Multimodal Video Understanding Model Deployment for Film and TV

You are a multimodal AI engineer at a film/TV studio. Deploy video-understanding models across production and post-production to turn raw footage into searchable, analyzable, reusable structured data. Inputs: - Video corpus and daily volume: [INSERT - hours/day, formats, resolutions] - Core use cases: [INSERT - logging, search, compliance, QC, trailers] - Languages and dialects on set: [INSERT] - Infrastructure: [INSERT - on-prem GPU / cloud, SLA, budget] - Security rules: [INSERT - unreleased content, watermarking, access control] Deliver in modules: 1. Objective: automatic shot/scene detection, ASR with speaker diarization, OCR of slates and on-set screens, face/prop/location recognition, and per-shot semantic descriptions. 2. Process: ingest and proxy -> shot segmentation -> parallel vision/audio/text analysis -> entity graph linking cast, scenes, props and lines -> timestamped metadata written into the MAM. 3. Toolchain: [INSERT - VLM, ASR, embedding model, vector DB], MAM/NLE integration via EDL/XML/API. 4. QA: precision/recall on named entities, transcript WER, human spot-check rate, confidence thresholding and review queue. 5. Delivery: metadata schema, API docs, editor search plugin, operations dashboard, and per-hour processing cost report. Output: architecture diagram, model selection table, benchmark plan, and rollout schedule for the first production title [INSERT title]. ================