More and more managers, buyers, and specialist departments are using AI systems for research, shortlisting, and market analysis. Instead of traditional Google searches, questions are increasingly being posed directly to systems like ChatGPT, Perplexity, or other AI-powered search and assistance systems. This creates a new challenge for companies: They not only need to be findable in traditional search engines, but also need to be correctly understood, categorized, and mentioned in the relevant context by AI systems.
A medium-sized company wanted to know how it is perceived in AI-powered research processes. The study was prompted by typical procurement and information-seeking scenarios: Which providers are mentioned in response to specific questions about products, services, or industries? How is the company described? Which competitors appear more frequently? And what content does the AI use to generate recommendations or market overviews? The goal was to analyze the company’s current AI visibility, evaluate its positioning relative to relevant competitors, and derive concrete measures to improve discoverability. The focus was on typical user queries from purchasing, sales, engineering, and management—in precisely the situations where potential customers are increasingly using AI systems today for initial orientation.
Liquam has developed a structured AI visibility screening to assess a company’s discoverability and positioning in AI-powered research processes.
The first step involves defining typical research and procurement scenarios. These include inquiries about suitable providers, technical solutions, product categories, industry expertise, references, or specific challenges.
Next, the analysis examines whether the company is mentioned in response to these queries, how it is described, and which competitors are visible in comparison. Additionally, the underlying content is evaluated: website, service descriptions, product information, references, industry references, technical articles, and publicly available company information. The analysis assesses whether the content is unambiguous, complete, thematically relevant, and sufficiently differentiated for AI systems.
Based on this, Liquam creates a concise evaluation that includes a current state analysis, a competitive comparison, and a prioritized list of recommended actions. The recommendations may include, for example, refining service pages, better describing use cases, providing more structured product information, adding additional technical content, or establishing a clearer positioning relative to competitors.
