Every video gets captions,
on brand, on time, without an editor's queue
Most video now gets watched muted, which means a video without captions is a video most people skip past in the first two seconds. We build a pipeline that transcribes, times and styles captions in your brand's look. It covers every video that goes out, in as many languages as your content calendar needs, with a quick human check before anything publishes.
Where captions become the bottleneck
A video gets finished and then sits waiting for someone with editing time to add captions. That is often the real gap between a video being ready and a video being published, especially for teams putting out several videos a week across multiple channels. Captions that do get added vary in style, depending on who did them and how much time they had. That shows up as inconsistency across a brand’s video output.
Language coverage is the next cost. Captioning in one language is already a queue item. Captioning the same video in several languages for different markets usually just does not happen, so content reaches fewer audiences than it could.
Platform mismatch is the third. A caption style that reads well on a YouTube video is often too small, or poorly positioned, for a vertical Reel or Short. Reformatting per platform by hand multiplies the editing work again.
None of this shows up as one dramatic failure. It shows up as a steady drag. Caption work that should take minutes stretches into a backlog item. A quality bar holds on a quiet week and slips on a busy one. And a team that knows the fix is mechanical never finds the free afternoon to build it themselves.
What the agent transcribes, styles and ships
The agent transcribes the video with accurate timing, handling multiple speakers and background noise reasonably well. It then applies your brand’s caption style, font, color, position and animation, consistently across every video rather than depending on who is editing that day. It exports both burned-in captions for platforms that favor them and soft-subtitle files for platforms that support native caption tracks.
From one transcript, it can generate styled captions in each language your content needs. A glossary keeps brand and product names consistent even as the surrounding translation varies. It also reformats the same caption set for different platform shapes - a wide feed video, a vertical Reel - from the same source, without a separate manual pass.
Typical integrations: your video editing tool or storage for source files, and whatever platforms the captioned exports get published to, YouTube, Instagram, TikTok.
What stays with humans
A quick human review before publishing catches the kind of error transcription models still make on names, numbers and brand terms. It stays a fixed step, never something skipped under deadline pressure. Any call on tone, humor or context that needs human judgment in the caption wording belongs to your content team, not generated independently by the agent.
Guards
Every caption file is logged against the transcript it came from, so a correction can be traced and fixed at the source. The glossary of brand and product terms is enforced across every language and every export, and nothing publishes without the review step confirming names and numbers read correctly.
Before it runs unattended, we run a side-by-side dry run against a sample of your own material. That way your team can see exactly what it would have done. Every build ships with a short written runbook so your team can pause it, adjust a threshold, or roll it back without waiting on us. The running-cost estimate below is a starting budget you set, with an alert built in before it is crossed.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $500 | Transcription with speaker and timing accuracy | 3 to 7 days |
| Department package | from $2,500 | captions, dubbing and video clipping across your content team | 2 to 4 weeks |
Running cost is usually $10 to $80 a month in model usage depending on volume, with a budget cap set before launch.
Related
Pair this with AI dubbing into 10 languages for markets where dubbing fits better than subtitles. Video chaptering and clipping for social captions clips cut from longer source video. For podcast and webinar content specifically, see subtitle and transcript generation. The full package breakdown is on the AI agents service page and the performance marketing service page. For a real build of a social video pipeline, see the AI Reels editor case study and the AI video content pipeline case study.
Ready to stop letting captions be the bottleneck before publishing? Get in touch and we will set your brand caption style in the first call.
Tired of doing this by hand? We can take the whole routine off your team, not only this step: Routine takeover, from $400 →
FAQ
How much does a captions pipeline cost?
from $500 to set up transcription, styling and export for your video format, live in 3 to 7 days. Running cost is usually a few dollars per hour of video in model usage.
Will captions match our brand's exact font and colors?
Yes, the styling is set once from your brand guide and applied to every caption export after that, rather than using a generic default style.
Can it generate captions in more than one language from the same video?
Yes, from one transcript the pipeline can generate styled captions in each language your content calendar needs, keeping brand and product names consistent via a glossary.
Does a person check the captions before they go live?
A quick review step is built in by default, since transcription errors on names or numbers are the kind of mistake worth catching before a video is public.
Does this work for live or near-live content, not just pre-recorded video?
The pipeline is built for pre-recorded content with a short turnaround. Near-live captioning is possible, but it is scoped separately since it has different accuracy and latency trade-offs.