Local AI captions for internal company videos

A correct text can appear at the wrong moment: a sentence spoken at the start of a shot is shown when the next person is already on screen. A caption workflow therefore covers three jobs: recognising the speech, splitting it into segments and timing them, and each one is checked separately.

A correct caption can appear at the wrong moment

Say a company has made a video showing how to use an internal portal. The narrator talks about the “Save draft” button and moves straight on to the next screen. In the working captions the words are right, but they appear only after the view has changed. The viewer sees text that refers to the previous action.

An editor watches the segment and moves the display time. They also split a long statement so it can be read while the video plays. In this use, the useful result is a checked caption track that plays with the recording. A transcript document on its own does not show how the viewer will see the text on screen.

What the caption workflow can be built from

Docling describes audio support with speech recognition and export to the WebVTT format. Features like these can be part of preparing the material. In the trial you have to open the result in the player the company uses and judge the captions together with the picture.

A person who knows the content of the recording corrects the names of features and the abbreviations. The editor checks readability and display times. Where speech is unclear, you need to go back to the recording or to the author of the material. Adding a guessed sentence could change the instruction the viewer is meant to understand.

If the video changes after editing, a track that was checked earlier may no longer fit. So it is worth agreeing at which stage of editing the captions are made and how the person in charge knows which file they belong to. This order makes it easier to hand the material over to the person who publishes it.

What to compare with a ready-made caption editor

For a few short videos, manual correction in your current tool may be enough. A separate workflow becomes interesting when the company prepares material regularly and the text and corrections move between several applications. The purchase should cover the editor's work from the recording to a track that can be published.

The comparison covers the time spent correcting and how easily a problem segment can be found. A fast transcription helps little if the editor has to fit the whole text to the video by hand. It is also worth watching the captions on the screens where viewers actually watch the material.

Your own environment for all the material

If the recording has to stay in the company, that rule covers the video file, the extracted audio, the working text and the finished captions. We agree with IT where processing happens, which editors have access and how files are stored afterwards. The local speech engine is one part of this app, and the other tools in the workflow need to be covered too.

Syntalith's proposed first step can cover one type of video and the person who edits them today. On an approved recording, they look at the working text, the corrections and the result in the target player. That lets you decide whether you need an integration with your existing platform or whether a file prepared for import is enough.

Describe how your team adds captions to videos today. We will go through the repeated editing work and the approved environment. Pricing information is on the pricing page.

As an AI app, the working captions, the corrections and the hand-over of the file to your current platform can be joined in one place. The scope should include accepting the track in the player the company uses.

What local captions cost and when they pay back

Where to start

In the free process review we choose one task and 20 to 100 documents the team has already worked on, and within 2 working days you get a written summary with a price. We roll out the local model as part of private LLMs, and the app around it as an AI app.