One-page planner for the whole article: the three engines (transcript-first vs hybrid vs native multimodal) with what each sees, its cost, and its best-fit content; the deciding 'words vs picture' question; the five-stage pipeline (acquire / clean & segment / summarize / shape / check) with the captions-vs-ASR fork; the three summarization strategies (stuff / map-reduce / refine) and the worked token-cost arithmetic (transcript ~6,000 tokens vs multimodal ~300 tokens/sec); the rent-vs-self-host split; and the ship gate (engine choice, timestamp grounding, LLM-as-judge faithfulness, caching, sanctioned API access).
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The book · Volume 6 of 7
AI for Video: A Complete Guide to Intelligence in Video Products: From Models and Inference to Computer Vision, Speech, Search, and Generative Video
This build sheet compares three AI video summarizer engines, walks the five-stage pipeline and does the token-cost math for transcript versus multimodal input. Volume 6 covers these capabilities in depth: ASR and diarization, search and RAG over a video archive, model artifacts and deployment topologies. Written by Nikolay Sapunov, CEO at Fora Soft.
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