Transcripts accurate enough to cut video by,
not just read back
A transcript that is 90 percent accurate is often useless for editing or search, because the ten percent that is wrong is never evenly spread. We build transcription tuned to your actual audio, with word-level timing accurate enough to cut video by.
What it is and who needs it
A speech and transcription product turns audio into text accurate enough to actually use, word-level timing for video editing, speaker labels for meeting notes, structured enough to search or analyze rather than just read once and discard. It fits content pipelines, meeting records, call centers, anything where transcription accuracy directly affects what gets built on top of it. It is not a fit for casual one-off transcription where a rough, unreviewed transcript is good enough; the value is in pipelines where the transcript is itself a working input to something else.
What is inside
The transcription model gets tuned against your actual audio, your speakers’ accents, your background noise profile, your domain vocabulary, since generic transcription accuracy numbers rarely hold up on real production audio. Word-level timestamps are aligned precisely enough to support exact video cuts or precise search within long recordings, not just rounded to the nearest sentence. Speaker separation identifies who said what in multi-speaker recordings, essential for meeting notes or interview transcripts where attribution matters. Processing runs locally when privacy rules or cost at scale rule out sending audio to a third-party API, keeping sensitive audio inside infrastructure you control.
How we build it
We start with real samples of your actual audio, not a generic benchmark, since transcription accuracy on your specific accents, noise profile and vocabulary is what matters, and that only shows up by testing against the real thing. Timestamp alignment and speaker separation get validated against manually checked samples before the pipeline is trusted for production use. Where local processing is the right call for privacy or cost, we benchmark it against API-based alternatives honestly, rather than defaulting to whichever is easier to set up. Structured output gets wired directly into whatever consumes the transcript, an editing tool, a search index, a CRM, so the transcript is immediately usable rather than another file to manually import.
What to watch
Accuracy on your actual audio is the thing to verify before trusting this pipeline for anything consequential, since published accuracy benchmarks for any speech model rarely hold up unchanged against your specific accents, background noise and vocabulary. This is why we tune and test against your real samples rather than quoting a generic number. Privacy is the other real consideration, processing audio locally avoids sending potentially sensitive recordings to a third party, but it is a deliberate tradeoff against the convenience and sometimes-higher accuracy of an API-based model, worth discussing explicitly rather than defaulting to whichever is easier to wire up first. Keep a small manually verified sample on hand as a running accuracy check, since audio conditions can drift over time (a new microphone, a noisier environment) in ways that are easy to miss until output quality has already slipped.
Timeline and price
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| MVP | from $1,800 | Single-speaker transcription, word-level timestamps, structured output | 3 to 4 weeks |
| Production | from $4,500 | Speaker separation, domain vocabulary tuning, confidence-based review flow | 5 to 7 weeks |
| Full control (handover-ready) | from $7,650 | Everything in Production, plus a full handover package: architecture docs, test suite, admin access audit, and a walkthrough so your own team or another vendor can run it without us | 7 to 8 weeks |
Running cost on top of the build is usually $10 to $50 a month in model or API costs, depending on audio volume and whether processing is local.
What you own at the end
You own the transcription pipeline, the transcripts and timing data, and the full source code. Where local models are used, you own the model weights and the processing infrastructure outright, with no per-minute billing from a third party.
Related
Pairs with AI content studio and AI avatar and video product for pipelines where transcription feeds directly into editing or captioning. See the AI agents service page for the broader range of AI product builds. Real builds: the AI reels editor case study, where local transcription with word timestamps drives the editing timeline, and the AI video content pipeline case study. Have hours of recordings nobody has transcribed yet? Get in touch.
FAQ
How much does a speech and transcription product cost?
From $1,800 for a single-speaker transcription pipeline with word-level timestamps. Speaker separation, domain vocabulary tuning and structured output into another system runs $4,500 to $7,500.
How long does it take?
Three to four weeks for clean, single-speaker audio with a standard model. Noisy audio, multiple speakers, or a specialized vocabulary (medical, legal, a specific industry) extends tuning time, typically to six to eight weeks.
What is the stack?
A speech-to-text model, run locally with an open model when privacy or cost demands it, or through an API for convenience and accuracy, with Python handling timestamp alignment, speaker separation and structured output.
Who owns the transcripts and the pipeline?
You. Transcripts, timing data and the code are yours. When processing happens locally, audio never leaves infrastructure you control, which matters for anything sensitive.
How accurate is the transcription?
Accuracy depends on audio quality and how much domain-specific vocabulary appears; we tune against real samples of your actual audio and report real word error rates rather than a generic claim, with low-confidence segments flagged for review.