Rivac LabsVoice AI Integration Engineer
Turning spoken audio into usable structured data means more than calling a transcription API — it means handling speaker diarization, background noise, and delivering the output in a format your downstream systems can actually consume. We build end-to-end speech-to-text pipelines using Whisper and other ASR models, including speaker diarization and structured transcript delivery, so a voice recording becomes clean, attributed, usable text. This is for products adding meeting transcription, call analysis, or voice-driven features where raw ASR output alone isn't good enough. You get a pipeline tuned to your actual audio conditions, not one calibrated on a clean demo recording.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Sample real audio from your use case — call recordings, meetings, voice notes — to assess noise and speaker conditions.
- 2
Build a pilot pipeline with diarization against those samples and measure transcript accuracy directly.
- 3
Review transcript quality and speaker attribution with your team before finalizing the pipeline configuration.
- 4
Deploy the pipeline with structured output delivery into your downstream systems and monitor accuracy over time.
What You Get
Deliverables from this engagement
- Production speech-to-text pipeline with speaker diarization
- Structured transcript output integrated into your systems
- Accuracy benchmarks against your real audio conditions
- Configuration documented for tuning as audio conditions change
Six Ways We Could Architect This
Different engagement, different build — pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.
Talk to us about Voice AI Integration Engineer