Multi-Agent Transcript Correction

LangGraph-based agent system for AI dubbing pipeline transcript correction

Description:

Built sophisticated multi-agent system for AI dubbing pipeline transcript correction using LangGraph. Developed specialized correction agents with custom reasoning tools, confidence scoring, and validation mechanisms that seamlessly integrated with existing dubbing workflows while significantly improving quality metrics.

Project Duration:

2024 (Principal AI Engineer at Slid/Bebridge)

Key Technical Achievements:

  • Agent Architecture: LangGraph-based orchestration with specialized correction agents
  • Custom Reasoning Tools: Built domain-specific tools for transcript validation
  • Confidence Scoring: Multi-stage confidence assessment for quality assurance
  • Multi-Modal Processing: Integration of audio analysis with text processing
  • Production Integration: Seamless workflow integration without disruption
  • Quality Improvement: 40% reduction in transcript errors

Agent System Architecture:

  • Orchestrator Agent: Main coordinator managing sub-agent workflows
  • Language Detection Agent: Identifies source language and dialect variations
  • Context Analysis Agent: Understands domain-specific terminology and context
  • Correction Agent: Applies corrections with reasoning explanations
  • Validation Agent: Final quality check with confidence scoring

Technical Implementation:

  • Framework: LangGraph for agent orchestration, LangChain for tool integration
  • LLM Integration: Google Gemini 2.5 Flash for fast processing
  • Custom Tools: Audio analysis, terminology databases, correction rules
  • State Management: Persistent agent memory across correction sessions
  • Monitoring: Real-time agent performance tracking and debugging

Reasoning & Decision Making:

  • Chain of Thought: Explicit reasoning paths for each correction
  • Confidence Metrics: Probability scores for suggested corrections
  • Fallback Logic: Human-in-the-loop for low-confidence segments
  • Learning Loop: Feedback incorporation for continuous improvement

Business Impact:

  • Quality Metrics: 40% reduction in post-production corrections needed
  • Processing Speed: 3x faster than manual correction workflows
  • Cost Reduction: 50% decrease in human review requirements
  • Scalability: Enabled handling of 10x more dubbing projects

Technical Innovations:

  • Domain Adaptation: Custom fine-tuning for industry-specific terminology
  • Multi-Agent Consensus: Voting mechanism for high-stakes corrections
  • Explainable Corrections: Each change includes reasoning explanation
  • Adaptive Processing: Dynamic agent selection based on content type

Integration Features:

  • API Design: RESTful endpoints for dubbing pipeline integration
  • Batch Processing: Efficient handling of multiple transcripts
  • Version Control: Tracking of all corrections with rollback capability
  • Export Formats: SRT, VTT, and custom dubbing formats support

Skills Demonstrated:

AI Agent Development, LangGraph, LangChain, Multi-Agent Systems, NLP, Production ML, System Integration, API Design