Speech-to-text without automatically sending recordings away
Recorded audio and video contain enormous amounts of useful information. They can also contain exactly the information an organization has decided must not be uploaded to a public AI platform. Both things are true at once, and a transcription workflow has to respect that.
From recording to useful data
A transcription workflow can do far more than produce a text file, and the text file is rarely the thing that makes it worth building. Depending on the project, processing may include speech-to-text, timestamps, speaker segmentation and identification, caption files, summaries, topic extraction, action items, named entities, search indexing and metadata — delivered into whatever application needs them.
The finished result is produced in a consistent structured format regardless of which engine did the transcribing. That is deliberate, and it is the next section.
The transcription engine can change
We do not build applications that depend unnecessarily on a particular speech-to-text product. The transcription engine is a processing component with a defined input and a defined output.
As better tools become available — and in this field they arrive constantly — that component can be evaluated and replaced without redesigning the system around it. The captions still land in the same place. The search index still has the same shape.
Swapping the engine should be a Tuesday, not a project.
Batch processing changes what this costs
Not every recording needs to be transcribed in real time. Very few do. And the moment a workload stops being real-time, the hardware requirement collapses.
A transcription queue that works steadily through the day's recordings can run on modest equipment — often equipment an organization already owns and is not using overnight. The job finishes before anybody needs it, which is the only deadline that matters.
Real-time transcription is a genuine requirement for some things: live captioning, a hearing that needs a running record, an operator who has to act on what was just said. When that is the requirement we size for it. When it is not, we say so, and the project gets considerably cheaper.
- ingest video from the recorder Runs on your network
- extract audio Runs on your network
- transcribe overnight queue Runs on your network
- identify speakers against a roster Runs on your network
- captions + summary Runs on your network
- publish to the meeting portal Runs on your network
- Runs on your network Runs inside your own environment.
- Runs on managed Runs on infrastructure we operate for you.
- Runs on cloud Uses a commercial cloud or AI service.
Common uses
- Government meetings and public hearings
- Interviews and depositions
- Training videos
- Recorded presentations
- Internal meetings
- Research recordings
- Historical media and oral histories
- Video archives with no transcripts at all
- Caption generation for accessibility obligations
A note on accuracy
Speech-to-text is very good and it is not perfect. Accents, crosstalk, bad room audio, proper nouns and technical vocabulary all cost accuracy, and no vendor's benchmark number survives contact with a gymnasium microphone.
Where a transcript becomes an official record, the workflow should include human review, and we build the review step in rather than pretending it is unnecessary. Where the transcript is a search aid, near-enough is genuinely near enough — you are trying to find the ten minutes of a four-hour meeting that matter.
Deciding which of those two a given workload is takes about one conversation, and it changes the design substantially.
You do not need an AI strategy. You need a problem worth solving.
Show us the repetitive work, the archive nobody can search, the recordings piling up, or the process that looks automatable. We will work backward from there.
How quickly does the transcript actually need to be ready?
That one question changes the hardware, the cost, and often the whole architecture. It is a good place to start a transcription project.