Video Content Analysis System

Core Logic

Implements DAG-based workflow execution with parallel branch support. Manages agent lifecycle, routes inter-agent messages via A2A protocol, handles failures with retry and fallback strategies, and aggregates results. Provides workflow visualization, execution tracing, and self-healing capabilities for transient failures.

Core Logic

Validates uploaded video files, extracts technical metadata (resolution, codec, duration, bitrate), performs format compatibility checks, generates preview thumbnails, and segments video for parallel processing. Outputs structured video manifest for downstream agents.

Core Logic

Leverages GPT-4-Vision and Claude-3.5-Sonnet for multi-frame visual analysis. Detects scene transitions, identifies objects and products, recognizes text overlays, analyzes lighting and composition, and tracks visual consistency. Outputs structured scene descriptions with timestamps and confidence scores.

Core Logic

Uses Whisper-Large-V3 for accurate speech-to-text transcription with timestamp alignment. Performs speaker diarization, detects background music and ambient noise, assesses audio quality metrics (clarity, noise level, volume consistency), and identifies copyrighted audio. Outputs timestamped transcripts with speaker labels.

Core Logic

Applies NLP models to transcripts and visual descriptions to extract semantic meaning. Classifies content topics and categories, analyzes sentiment and tone, detects key messages and claims, identifies brand mentions, and assesses content policy compliance. Outputs content summary with topic tags and sentiment vectors.

Core Logic

Runs authenticity detection models to identify AI-generated content, stock footage reuse, and manipulation artifacts. Analyzes creator-environment consistency, verifies product presence in video, cross-references with creator history, and detects suspicious patterns. Outputs authenticity score (0-100) with detailed findings.

Core Logic

Evaluates technical quality (resolution, bitrate, frame stability), production quality (lighting, composition, audio clarity), and content quality (engagement potential, clarity of message). Uses multi-factor quality models to output quality tier classification (Premium/Standard/Basic) with detailed breakdowns.

Core Logic

Auto-generates video titles, descriptions, tags, and categories based on content analysis. Optimizes for platform-specific SEO, suggests trending hashtags, extracts product mentions for linking, and generates accessibility descriptions. Outputs structured metadata ready for marketplace listing.

Core Logic

Analyzes quality tier, creator metrics, content uniqueness, market demand, and competitive pricing to recommend optimal price points. Uses pricing elasticity models, considers seasonal factors, and provides price range recommendations with expected sell-through rates.

Core Logic

Synthesizes findings from all analysis agents to generate specific, actionable improvement recommendations. Prioritizes issues by impact, provides examples of good practices, links to educational resources, and estimates quality improvement potential for each recommendation. Outputs prioritized improvement plan.