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Auto Summarisation
Generate structured packaging outputs from long-form video in the exact formats real teams use every day.
Workflow Solution
AI episode summaries, titles, and metadata for teams shipping long-form video every day
For broadcasters, TV channels, streamers, programming teams, metadata operators, and marketing teams that need one episode to become publishable copy across many surfaces without rewatching and rewriting it by hand.
Products in play
Products that solve this workflow
Start with the product that solves the immediate workflow, then expand into adjacent outputs from the same long-form video foundation.
Available now
Audio Description
Layer in accessibility from the same long-form video foundation when the workflow also needs delivery-ready AD.
The challenge
Common bottlenecks we hear from teams like yours
One episode has to serve many surfaces
A single program can need title variants, short synopses, longer summaries, guide copy, platform metadata, and marketing text.
Manual packaging work does not scale
When teams are shipping 20 to 30 episodes a day, repeated screening and rewriting becomes a bottleneck by itself.
Character limits force repeated edits
A long summary does not solve the problem when teams still need 40-character titles and tightly constrained synopsis variants.
Language workflows multiply the effort
Regional-language shows often need packaging in English and other display languages, which creates a second layer of manual work.
How we help
What changes when you add Visonic AI
Go from video to structured packaging outputs
Generate the title, synopsis, and summary lengths the workflow actually needs instead of one generic recap.
Replace blank-page writing with focused review
Editorial teams can edit and approve rather than writing every asset from zero.
Support multilingual packaging from the start
Treat language coverage as part of the generation workflow, not as an afterthought once one summary already exists.
Give operations a repeatable daily process
Turn a fragile editorial grind into a system that holds up across a full programming slate.
Output formats
What teams actually get back
The point is not a vague AI promise. It is outputs teams can actually publish, review, and route through real workflows.
40-char title
For highly constrained title surfaces where every character matters.
60-char title
For slightly more expressive title slots without losing packaging discipline.
150-char synopsis
For tight UI surfaces and minimal-summary requirements.
200-char synopsis
For richer short descriptions with slightly more narrative room.
256-char synopsis
For packaging surfaces that need a fuller compact summary.
1000-char long summary
For guide copy, internal review, and richer editorial packaging.
4000-char long summary
For deeper reference use where teams need full narrative context, not only a short synopsis.
Why not generic AI
Why generic tools break down here
The difference is not that AI exists. The difference is whether the workflow produces outputs teams can actually publish, review, and operationalize.
The product sees the episode, not just the transcript
That matters when the important packaging signals are visual, tonal, or narrative rather than explicitly spoken.
The output is structured for real publishing slots
Generic AI summaries still leave teams doing the hard work of resizing, tightening, and rewriting for every destination.
Long-form understanding matters
Episodic content needs continuity, character tracking, and story awareness across the full runtime.
Multilingual packaging is built in
Teams can generate the display-language output they need instead of treating translation as a separate manual rescue step.
Proof
Why teams move fast once they test it
This workflow is already being used in serious media environments where packaging speed and volume are non-negotiable.
Used by international TV operators
Multiple international TV houses, TV stations, and broadcasters are already using the workflow in the real world.
10x-20x productivity impact
Teams report very large gains because the repeated screening and writing work collapses into review and approval.
Operationally indispensable once adopted
Some teams say they cannot live without it and have already changed how the work gets organized around it.
Frequently asked questions
Does Auto Summarisation really generate several different summary lengths from the same episode?
Yes. That is one of the main reasons the workflow matters. The goal is not one generic summary but several packaging-ready outputs from the same source video.
Can it generate English packaging for a show in another language?
Yes. That is already part of the real use case. Teams often need title and description outputs in a display language that differs from the original program language.
Who gets the most value from this workflow?
Broadcasters, TV channels, streamers, metadata teams, and marketing operators that have to publish many long-form titles every day and cannot afford repeated manual packaging work.
How should I evaluate output quality?
Judge whether the outputs are publishable with light review. The right bar is not abstract literary quality. It is whether the title, synopsis, and summary variants are usable inside the real packaging workflow.
Does this replace editors or metadata teams?
No. It changes where they spend time. Instead of screening every asset and drafting every version from zero, they review, select, correct, and shape the final packaging.
Ready to speed up packaging?
Try free, or contact us for a packaging workflow review.