MindCheck AI integrates multimodal inputs—voice, text, and behavioral signals—through a layered architecture combining speech-to-text, emotion detection via SVM, and NLP classification to assess user mental health state. Data flows from the Presentation Layer through Processing (speech conversion, feature extraction) and Analysis (crisis detection, safety guardrails) to the Intelligence Layer, where a Gemini API-backed response module generates personalized interventions. The system employs a RAG Pipeline with Vector Database for contextual retrieval, Feature Store for model inputs, and comprehensive Monitoring to ensure safety and accuracy in high-stakes mental health scenarios. Fork this diagram to customize the crisis detection thresholds, swap the NLP model, or integrate alternative LLMs while maintaining the safety-first architecture pattern.