Igiza Studio: AI-native media orchestration at scale

We engineered a robust, FFmpeg-based processing pipeline that ingests multi-GB chunked file uploads (with resume capability) and generates HLS streams and proxy formats. Processing is handled by BullMQ workers deployed on configurable AWS Fargate Spot instances (4 vCPU/8GB) to optimize compute costs at scale.

The platform integrates the FAL.ai serverless client to generate cinema-quality video, music, and images. To manage the high cost of API calls, the system utilizes an atomic credit management database leveraging BigInt precision, mathematically preventing double-charging or race conditions at enterprise scale.

The infrastructure is hardened to process 100,000 requests per minute globally, utilizing AWS WAF rate limiting and CloudFront CDN with Origin Shield. Reliability is enforced through structured JSON logging (Pino), strict Zod payload validation, and dedicated health-monitoring endpoints.

The Next.js 16 frontend features timeline-based video editing powered by Remotion. The client incorporates custom performance profiler hooks that actively track FPS, render counts, and identify long tasks (>50ms) to prevent browser throttling during heavy media rendering.