Laddering interviews are a qualitative method to understand how customers value specific product attributes. However, traditional face-to-face approaches are resource-intensive and lack scalability. This paper introduces LadderChat, a conversational agent (CA) based on Large Language Models (LLMs) designed to conduct human-like laddering interviews. We propose design principles for an LLM-based CA and implement them in LadderChat. The system leverages LLMs for response analysis, adaptive probing, and visualization of Attributes-Consequences-Values (ACV) chains. A formative evaluation with six researchers provides initial insights into LadderChat’s effectiveness in guiding participants through the laddering process and generating real-time visualizations. The evaluation also identifies areas for refinement, including user interface improvements and data privacy considerations. This study contributes to ongoing efforts to enhance qualitative research methods through the application of advanced large language models.