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AudioKIT

Voice AI that feels like a person. Transcription, diarization, cloning, and real-time conversational audio for calls and meetings.

Overview

AudioKIT is an end-to-end voice pipeline for teams building call centers, meeting tools, kiosks, and voice agents that need to sound human. It covers the full path — telephony in, transcript out, agent response back as speech — with the latency budgets, compliance hooks, and QA tooling that real production voice products demand.

Streaming audio terminates in LiveKit or Twilio and feeds into Whisper-family and Deepgram ASR with custom lexicons, domain adaptation, and punctuation restoration. pyannote handles speaker diarization; ElevenLabs-class TTS produces sub-second responses with barge-in and natural turn-taking. A policy layer handles PII redaction inline, and call analytics (intent, sentiment, QA scoring) run asynchronously against the transcript store.

Day 1

On day 1 you get streaming ASR on your telephony or app audio, a stock voice agent that handles interruptions, and a transcript store with search.

Week 3

By week 3 the system runs on a brand-specific voice clone, domain lexicons trained on your call recordings, QA scoring tuned to your rubric, and automated redaction validated against your compliance posture.

The difference

Build it from scratch, or start a week ahead

Building it yourself
  • Six to nine months building the orchestration, memory and recovery before a first real result.
  • An evaluation harness you write from scratch — then argue about.
  • A team learning your edge cases live, in production.
  • A black-box vendor you can't inspect, tune or move off.
With AudioKIT
  • A working AudioKIT on your data in week one — the hard parts already solved.
  • Evals seeded on day one and graded against your real workflows.
  • Senior owners with full traces and dashboards from the first deploy.
  • You own the prompts, the weights, the traces and the outcomes.
What's inside

Ships with the hard parts solved

01

Streaming ASR

Low-latency transcription with punctuation, numbers, and custom lexicons.

02

Speaker diarization

Who-said-what across long conversations and noisy rooms.

03

Real-time voice agent

Barge-in, interruption handling, and natural turn-taking.

04

Call analytics

Intents, sentiment, QA scoring, and redaction of PII.

Use cases

Where teams deploy it

Fine-tuned on your data and shaped to the workflow it lands in — these are the deployments we see most.

Stack
WhisperDeepgramElevenLabsLiveKitpyannoteTwilioFFmpeg
AI call centers and voicebots
Meeting capture and action-item extraction
Localized voice clones for brand
Compliance and QA monitoring at scale
The runway

Kick-off to production in three weeks

A fixed scope and a visible finish line — you see it working before it's load-bearing.

Week 1 — Integration

Connect telephony or app audio; stand up streaming pipelines.

Week 2 — Fine-tune

Lexicons, prompts, and voices tuned on your recordings and brand.

Week 3 — Ship

Live on production traffic with dashboards and redaction in place.

Why this kit

Voice is the hardest modality to get right — latency, interruption handling, and voice quality are all ship-blockers individually. AudioKIT ships with all three solved, so your team can focus on the conversation design instead of the signal chain.

They understand our needs quickly and are a delight to work with.
Zayn BloreCOO, Simplify ChangeRead the case study

AudioKIT, live on your stack in a week

One call to scope it, a senior team on it from day one — and a fixed scope agreed before we start.