Broadcaster
A broadcaster with a VOD archive of long-form programmes, interviews or news editions that viewers are failing to engage with beyond the first few minutes
AI & automationReady to pilotBuilt by LabsLast updated 29 July 2026
Video ChapteringNavigable chapters with titles and summaries for every asset
Long-form video loses viewers the moment they cannot find the part they came for. Video Chaptering solves that by generating navigable chapters with titles, summaries and precise timestamps automatically from the asset itself - no manual cue-point entry, no editors required - so the structure your audience needs is ready the moment the file lands.
Automatic Chapter DetectionMulti-Language SupportLLM-Powered AnalysisPrecise TimestampsContent Categorization
Try the demo
Browse news broadcasts with automatically generated chapter markers
Failed to load video library
The problem this solves
A VOD library without chapters is a wall of content. Viewers will start from the beginning, give up within minutes if the opening is not the right bit, and leave. Broadcasters and rights holders know this: audiences they have already paid to acquire abandon perfectly good content because there is no way to navigate it.
The manual alternative - an editor entering cue points and writing chapter titles - does not scale. Even a modest library of long-form assets would require weeks of human time, and that work has to be repeated every time a clip is re-edited. The economics only work if the structure is generated rather than hand-crafted.
Who this is for
This is relevant if you are:
Rights holder
A rights holder licensing long-form content to platforms that require navigable structure as a delivery condition
Developer
A developer integrating automated post-production tooling into an existing ingest pipeline and needing chapter metadata without a separate editorial step
How it works
- 1Audio is extracted from the video asset and passed to AWS Transcribe, which returns a time-stamped transcript with word-level precision.
- 2The transcript is analysed by a large language model that identifies topic shifts and structural boundaries - the moments where one subject ends and another begins.
- 3For each detected segment the model generates a chapter title and a short summary in the source language of the content.
- 4Chapter metadata - titles, summaries and precise timestamps - is returned as structured output ready to attach to the asset in your MAM or streaming platform.
- 5The same pipeline supports multiple source languages; language detection is automatic and the output matches the source.
Large Language Models (LLMs)
Content Analysis & Segmentation
Intelligently identifies topic shifts, story boundaries, and content structure from transcript context.
AWS Transcribe
Speech-to-Text Processing
Accurate transcription with precise timestamps for reliable chapter positioning.
Multi-Lingual Support
Language-Aware Processing
Generates chapter titles and summaries in the same language as the source content.
FFmpeg
Audio Extraction
Efficiently extracts audio from video files for transcription processing.
Amazon S3
Cloud Storage & Processing
Scalable storage for videos, audio files, and generated chapter metadata.
What makes it different
Most chaptering tools require a human to confirm every boundary the model suggests. This POC treats the model output as the deliverable, not a draft for review - which is the only way it scales across a large archive. The chapter boundaries are derived from semantic content rather than scene cuts or audio silence, so they track the meaning of the footage rather than its production structure. It is designed to integrate with your existing ingest pipeline rather than replace it, which means chapter metadata can start appearing in your MAM the same day processing starts.
Current status & next steps
Ready to pilotReviewed 29 July 2026
Video Chaptering is ready to pilot. The demo on this page processes real video assets end-to-end, and the pipeline is in the state we would hand to a client for a structured evaluation against their own content.
Chapter detection works from audio content only, so a transition that is purely visual can be missed, and quality follows audio clarity and transcription accuracy. Processing time scales with duration, the pipeline is optimised for single-language content, and it suits structured material - news, educational video, presentations - better than loosely structured footage.
The next step is running the pipeline against a sample of content from your library so that chapter quality can be measured on material that is representative of what your audience actually watches.
Want to add chapters to your video library?
Tell us roughly how much long-form content you hold and what your ingest pipeline looks like, and we will tell you what a pilot against your own material would involve.