IT asks: How does Empatyzer reduce the risk of Em “making up” answers, and how is it controlled?

Empatyzer anchors Em’s responses in a prepared and validated content library combined with safety rules, which reduces improvisation and the chance that the model “makes things up.” The system uses scripts and language patterns reviewed from a psychological perspective to promote neutral, non-judgmental wording and to avoid clinical diagnosis. In ambiguous or higher-risk situations, Em redirects users to human resources or suggests contacting HR instead of generating risky guidance. The product also includes technical safeguards: audited administrative logs, restricted provider access, and action logging so decisions and changes can be traced and reviewed. Algorithms rely on organizational context and aggregated, anonymized signals rather than raw individual scores, reducing misuse potential. Models are not trained on the client’s company data, and conversation data is not used for training, in line with contracts and the privacy policy. In addition, the system applies filters and blocking rules for medical, legal and crisis-related content and routes such requests to appropriate specialists. Em’s output is produced through layered checks: content library, safety rules, user context, and an escalation path to a human. Language components are reviewed by experts, and response logic is regularly tested against scenarios that could lead to incorrect, harmful or judgmental outputs. The rollout also includes incident reporting mechanisms and clear terms that prohibit using Empatyzer for performance reviews, recruiting or therapy, which further limits misuse. With these layers, the “hallucination” risk is managed in a multi-layer, auditable way that supports IT security and internal policy compliance.

In summary, Empatyzer combines validated content, safety rules, language controls, technical safeguards and strict scope limits to minimize Em “making up” answers and to keep the system auditable.