From Vision to Production API in Under 24 Hours
How we delivered a complete video processing API with silence detection, automatic trimming, and file management in a single day using AI-powered development.
Key Results
The Challenge
A friend and ex-colleague needed a specialized video processing API. The core requirement was detecting and removing silence from videos - a common need for podcast editors, course creators, and content producers.
The specific requirements included:
- Upload videos via API with support for large files
- Detect silence segments with configurable thresholds (duration and decibel levels)
- Automatically trim detected silence and produce optimized videos
- Track processing status for async operations
- Secure download of processed files
The challenge: deliver this as a production-ready API, with proper error handling, documentation, and tests - in the shortest time possible.
The Solution
We leveraged our AI-powered development platform to deliver the complete solution in under 24 hours:
Clean Architecture Implementation
- Domain layer with Video entities and processing rules
- Use cases for each operation (Upload, Analyze, Trim, Download)
- Interface adapters with NestJS controllers and DTOs
- Infrastructure with FFmpeg integration for video processing
Four Core API Endpoints
POST /videos/process- Multipart file upload with validationPOST /videos/:id/analyze- Silence detection with configurable thresholdsPOST /videos/:id/trim- Automatic silence removalGET /videos/:id/download- Secure file streaming
Interactive API documentation available via Swagger UI at /api/docs
Real-time Collaboration via Slack
- Requirements gathered and clarified in dedicated project channel
- Each feature tracked in separate threads with progress updates
- Instant feedback loop during development
- All decisions documented and traceable
Quality Assurance
- TDD approach with tests written before implementation
- 100% code coverage on all use cases
- Swagger/OpenAPI documentation auto-generated
- Integration tests for FFmpeg processing
Technologies Used
The Results
Delivered in Record Time
The complete API was delivered in under 24 hours from initial requirements gathering to production-ready code:
- 5:09 PM - Project kickoff, initial setup (git commit: 17:09:17)
- 6:46 PM - Domain layer with entities and value objects complete
- 7:50 PM - Video upload endpoint live and deployed
- 9:13 PM - Silence detection with FFmpeg integration working
- 10:55 PM - Video trimming functional with OOM prevention
- 1:33 AM (next day) - Download endpoint with streaming complete
- 4:03 PM - All tests passing, deployed to Railway, verified with real video
Technical Deliverables
- Production-ready NestJS API with TypeScript
- FFmpeg integration for professional video processing
- Configurable silence detection (threshold, duration, padding)
- Async video processing pipeline
- Swagger documentation for all endpoints
- Docker-ready deployment configuration
Live Demo & Source Code
- Live API Documentation (Swagger) - Try the API endpoints interactively
- GitHub Repository - Full source code with Clean Architecture
Before & After: See the Difference
Watch the actual API in action - the original video with silence (3.9MB) vs. the processed result (431KB):
The processed video is ~90% smaller than the original - that's the power of intelligent silence removal!
What Made This Possible
- AI agents working in parallel on different features
- Automated TDD harness ensuring quality at speed
- Real-time Slack collaboration eliminating communication delays
- Clean Architecture enabling rapid, confident development
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