MVP Specification: AI-Powered Video Analysis Platform

Document Version: 1.0
Date: 2026-01-19
Owner: CODITECT Product Team
Target Launch: 90 Days from Approval

Executive Summary

The MVP delivers a production-ready video analysis pipeline that automates the extraction of insights from video content through AI-powered transcription, frame analysis, and synthesis. The MVP targets 5 pilot customers processing 100-500 hours of video monthly with a 20-day ROI guarantee.

MVP Goals

Validate Core Value Prop: Demonstrate 90%+ time savings on video analysis
Prove Technical Feasibility: Process 60-minute video in <15 minutes
Establish Cost Model: Achieve <$2 per video processing cost
Generate Case Studies: 3 referenceable customers with quantified results
Inform Product Roadmap: Learn what features drive adoption

Success Criteria

mvp_success_metrics = {
    'pilot_customers': {
        'target': 5,
        'minimum': 3,
        'industries': ['L&D', 'Market Research', 'Legal']
    },
    'processing_performance': {
        'time_per_video_60min': '<15 minutes',
        'success_rate': '>95%',
        'accuracy_transcription': '>90%',
        'accuracy_frame_analysis': '>85%'
    },
    'business_outcomes': {
        'time_savings_demonstrated': '>80%',
        'cost_per_video': '<$2.00',
        'roi_demonstrated': '>10x first year',
        'customer_satisfaction': 'NPS >40'
    },
    'technical_validation': {
        'api_uptime': '>99%',
        'error_rate': '<5%',
        'processing_queue': 'No 24hr+ backlogs'
    }
}

1. MVP Scope Definition

1.1 Core Features (Must Have)

Feature 1: Video Ingestion

User Story: "As an analyst, I want to upload a video or provide a YouTube URL so that the system can process it."

Acceptance Criteria:

✅ Accept YouTube URL (any public video)
✅ Accept file upload (MP4, AVI, MOV up to 2GB)
✅ Validate video format and duration (<2 hours)
✅ Display upload progress (0-100%)
✅ Generate unique job ID for tracking

Technical Implementation:

endpoints = {
    'POST /api/v1/jobs/create': {
        'input': {
            'source_url': 'https://youtube.com/watch?v=xxx',
            'OR': 'file_upload',
            'metadata': {
                'title': 'Optional title',
                'tags': ['tag1', 'tag2']
            }
        },
        'output': {
            'job_id': 'job_abc123',
            'status': 'pending',
            'estimated_time_seconds': 600
        }
    }
}

Out of Scope (MVP):

❌ Batch upload (multiple videos at once)
❌ Private/authenticated video sources
❌ Video editing/trimming before processing
❌ Real-time streaming analysis

Feature 2: Audio Transcription

User Story: "As an analyst, I want accurate tranH.P.004-SCRIPTS with timestamps so I can find specific moments in the video."

Acceptance Criteria:

✅ Transcribe audio with >90% word accuracy
✅ Include word-level timestamps
✅ Segment by speaker (if multiple speakers detected)
✅ Support English language (only)
✅ Display confidence scores per segment

Technical Implementation:

transcription_service = {
    'provider': 'OpenAI Whisper API',
    'model': 'whisper-1',
    'features': {
        'word_timestamps': True,
        'language': 'en',
        'response_format': 'verbose_json'
    },
    'output_format': {
        'segments': [
            {
                'segment_id': 1,
                'start_time': 0.0,
                'end_time': 5.2,
                'text': 'Welcome to today\'s presentation.',
                'confidence': 0.95,
                'words': [
                    {'word': 'Welcome', 'start': 0.0, 'end': 0.5}
                ]
            }
        ]
    }
}

Out of Scope (MVP):

❌ Multi-language support (Spanish, Mandarin, etc.)
❌ Speaker diarization (who said what)
❌ Custom vocabulary/jargon training
❌ Real-time transcription

Feature 3: Frame Extraction with Content Deduplication

User Story: "As an analyst, I want the system to extract only unique frames so I don't pay for analyzing duplicate content."

Acceptance Criteria:

✅ Extract frames using multi-strategy sampling (scene change, slide detection, fixed interval)
✅ Deduplicate frames using perceptual hashing (pHash)
✅ Target: 80-120 unique frames per 60-minute video
✅ Display extraction method per frame (why it was selected)
✅ Deduplication reduces frames by 40-60%

Technical Implementation:

frame_extraction = {
    'strategies': [
        'scene_change',      # FFmpeg scene detection
        'slide_detection',   # Content stability >2s
        'fixed_interval'     # Every 5 seconds (backup)
    ],
    'deduplication': {
        'method': 'perceptual_hash',
        'threshold': 5,  # Hamming distance
        'expected_reduction': 0.50  # 50% fewer frames
    },
    'output': {
        'frames_extracted': 180,
        'frames_after_dedup': 90,
        'frames_by_strategy': {
            'scene_change': 40,
            'slide_detection': 35,
            'fixed_interval': 15
        }
    }
}

Out of Scope (MVP):

❌ User-H.P.009-CONFIGurable sampling strategies
❌ Manual frame selection by user
❌ Deep learning-based deduplication (SSIM validation is Phase 2)
❌ Video thumbnail generation

Feature 4: Vision-Powered Frame Analysis

User Story: "As an analyst, I want the system to identify slides, extract text, and describe visual content so I don't have to manually review frames."

Acceptance Criteria:

✅ Classify frame content type (slide, diagram, person, scene)
✅ Extract text from frames (OCR)
✅ Generate 1-2 sentence description per frame
✅ Identify presentation content (slides with bullet points)
✅ Process 80-120 frames in <5 minutes

Technical Implementation:

vision_analysis = {
    'provider': 'Anthropic Claude Vision',
    'model': 'claude-sonnet-4-20250514',
    'batch_size': 5,  # Frames per API call
    'output_per_frame': {
        'content_type': 'slide|diagram|person|text|scene|mixed',
        'confidence': 0.92,
        'extracted_text': 'All visible text...',
        'description': '2-sentence summary',
        'has_presentation_content': True,
        'key_points': ['Point 1', 'Point 2']
    }
}

Out of Scope (MVP):

❌ Object detection (identifying specific objects)
❌ Face recognition
❌ Logo detection
❌ Handwriting recognition
❌ Multiple language OCR

Feature 5: Multi-Agent Synthesis

User Story: "As an analyst, I want the system to correlate audio and visual content into structured insights so I can quickly understand the video."

Acceptance Criteria:

✅ Identify 5-10 distinct topics from transcript
✅ Extract key moments (definitions, examples, transitions)
✅ Correlate transcript segments with relevant frames
✅ Generate executive summary (3-5 sentences)
✅ Complete synthesis in <3 minutes

Technical Implementation:

synthesis_pipeline = {
    'H.P.001-AGENTS': [
        {
            'name': 'topic_identifier',
            'input': 'full_transcript',
            'output': 'topics_with_timestamps',
            'execution': 'parallel'
        },
        {
            'name': 'key_moment_extractor',
            'input': 'frame_analyses',
            'output': 'important_frames',
            'execution': 'parallel'
        },
        {
            'name': 'correlation_agent',
            'input': 'topics + frames',
            'output': 'audio_visual_correlations',
            'execution': 'sequential'
        },
        {
            'name': 'synthesis_agent',
            'input': 'all_outputs',
            'output': 'final_insights',
            'execution': 'sequential'
        }
    ]
}

Out of Scope (MVP):

❌ Sentiment analysis
❌ Entity extraction (people, companies, products)
❌ Knowledge graph generation
❌ Automated video editing (highlight reels)

Feature 6: Structured Markdown Output

User Story: "As an analyst, I want a formatted markdown report with timestamps and images so I can quickly review and share findings."

Acceptance Criteria:

✅ Generate markdown with table of contents
✅ Include topics with clickable timestamp links
✅ Embed extracted slide images
✅ Provide full transcript at end
✅ Display processing metrics (cost, time)

Output Format:

# Video Title

## Summary
[3-5 sentence executive summary]

## Topics
1. [Introduction](#intro) (00:00 - 02:30)
2. [Main Concept](#concept) (02:30 - 15:45)

## Key Insights
- Insight 1 at [05:23]
- Insight 2 at [12:10]

## Extracted Slides
![Slide 1](slides/frame_001.jpg)

## Full Transcript
[00:00] Speaker: Welcome...

Out of Scope (MVP):

❌ PowerPoint export
❌ Interactive web viewer
❌ Video editing with embedded timestamps
❌ Multi-format export (JSON, XML, CSV)

Feature 7: Job Status Tracking

User Story: "As an analyst, I want to see real-time progress of my video processing so I know when it's complete."

Acceptance Criteria:

✅ Display current processing stage (downloading, transcribing, analyzing, synthesizing)
✅ Show percentage complete (0-100%)
✅ Estimate time remaining
✅ Send email notification on completion
✅ Display errors clearly if processing fails

Technical Implementation:

job_status_api = {
    'GET /api/v1/jobs/{job_id}/status': {
        'response': {
            'job_id': 'job_abc123',
            'status': 'analyzing',
            'progress_percent': 65,
            'current_stage': 'Frame analysis (45/90)',
            'estimated_time_remaining_seconds': 180,
            'created_at': '2026-01-19T10:00:00Z',
            'updated_at': '2026-01-19T10:08:00Z'
        }
    }
}

Out of Scope (MVP):

❌ Pause/resume functionality
❌ Priority queue management
❌ Batch job management
❌ Real-time WebSocket updates

1.2 Non-Functional Requirements (Must Have)

Performance

Processing Time: 60-minute video processed in <15 minutes (P95)
Uptime: 99% availability during business hours (8am-8pm ET)
Throughput: Support 5 concurrent jobs
Latency: API response times <2 seconds

Security

Authentication: API key-based authentication
Data Encryption: TLS 1.3 for data in transit
Data Retention: Auto-delete processed videos after 7 days
Access Control: User can only access their own jobs

Cost Management

Target Cost: <$2 per video (60-minute average)
Budget Alerts: Notify if job exceeds $5
Cost Tracking: Display estimated cost before processing
Rate Limiting: 20 videos/day per user

Quality

Transcription Accuracy: >90% word error rate
Frame Analysis Quality: >85% content type classification accuracy
Success Rate: >95% jobs complete without errors
User Satisfaction: NPS >40

1.3 Features Explicitly Out of Scope (Future Phases)

Phase 2 (Months 4-6)

Multi-language support (Spanish, Mandarin, French)
Speaker diarization (who said what)
SSIM validation for frame deduplication
Interactive timeline visualization
Batch upload (multiple videos)
Custom workflow H.P.008-TEMPLATES

Phase 3 (Months 7-12)

Real-time streaming analysis
Video-to-video similarity search
Fine-tuned domain models (legal, medical)
Knowledge graph generation
Integration with LMS/SharePoint/Confluence
White-label option

Not Planned

Video editing capabilities
Live captioning
Audio enhancement/cleanup
Social media sharing
Mobile app

2. MVP User Journey

Primary Flow: Process a YouTube Video

1. User lands on dashboard
   ↓
2. Click "New Analysis Job"
   ↓
3. Enter YouTube URL
   ↓
4. (Optional) Add title/tags
   ↓
5. Click "Start Processing"
   ↓
6. See job status page (progress bar)
   ↓
7. Receive email: "Processing complete!"
   ↓
8. Return to dashboard, click "View Results"
   ↓
9. See markdown report with:
   - Summary
   - Topics
   - Key insights
   - Extracted slides
   - Full transcript
   ↓
10. Download markdown file
    ↓
11. Share link with colleague (optional)

Edge Cases & Error Handling

error_scenarios = {
    'invalid_url': {
        'trigger': 'YouTube URL does not exist',
        'response': 'Error: Video not found. Please check URL.',
        'user_action': 'Retry with valid URL'
    },
    'video_too_long': {
        'trigger': 'Video duration >2 hours',
        'response': 'Error: Video exceeds 2-hour limit.',
        'user_action': 'Process shorter video or contact support'
    },
    'processing_failure': {
        'trigger': 'API timeout, network error, etc.',
        'response': 'Processing failed. Retrying automatically...',
        'user_action': 'Wait for retry (3 attempts) or contact support'
    },
    'cost_exceeded': {
        'trigger': 'Estimated cost >$5',
        'response': 'Warning: This video will cost $6.50. Proceed?',
        'user_action': 'Confirm or cancel'
    }
}

3. MVP Architecture

3.1 High-Level Components

┌─────────────────────────────────────────────────────┐
│               Frontend (React)                      │
│  • Dashboard                                        │
│  • Job submission form                              │
│  • Status tracker                                   │
│  • Results viewer                                   │
└─────────────────┬───────────────────────────────────┘
                  │ REST API
┌─────────────────▼───────────────────────────────────┐
│            Backend (FastAPI)                        │
│  • Job management API                               │
│  • Authentication                                   │
│  • Status endpoints                                 │
└─────────────────┬───────────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────────┐
│         Processing Worker (Python)                  │
│  • Video download (yt-dlp)                          │
│  • Audio extraction (ffmpeg)                        │
│  • Transcription (Whisper API)                      │
│  • Frame extraction (OpenCV)                        │
│  • Vision analysis (Claude API)                     │
│  • Synthesis (LangGraph)                            │
└─────────────────┬───────────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────────┐
│              Storage Layer                          │
│  • Job metadata (SQLite)                            │
│  • Temp files (Local filesystem)                    │
│  • Output files (Local filesystem)                  │
└─────────────────────────────────────────────────────┘

3.2 Technology Stack

mvp_tech_stack = {
    'frontend': {
        'framework': 'React 18',
        'ui_library': 'Tailwind CSS',
        'state_management': 'React Query',
        'deployment': 'Vercel or Netlify'
    },
    'backend': {
        'framework': 'FastAPI (Python 3.11)',
        'async_runtime': 'asyncio',
        'api_docs': 'OpenAPI (Swagger)',
        'deployment': 'Docker on AWS ECS or Railway'
    },
    'processing': {
        'orchestration': 'LangGraph + LangChain',
        'video_tools': 'yt-dlp, ffmpeg, OpenCV',
        'ai_apis': 'OpenAI Whisper, Anthropic Claude',
        'deduplication': 'imagehash library (pHash)'
    },
    'storage': {
        'database': 'SQLite (MVP), Postgres (production)',
        'file_storage': 'Local filesystem (MVP), S3 (production)',
        'temp_storage': '/tmp with auto-cleanup'
    },
    'monitoring': {
        'logging': 'Python logging + JSON formatter',
        'metrics': 'Prometheus (optional)',
        'alerting': 'Email notifications'
    }
}

3.3 Deployment Strategy

mvp_deployment:
  environment: cloud
  infrastructure: minimal
  
  services:
    - name: frontend
      platform: Vercel
      auto_deploy: true
      cost: $0 (free tier)
      
    - name: backend_api
      platform: Railway or Render
      instance: Starter plan
      cost: $20/month
      
    - name: processing_worker
      platform: Railway
      instance: Pro plan (2GB RAM, 2 vCPU)
      cost: $50/month
      
  total_monthly_cost:
    infrastructure: $70
    api_costs: $500 (estimated 250 videos @ $2 each)
    total: $570
    
  scaling_plan:
    current: Single worker, 5 concurrent jobs
    next_phase: Add workers, implement job queue (Redis)

4. Development Plan

4.1 Sprint Breakdown (3-Month Timeline)

Sprint 1-2: Foundation (Weeks 1-4)

Goals: Core pipeline working end-to-end

Setup project structure (backend + processing worker)
Implement video download (yt-dlp integration)
Implement audio extraction + transcription (Whisper API)
Basic frame extraction (scene change only)
Simple REST API (create job, get status)
End-to-end test: 1 video processed successfully

Deliverable: Command-line tool processes YouTube URL → outputs transcript

Sprint 3-4: Vision & Synthesis (Weeks 5-8)

Goals: Add frame analysis and AI synthesis

Implement multi-strategy frame extraction
Implement pHash deduplication
Integrate Claude Vision API for frame analysis
Build multi-agent synthesis pipeline (LangGraph)
Markdown report generation
End-to-end test: Complete markdown report generated

Deliverable: Full pipeline outputs structured markdown

Sprint 5-6: Frontend & Polish (Weeks 9-12)

Goals: User interface and production readiness

Build React dashboard (job submission, status)
Implement results viewer (markdown rendering)
Add error handling & retry logic
Cost estimation & budget warnings
Email notifications
Deploy to cloud (Railway/Vercel)
Load testing (5 concurrent videos)

Deliverable: Live MVP accessible to pilot customers

4.2 Team & Resources

mvp_team = {
    'engineering': {
        'backend_engineer': {
            'role': 'Backend API, processing pipeline',
            'fte': 1.0,
            'duration_weeks': 12,
            'cost': 80000  # 3 months contract
        },
        'frontend_engineer': {
            'role': 'React dashboard, UI/UX',
            'fte': 0.5,
            'duration_weeks': 8,
            'cost': 30000  # Part-time, weeks 5-12
        }
    },
    'product': {
        'product_manager': {
            'role': 'Requirements, pilot customer management',
            'fte': 0.25,
            'duration_weeks': 12,
            'cost': 15000
        }
    },
    'total_cost': {
        'engineering': 110000,
        'infrastructure': 2000,  # 3 months @ $570/mo + buffer
        'api_costs': 2500,  # Development + testing
        'total': 114500
    }
}

5. Pilot Customer Program

5.1 Pilot Objectives

Validate Value Prop: Demonstrate 80%+ time savings
Collect Feedback: Identify missing features
Generate Case Studies: 3 written testimonials
Refine Pricing: Test willingness to pay
Build Pipeline: Referrals to other customers

5.2 Pilot Criteria

Target Customers: 5 pilot customers

pilot_selection = {
    'must_have': [
        'CODITECT existing customer (preferred)',
        'Processing 100-500 hours video/month',
        'Willing to provide feedback weekly',
        'Able to start within 30 days',
        'Decision-maker accessible (VP-level)'
    ],
    'nice_to_have': [
        'Multiple use cases to test',
        'Technical team can provide API feedback',
        'Willing to be public reference',
        'Industry diversity (L&D, research, legal)'
    ]
}

5.3 Pilot Offer

Free Implementation ($50K value)
3-Month Free Access ($24K value)
Total Value: $74K per pilot customer

In Exchange:

Weekly feedback sessions (30 min)
Written case study with metrics
Public reference (press release, testimonial)
2 hours of user testing (observe H.P.006-WORKFLOWS)

6. Success Metrics & KPIs

6.1 Technical KPIs

technical_kpis = {
    'processing_performance': {
        'avg_processing_time_60min_video': '<15 minutes',
        'p95_processing_time': '<20 minutes',
        'success_rate': '>95%',
        'cost_per_video': '<$2.00'
    },
    'quality_metrics': {
        'transcription_accuracy': '>90% WER',
        'frame_deduplication_rate': '40-60%',
        'vision_classification_accuracy': '>85%',
        'user_reported_accuracy': '>80% "Good or Excellent"'
    },
    'reliability': {
        'api_uptime': '>99%',
        'error_rate': '<5%',
        'retry_success_rate': '>90%'
    }
}

6.2 Business KPIs

business_kpis = {
    'pilot_program': {
        'customers_enrolled': 5,
        'customers_active_week_1': 5,
        'customers_active_month_3': 4,  # Allow 1 dropout
        'videos_processed': 200,  # 40 per customer avg
        'case_studies_completed': 3
    },
    'customer_satisfaction': {
        'nps_score': '>40',
        'feature_satisfaction': '>4.0/5.0',
        'would_recommend': '>80%',
        'willing_to_pay': '>60%'
    },
    'business_validation': {
        'time_savings_demonstrated': '>80%',
        'roi_demonstrated': '>10x',
        'willingness_to_pay_monthly': '>$5000',
        'pilot_to_paid_conversion': '>60%'
    }
}

6.3 Go/No-Go Decision Criteria

At End of 3-Month Pilot:

go_no_go = {
    'go_criteria': {
        'minimum_requirements': [
            'technical_kpis["success_rate"] > 0.90',
            'business_kpis["customer_satisfaction"]["nps_score"] > 30',
            'business_kpis["pilot_program"]["customers_active_month_3"] >= 3',
            'business_kpis["business_validation"]["roi_demonstrated"] > 5.0',
            'business_kpis["business_validation"]["pilot_to_paid_conversion"] > 0.40'
        ],
        'decision': 'Proceed to general availability (GA)'
    },
    'pivot_criteria': {
        'indicators': [
            'NPS < 30 (low satisfaction)',
            'Success rate < 90% (quality issues)',
            'Pilot-to-paid < 40% (pricing/value issues)'
        ],
        'actions': [
            'Extend pilot 30 days',
            'Adjust pricing',
            'Add missing features',
            'Improve quality'
        ]
    },
    'no_go_criteria': {
        'indicators': [
            'customers_active_month_3 < 2',
            'roi_demonstrated < 3.0',
            'willing_to_pay < 40%'
        ],
        'decision': 'Shelve product, insufficient market demand'
    }
}

7. Risks & Mitigation

7.1 Technical Risks

Risk	Probability	Impact	Mitigation
API Rate Limits	Medium	High	Implement queue, fallback to GPT-4V, batch requests
Processing Time Exceeds 15min	Medium	Medium	Optimize frame extraction, parallel processing
Quality Below 85%	Low	High	SSIM validation, manual review queue
Cost Overruns (>$2/video)	Low	Medium	Aggressive deduplication, cost alerts

7.2 Business Risks

Risk	Probability	Impact	Mitigation
Can't Find 5 Pilots	Low	Critical	Leverage CODITECT customer base, offer higher incentives
Pilot Dropout (>2 customers)	Medium	High	Weekly check-ins, address issues immediately
No Willingness to Pay	Medium	Critical	Test multiple price points, validate ROI
Competitors Launch First	Low	Medium	Speed to market (90 days), differentiate on integration

Appendix: MVP Checklist

Pre-Launch Checklist

Launch Day Checklist

Deploy to production environment
Send pilot customers access credentials
Schedule kickoff calls (30 min each)
Set up monitoring alerts
Prepare incident response plan

Week 1 Post-Launch

Daily check-ins with pilot customers
Monitor error logs and API costs
Collect feedback on onboarding experience
Address critical bugs within 24 hours

MVP Target Launch: 90 days from approval
Expected Investment: $115K (engineering + infrastructure)
Expected Outcome: 3-5 referenceable customers, validated product-market fit, clear path to $5M ARR

Executive Summary​

MVP Goals​

Success Criteria​

1. MVP Scope Definition​

1.1 Core Features (Must Have)​

Feature 1: Video Ingestion​

Feature 2: Audio Transcription​

Feature 3: Frame Extraction with Content Deduplication​

Feature 4: Vision-Powered Frame Analysis​

Feature 5: Multi-Agent Synthesis​

Feature 6: Structured Markdown Output​

Feature 7: Job Status Tracking​

1.2 Non-Functional Requirements (Must Have)​

Performance​

Security​

Cost Management​

Quality​

1.3 Features Explicitly Out of Scope (Future Phases)​

Phase 2 (Months 4-6)​

Phase 3 (Months 7-12)​

Not Planned​

2. MVP User Journey​

Primary Flow: Process a YouTube Video​

Edge Cases & Error Handling​

3. MVP Architecture​

3.1 High-Level Components​

3.2 Technology Stack​

3.3 Deployment Strategy​

4. Development Plan​

4.1 Sprint Breakdown (3-Month Timeline)​

Sprint 1-2: Foundation (Weeks 1-4)​

Sprint 3-4: Vision & Synthesis (Weeks 5-8)​

Sprint 5-6: Frontend & Polish (Weeks 9-12)​

4.2 Team & Resources​

5. Pilot Customer Program​

5.1 Pilot Objectives​

5.2 Pilot Criteria​

5.3 Pilot Offer​

6. Success Metrics & KPIs​

6.1 Technical KPIs​

6.2 Business KPIs​

6.3 Go/No-Go Decision Criteria​

7. Risks & Mitigation​

7.1 Technical Risks​

7.2 Business Risks​

Appendix: MVP Checklist​

Pre-Launch Checklist​

Launch Day Checklist​

Week 1 Post-Launch​

Executive Summary

MVP Goals

Success Criteria

1. MVP Scope Definition

1.1 Core Features (Must Have)

Feature 1: Video Ingestion

Feature 2: Audio Transcription

Feature 3: Frame Extraction with Content Deduplication

Feature 4: Vision-Powered Frame Analysis

Feature 5: Multi-Agent Synthesis

Feature 6: Structured Markdown Output

Feature 7: Job Status Tracking

1.2 Non-Functional Requirements (Must Have)

Performance

Security

Cost Management

Quality

1.3 Features Explicitly Out of Scope (Future Phases)

Phase 2 (Months 4-6)

Phase 3 (Months 7-12)

Not Planned

2. MVP User Journey

Primary Flow: Process a YouTube Video

Edge Cases & Error Handling

3. MVP Architecture

3.1 High-Level Components

3.2 Technology Stack

3.3 Deployment Strategy

4. Development Plan

4.1 Sprint Breakdown (3-Month Timeline)

Sprint 1-2: Foundation (Weeks 1-4)

Sprint 3-4: Vision & Synthesis (Weeks 5-8)

Sprint 5-6: Frontend & Polish (Weeks 9-12)

4.2 Team & Resources

5. Pilot Customer Program

5.1 Pilot Objectives

5.2 Pilot Criteria

5.3 Pilot Offer

6. Success Metrics & KPIs

6.1 Technical KPIs

6.2 Business KPIs

6.3 Go/No-Go Decision Criteria

7. Risks & Mitigation

7.1 Technical Risks

7.2 Business Risks

Appendix: MVP Checklist

Pre-Launch Checklist

Launch Day Checklist

Week 1 Post-Launch