the product in a specific technical category and eliminate the most common risk in AI interpretation: category collapse into generic software company or tech startup.
Where interpretations diverged
The divergence appeared in business type classification.
Most systems interpreted LinesLogic as a product company, specifically a SaaS platform with an AI component. However, some systems interpreted it as an IT services company or a software development studio.
The core function remained the same across all interpretations. But the business model classification shifted between systems.
This matters because AI recommendation logic depends not only on what a product does but on what category the business belongs to. A product company and an IT services studio are recommended in different contexts, to different types of buyers, and at different stages of a purchasing decision.
What caused the divergence
This case illustrates a pattern that appears frequently in B2B software companies with strong technical positioning.
The product is clearly defined. The problem is clearly described. Technical credibility is present. But the business category, the audience label, and the use case anchors are either missing from the main content or placed where AI crawlers do not prioritise them.
In these situations, AI interpretation is accurate at the product level but inconsistent at the business classification level. The divergence is not about whether the company is understood. It is about whether it is recommended in the right context, to the right buyer, at the right moment.
What changes next
Following the audit, the work continues across several areas: strengthening the product category signal in the main content, making the target audience explicit outside of secondary sections, structuring concrete use cases so AI systems can map them to specific buyer situations, and reviewing the information architecture for pages where classification is most critical.
After the changes are implemented, the audit will be repeated using the same five systems to measure how the interpretation shifts.
Conclusion
An AI visibility audit does not replace SEO or product positioning work. It answers a different question: how is this business being interpreted right now, before any optimisation takes place?
In this case, the answer was mostly accurate but inconsistent in one area. That inconsistency had a structural cause, and structural causes have structural corrections.
If your company operates in a specialised B2B sector and you are not certain how AI systems currently classify your business, a visibility audit is the first step before any optimisation decisions are made.
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