01
Scope and fit
We decide where Whisper & Speech-to-Text earns its place in your system, and where a simpler tool wins. No resume-driven architecture.
★ Whisper & Speech-to-TextLLM Models8-week engagement
SPEECH · TRANSCRIPTIONSpeech to text, production grade.
Whisper and modern speech models turn audio into accurate, searchable text. We build the pipeline around them: diarization, timestamps, and clean handoff to downstream LLM work.
LLM APIOCR engineEval pipelines
Cycle
8 weeks · fixed price
Stack
Whisper / STT
Output
Production code + eval suite
Handoff
Full source ownership
[THE SHORT VERSION]
Transcription is step one, not the whole job.
Whisper-class models transcribe accurately, but the value is in the pipeline: speaker diarization, timestamps, formatting, and feeding clean text into summarization, search, or extraction. We build the whole path, with evals on accuracy where it counts.
When it fits
- Meeting, call, and media transcription
- Voice interfaces and dictation
- Audio search and analysis pipelines
[HOW WE BUILD IT]
How we build with Whisper & Speech-to-Text.
02
Build on a tested foundation
We integrate Whisper & Speech-to-Text against a foundation we trust: typed code, CI, and observability from the first commit. Boring infrastructure, modern surface.
03
Eval before launch
An eval suite proves the build behaves before it reaches a user. We measure, then ship.
04
Handoff with ownership
Your team gets the code, the tests, and a runbook. No lock-in to us or to a vendor framework.
[WHAT YOU GET]
What the engagement leaves behind.
8 wks
Problem to production
100%
Source ownership at handoff
Eval-first
Tested before it ships
0
Framework lock-in
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APPLIED K-FRAMEWORKBring the problem.
Bring the problem.
We’ll bring the build.
Eight weeks, fixed price, eval suite at handoff. Senior engineers, full source ownership, no framework lock-in.