Video Search & Summarisation Blueprint
Standing up a VSS agent that ingests years of footage, answers natural language questions, and respects compliance boundaries.
Video Search & Summarisation (VSS) is transformative when you own years of footage or surveillance feeds. The blueprint below is how we landed an interactive Q&A agent for a global broadcaster.
1. Normalise the Media Estate
We transcode archives into a consistent codec, extract frames, audio, and metadata, then enrich with speech-to-text, OCR, and object detection. Everything lands in a common schema for efficient retrieval.
2. Build Multi-Modal Embeddings
Each asset produces text, image, and audio embeddings. We store them in a vector database with metadata filters (programme, talent, location, rights) so journalists can slice results instantly.
3. Compose the Agent
The agent orchestrates search, reranking, summarisation, and citation steps. Answers return highlight reels, transcript snippets, and compliance notes so editors can publish fast.
answer(query) {
clips = search.multimodal(query)
ranked = rerank.semantic(clips)
summary = llm.generate(context=ranked.top(5))
return {
synopsis: summary.text,
timestamps: ranked.timestamps,
compliance: policy.check(ranked)
}
}4. Respect Compliance
Rights management and retention rules are enforced at query time. Sensitive content requires MFA approvals and the system auto-redacts regulated material before delivery.
The result: newsroom teams answer questions in seconds instead of scrubbing footage for hours.