AI-powered platform for searching broadcast video archives
An AI platform that turns decades of broadcast video into a searchable knowledge base with transcripts, faces, chapters, and natural language search.
Python
FastAPI
Argo Workflows
Kubernetes
Qdrant
React
OpenAI
Anthropic
What is Media Infrastructure?
Built for a major broadcast archive, this platform auto-transforms raw video into searchable content via AI: transcripts, face recognition, chapters, summaries, and semantic indexing.
The Problem
Decades of footage were searchable only via manually assigned metadata, making content hard to discover. Each AI task—transcription, face recognition, chapter generation—ran independently, with undocumented, assumption-based dependencies. The platform orchestrates every AI service through one scalable workflow with real dependency tracing built in.
Our Solution
Arithmiks designed the platform around a control plane and data plane architecture, keeping workflow coordination separate from media transfer for security and scale.
Tracing Reality Over Assumptions
Dependencies were assumed from service names, not real data flow. We traced actual reads/writes, rebuilt the pipeline, fixed bad dependencies, and unlocked blocked concurrency.
Visibility Into Long-Running Inference
AI stages ran minutes with no output, masking stalls as progress. Each video now runs isolated with live UI progress, so failures stay contained and retry without full restarts.
Search Built for How People Actually Ask
Raw video isn't searchable by nature. The final stage converts pipeline extracts into vector embeddings, powering NL search and a RAG chat interface over the archive.
Key Features
1. Corrected AI Dependency Graph:
Dependencies traced from real data flow, revealing wrong assumptions—chapters depend on transcripts, not frames.
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Omer and his team put in significant effort and delivered many positive contributions.
Louis-AntoineCTO at GO, Founder at Web2 Web3
