AI in LUX MED Group’s Contact Center: Contextual Analysis of Sales Calls

The quality of consultant conversations directly affects customer experience, sales performance, and compliance with internal standards. Calls had been reviewed manually against a scorecard, which meant that only a small fraction of all interactions could be assessed.

LUX MED Group wanted to find out whether AI could automate not only simple checks, such as whether the consultant introduced themselves, but also nuanced aspects of a conversation. The hardest challenge was objection handling: recognizing explicit and implicit concerns, investigating their cause, and assessing whether the consultant’s response was relevant and effective.

Together with the LUX MED Group team, we developed a prototype for analyzing sales calls. Anonymized recordings were transcribed and then assessed by AI models against criteria designed with LUX MED experts.

The system detected red flags and required procedural elements, including a correct introduction, the call-recording notice, and proper use of the company name. At the same time, it evaluated the full context: identifying explicit and hidden objections, assessing how their causes were explored, and determining whether the argument matched the customer’s situation.

Beyond a score, the AI generated actionable feedback highlighting strengths, effective arguments, and specific opportunities for improvement. The analysis can therefore support both quality assurance and day-to-day consultant coaching.

Information security was a core condition of the pilot. Every sampled call was manually anonymized before being shared for analysis, and the anonymization process was independently verified.

Only material stripped of information that could identify customers, consultants, or other individuals was transcribed and analyzed. This made it possible to validate the technology on real conversations while reducing personal-data processing risk.