System Design Document: AI-Powered Video Content Analysis Pipeline

Version: 1.0
Date: 2026-01-19
Status: Proposed Architecture

---

1. Executive Summary

1.1 Purpose

Design a production-grade pipeline for extracting, analyzing, and synthesizing insights from video content using AI/LLM technologies. The system decomposes videos into audio (transcription), images (frame analysis), and synthesizes multimodal understanding through LLM orchestration.

1.2 Success Metrics

• Processing Throughput: 60-minute video processed in <10 minutes
• Accuracy: >95% transcription accuracy, >90% visual content extraction
• Cost Efficiency: <$2 per hour of video content
• Reliability: 99.5% successful processing rate with automatic retry

---

2. System Architecture

2.1 High-Level Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        Input Layer                               │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐          │
│  │ YouTube URL  │  │ Local Video  │  │  Video File  │          │
│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘          │
└─────────┼──────────────────┼──────────────────┼─────────────────┘
          │                  │                  │
          └──────────────────┴──────────────────┘
                              │
┌─────────────────────────────▼─────────────────────────────────────┐
│                    Ingestion & Validation                          │
│  ┌──────────────────────────────────────────────────────────────┐ │
│  │  yt-dlp Downloader → Format Validator → Metadata Extractor  │ │
│  └──────────────────────────────────────────────────────────────┘ │
└─────────────────────────────┬─────────────────────────────────────┘
                              │
          ┌───────────────────┼───────────────────┐
          │                   │                   │
┌─────────▼─────────┐ ┌───────▼────────┐ ┌───────▼────────┐
│  Audio Pipeline   │ │ Video Pipeline │ │ Metadata Store │
│                   │ │                │ │                │
│ • Extract Audio   │ │ • Sample Frames│ │ • Video Info   │
│ • Transcribe      │ │ • Extract Imgs │ │ • Timestamps   │
│ • Align Timing    │ │ • Detect Scenes│ │ • Chapters     │
│ • Detect Speech   │ │ • OCR Content  │ │ • Stats        │
└─────────┬─────────┘ └───────┬────────┘ └───────┬────────┘
          │                   │                   │
          └───────────────────┼───────────────────┘
                              │
┌─────────────────────────────▼─────────────────────────────────────┐
│                      Analysis Layer (LLM)                          │
│  ┌──────────────────────────────────────────────────────────────┐ │
│  │             Multi-Agent Orchestrator                         │ │
│  │  ┌────────────┐ ┌────────────┐ ┌──────────────┐            │ │
│  │  │  Vision    │ │ Transcript │ │ Synthesizer  │            │ │
│  │  │  Analyzer  │ │ Analyzer   │ │    Agent     │            │ │
│  │  └────────────┘ └────────────┘ └──────────────┘            │ │
│  └──────────────────────────────────────────────────────────────┘ │
│                                                                    │
│  Capabilities:                                                     │
│  • Visual content extraction (slides, diagrams, text)             │
│  • Topic detection and segmentation                               │
│  • Key moment identification                                      │
│  • Cross-modal correlation (audio ↔ visual)                      │
│  • Entity extraction and linking                                  │
│  • Concept mapping and relationships                              │
└─────────────────────────────┬─────────────────────────────────────┘
                              │
┌─────────────────────────────▼─────────────────────────────────────┐
│                    Synthesis & Output Layer                        │
│  ┌──────────────────────────────────────────────────────────────┐ │
│  │  Content Organizer → Markdown Generator → Export Manager    │ │
│  └──────────────────────────────────────────────────────────────┘ │
│                                                                    │
│  Output Formats:                                                   │
│  • Structured Markdown (sections, timestamps, references)         │
│  • JSON metadata (searchable, queryable)                          │
│  • Image gallery (extracted slides/diagrams)                      │
│  • Timeline view (correlated events)                              │
│  • Knowledge graph (entities, relationships)                      │
└────────────────────────────────────────────────────────────────────┘

2.2 Component Responsibilities

2.2.1 Ingestion Layer

• Video Downloader: yt-dlp integration with retry logic, rate limiting
• Format Validator: Check codecs, resolution, duration constraints
• Metadata Extractor: Title, description, chapters, closed captions

2.2.2 Processing Layer

• Audio Pipeline: Extract WAV, transcribe with Whisper, align timestamps
• Video Pipeline: Intelligent frame sampling, scene detection, OCR
• Storage Manager: Organize artifacts, manage cache, cleanup

2.2.3 Analysis Layer

• Vision Agent: Analyze frames for content type (slide, diagram, person, scene)
• Transcript Agent: Identify topics, speakers, key statements
• Synthesizer Agent: Correlate audio and visual, generate insights

2.2.4 Output Layer

• Content Organizer: Structure findings into logical sections
• Markdown Generator: Create formatted documentation with references
• Export Manager: Handle multiple output formats, asset bundling

---

3. Data Flow

3.1 Primary Processing Flow

Input Video
    ↓
[Download/Validate]
    ↓
┌───────────┴───────────┐
│                       │
Audio Track         Video Frames
    ↓                   ↓
[Transcription]    [Frame Analysis]
    ↓                   ↓
Timestamped Text   Categorized Images
    ↓                   ↓
    └───────┬───────────┘
            ↓
    [LLM Synthesis]
            ↓
    Unified Insights
            ↓
    [Format & Export]
            ↓
    Markdown + Assets

3.2 Frame Sampling Strategy

# Intelligent frame extraction rules
sampling_strategies = {
    'scene_change': {
        'method': 'ffmpeg_select',
        'threshold': 0.4,  # Scene change sensitivity
        'description': 'Capture frames at scene transitions'
    },
    'fixed_interval': {
        'method': 'periodic_sample',
        'interval_seconds': 5,
        'description': 'Regular sampling for comprehensive coverage'
    },
    'slide_detection': {
        'method': 'content_stability',
        'stability_duration': 2.0,  # Seconds
        'description': 'Capture static content (presentations)'
    },
    'text_density': {
        'method': 'ocr_density_threshold',
        'min_text_ratio': 0.1,
        'description': 'Prioritize frames with text content'
    }
}

3.3 Data Storage Schema

project_structure:
  {video_id}/
    metadata.json:          # Video metadata, timestamps, chapters
    raw/
      video.mp4:            # Original or downloaded video
      audio.wav:            # Extracted audio track
    processed/
      frames/               # Extracted frames
        scene_001.jpg
        scene_002.jpg
      transcription/
        full.json:          # Whisper output with word-level timing
        segments.json:      # Segmented by topic/speaker
    analysis/
      vision/
        frame_analysis.json: # Per-frame AI analysis
        extracted_text.json: # OCR results
      audio/
        topics.json:         # Detected topics with timestamps
        speakers.json:       # Speaker diarization
      synthesis/
        insights.json:       # Cross-modal findings
        timeline.json:       # Unified timeline
    outputs/
      README.md:             # Primary structured output
      slides/                # Extracted presentation slides
      assets/                # Diagrams, charts, etc.
      knowledge_graph.json:  # Entity relationships

---

4. Technology Stack

4.1 Core Dependencies

Component	Technology	Rationale	License
Video Download	yt-dlp	Most robust YouTube downloader, active maintenance	Unlicense
Video Processing	ffmpeg	Industry standard, comprehensive codec support	LGPL/GPL
Audio Transcription	OpenAI Whisper	SOTA accuracy, multiple languages, open source	MIT
Image Processing	OpenCV + Pillow	Computer vision primitives, image manipulation	BSD
OCR	Tesseract + PaddleOCR	Multi-language support, good accuracy	Apache 2.0
Vision Analysis	GPT-4V / Claude Vision	Multimodal understanding, content extraction	API
LLM Orchestration	LangGraph	State management, multi-agent coordination	MIT
Storage	SQLite + File System	Simple, portable, no server required	Public Domain
Output Generation	Python-Markdown + Jinja2	Flexible templating, markdown extensions	BSD

4.2 Alternative Considerations

Transcription Alternatives:

• AssemblyAI: Better speaker diarization, higher cost
• Deepgram: Lower latency, streaming support
• Google Speech-to-Text: Good accuracy, GCP lock-in

Vision Alternatives:

• Google Gemini Vision: Strong OCR, multimodal reasoning
• Azure Computer Vision: Good scene understanding
• Open source models: LLaVA, BLIP-2 (lower quality, free)

LLM Alternatives:

• Local models: Llama 3, Mistral (no API costs, lower quality)
• Claude Sonnet 4.5: Better reasoning, higher token limits
• GPT-4 Turbo: Fast, cost-effective for analysis

---

5. Scalability & Performance

5.1 Processing Capacity

single_worker_capacity:
  videos_per_hour: 6-12        # Depends on video length
  concurrent_videos: 3         # Parallel processing
  bottleneck: LLM_API_calls    # Rate limits on vision/text APIs
  
horizontal_scaling:
  method: task_queue           # Redis/RabbitMQ
  worker_count: unlimited      # Stateless workers
  coordination: distributed_lock
  
cost_optimization:
  batch_frame_analysis: true   # Send multiple frames per API call
  smart_sampling: true         # Avoid redundant frame extraction
  cache_results: true          # Reuse analysis for similar content
  local_models_tier: optional  # Use local for pre-filtering

5.2 Error Handling & Reliability

reliability_patterns = {
    'download_retry': {
        'max_attempts': 3,
        'backoff': 'exponential',
        'fallback': 'alternative_format'
    },
    'transcription_retry': {
        'max_attempts': 2,
        'fallback': 'lower_quality_model'
    },
    'api_circuit_breaker': {
        'failure_threshold': 5,
        'timeout_seconds': 60,
        'fallback': 'queue_for_later'
    },
    'checkpointing': {
        'enabled': True,
        'granularity': 'per_stage',
        'resume_capability': True
    }
}

---

6. Security & Privacy

6.1 Data Handling

• Video Storage: Temporary, auto-delete after processing (H.P.009-CONFIGurable retention)
• API Keys: Environment variables, never logged
• User Content: No telemetry, local processing preferred
• PII Detection: Automatic redaction in tranH.P.004-SCRIPTS (optional)

6.2 Compliance

• Copyright: User responsible for content rights
• GDPR: No user tracking, data retention policies
• Terms of Service: Respect platform ToS (YouTube, etc.)

---

7. Monitoring & Observability

7.1 Key Metrics

metrics = {
    'processing_time': {
        'download_duration': 'histogram',
        'transcription_duration': 'histogram',
        'frame_extraction_duration': 'histogram',
        'llm_analysis_duration': 'histogram',
        'total_pipeline_duration': 'histogram'
    },
    'resource_usage': {
        'disk_space_used': 'gauge',
        'api_tokens_consumed': 'counter',
        'api_cost_dollars': 'counter'
    },
    'quality': {
        'transcription_confidence': 'histogram',
        'frames_extracted_count': 'counter',
        'ocr_text_extracted_chars': 'counter'
    },
    'reliability': {
        'successful_pipelines': 'counter',
        'failed_pipelines': 'counter',
        'retry_count': 'counter'
    }
}

7.2 Logging Strategy

• Structured Logging: JSON format, correlation IDs
• Log Levels: DEBUG for dev, INFO for prod, ERROR for alerts
• Sensitive Data: Automatic redaction of API keys, user data

---

8. Deployment Architecture

8.1 Local Development

docker-compose.yml
├── worker: Python application
├── redis: Task queue (optional)
└── postgres: Metadata storage (optional, SQLite default)

8.2 Production Deployment

deployment_options:
  local_machine:
    setup: pip install + H.P.009-CONFIG file
    use_case: personal use, small batches
    
  docker_container:
    setup: docker build + volume mounts
    use_case: reproducible environments
    
  cloud_function:
    setup: AWS Lambda / GCP Cloud Run
    use_case: event-driven processing
    limits: 15min timeout, memory constraints
    
  kubernetes:
    setup: helm chart deployment
    use_case: high-throughput production
    features: auto-scaling, monitoring, redundancy

---

9. Cost Analysis

9.1 Per-Video Cost Breakdown

# Based on 60-minute video
cost_estimate = {
    'transcription': {
        'whisper_local': 0.00,      # Free, uses GPU
        'openai_whisper_api': 0.36,  # $0.006/min
        'assemblyai': 0.60           # $0.01/min
    },
    'vision_analysis': {
        'frames_extracted': 120,     # 2 per minute
        'gpt4v_cost': 0.60,          # $0.005/image
        'claude_vision_cost': 0.48,  # $0.004/image
        'gemini_vision_cost': 0.24   # $0.002/image
    },
    'llm_synthesis': {
        'tokens_consumed': 50000,    # Estimate
        'claude_sonnet_cost': 0.15,  # $3/M input tokens
        'gpt4_turbo_cost': 0.50      # $10/M input tokens
    },
    'storage': {
        'temp_storage_gb': 5,
        's3_cost': 0.12,             # $0.023/GB/month
        'egress_cost': 0.05          # Data transfer
    }
}

# Total cost range: $0.24 - $2.13 per hour of video
# Optimal: $0.80 (Gemini Vision + Claude Sonnet + Whisper API)

9.2 Cost Optimization Strategies

1. Smart Frame Sampling: Reduce frames by 70% with content-aware sampling
2. Batch API Calls: Send multiple frames per request where supported
3. Local Models for Pre-filtering: Use open models to identify valuable frames
4. Caching: Store analysis for similar content (e.g., recurring intro/outro)
5. Quality Tiers: Offer fast/cheap vs. comprehensive/expensive processing

---

10. Future Enhancements

Phase 2 (3-6 months)

• Real-time streaming analysis
• Multi-language support (automatic detection)
• Speaker identification and diarization
• Interactive timeline visualization
• Search across processed videos

Phase 3 (6-12 months)

• Video-to-video comparison (similar content detection)
• Automatic highlight reel generation
• Integration with knowledge management systems
• Fine-tuned models for domain-specific content
• Collaborative annotation and review

---

11. Risk Assessment

Risk	Probability	Impact	Mitigation
API rate limits	High	Medium	Circuit breakers, queueing, backoff
Cost overruns	Medium	High	Budget alerts, cost estimation, tier system
Transcription accuracy	Medium	Medium	Confidence scoring, human review option
Content copyright issues	Low	High	User agreement, disclaimer, DMCA compliance
Data privacy violations	Low	Critical	PII detection, secure deletion, audit logs
Platform ToS violations	Medium	High	Respect rate limits, user authentication

---

Appendices

A. Glossary

• Scene Change Detection: Computer vision technique to identify visual transitions
• Speaker Diarization: Identifying different speakers in audio
• OCR: Optical Character Recognition - extracting text from images
• Multimodal: Processing multiple types of data (text, image, audio) together

B. References

• yt-dlp Documentation: https://github.com/yt-dlp/yt-dlp
• OpenAI Whisper Paper: https://arxiv.org/abs/2212.04356
• FFmpeg Documentation: https://ffmpeg.org/documentation.html
• LangGraph Documentation: https://github.com/langchain-ai/langgraph