Media Intelligence Engine
An automated pipeline that ingests raw footage, transcodes it, and tags it using vision models.
Creative agency with thousands of archived assets.
Editors spent 30% of their time searching for clips instead of editing.
Constraints
- Processing must happen asynchronously (long-running jobs).
- Cost control on GPU instances.
- UI must be intuitive for non-technical video editors.
Architecture
System designed for isolation. Auth, Core, and Analytics scale independently based on load profiles.
Engineering Decisions
Queue-Based Processing
Used BullMQ (Redis) to handle video transcoding jobs. Decoupled the API from the heavy lifting.
Vector Search
Stored embeddings of video descriptions in Pinecone, allowing semantic search like 'show me a happy dog running' instead of keyword matches.
FFmpeg WASM
Offloaded simple video trimming to the client-side browser to save server bandwidth.
Frontend & UX Intent
[ UI_SNAPSHOT_PLACEHOLDER ]
Focus on media playback performance. Custom video player with frame-accurate seeking and metadata overlays.
Outcome & Reflection
A searchable internal 'Netflix' for raw assets.
Search retrieval time dropped to <200ms.
I would utilize edge functions for the metadata display layer earlier to reduce latency for global teams.