AI Voice Compliance Auditor

In this project, we present an enterprise-grade AI-powered platform for analyzing voice conversations, monitoring regulatory and operational compliance, and extracting actionable intelligence from audio interactions. The system is designed to process uploaded or browser-recorded conversations and transform them into structured insights through automated transcription, speaker separation, sentiment tracking, toxicity detection, intent classification, topic extraction, and compliance evaluation. Built using Python, FastAPI, React, and TypeScript, the platform combines modern full-stack engineering with advanced natural language processing and speech intelligence to support real-world compliance and quality assurance workflows.

At the core of the application is a multi-stage AI pipeline that integrates Whisper for high-quality speech transcription, speaker diarization for separating speakers, transformer-based models for summarization and intent classification, Detoxify for harmful language detection, and topic modeling techniques such as LDA for extracting discussion themes. The system further computes conversation-level intelligence including turn-taking balance, interruption patterns, action item extraction, emotion and sentiment progression, technical audio quality metrics, and an overall compliance score from 0 to 100. This architecture enables organizations to move beyond raw transcripts and toward interpretable, auditable conversation intelligence.

In addition to core audio analysis, the platform includes a robust operational layer for enterprise use cases. Users can define custom compliance rules using keywords, regular expressions, or sentiment conditions; configure automated alerts through email and webhooks; schedule recurring reports; and collaborate through annotations, tags, role-based access control, and team management workflows. The backend is implemented with FastAPI, SQLAlchemy, APScheduler, and Pydantic, while the frontend provides a responsive dashboard with synchronized audio playback, transcript highlighting, interactive charts, and export options for JSON, CSV, and PDF. This modular architecture makes the system practical for compliance teams, customer support auditing, sales quality monitoring, and multilingual conversation review.

A key contribution of this work is the unification of speech analytics, NLP-driven compliance intelligence, and collaborative enterprise reporting into a single extensible platform. Rather than treating transcription, sentiment, and compliance as disconnected tasks, the system brings them together in a continuous analysis workflow that supports both real-time review and long-term historical monitoring. The project demonstrates how modern AI models and full-stack software engineering can be combined to build a production-oriented compliance auditing solution for voice-driven business environments.

Implementation of entire project can be found here: Code