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How it works

Four layers.
One real call.

The architecture below is what happens when Rosa, calling from Houston in Spanish, reaches Maya at a clinic that operates in English. Same pipeline, regardless of which doorway the call comes through.

The architecture real-time, end to end
↓ VOICE IN
04
Integration
CCT · TEL · CRM · EHR
03
Trust
C2PA · residency
02
Language
Translate · correct
01
Voice
Capture · clone
↓ VOICE OUT
One real call · CALL_8F3E REC
L01
Ring
Twilio SIP trunk · es-MX detected
L01
Maya: "Hello"
Speaker embedding warmed · ready
L02
Rosa hears it · Spanish
Cloned in Maya's voice · in real time
L02
Correction appended
"my account" → "my mother's account"
L03
Manifest signed
C2PA · 47 utterances · 2 corrections
Five events · Four minutes · One conversation
01 Voice
Capture & clone

Stream audio in, generate a per-call speaker embedding, render in the listener's ear in real time.

02 Language
Translate & correct

Glossary-aware NMT runs token-by-token. Late corrections append to the transcript instead of rewriting it.

03 Trust
Attest

Every utterance ships with a C2PA-signed manifest.

04 Integration
Where it plugs in

One pipeline, six adapters: contact center, telephony, AI voice agents, CRM, EHR, browser. Same model, different doorway.

LAYER 01 · Voice

Rosa says “Buenas tardes.” In real time, Maya hears it in her own voice: same warmth, same hesitation, just a different language.

Before the first word

Maya picks up. The pipeline is already there.

Between Rosa's first ring and Maya's hello, TransVoix provisions a language-detected, region-pinned session, ready to stream voice in both directions. Here's what happens, step by step.

01 INGRESS SIP INVITE arrives at TransVoix media bridge
02 AUTH Tenant matched · region pinned to us-east-1
03 SESSION Agent embedding (agt_maya_8f3e) warmed in GPU pool
04 LANG DETECT Caller audio classified · es-MX (confidence 0.97)
05 READY Both legs joined · pipeline streaming in real time
Voice cloning · bidirectional

Their words, in your voice. Your words, in theirs .

TransVoix doesn't replace voices with synthetic narration. It clones each speaker from a few seconds of audio, then carries that voice across the language barrier in both directions, so customers and agents recognize each other through accent, cadence and warmth, not just words.

Maya · Agent
English (US)
VOICE → VOICE
Rosa · Caller
hearing Spanish (MX)

Maya speaks English in her own voice. Rosa hears Spanish in Maya's voice.

Rosa · Caller
Spanish (MX)
VOICE → VOICE
Maya · Agent
hearing English (US)

Rosa speaks Spanish in her own voice. Maya hears English in Rosa's voice.

What we don't do: read a translation back in a generic voice. The cloned voice is ephemeral, generated per-call, never stored, never re-used. Voice consent & ethics →

Real-time pipeline

How we keep it real-time.

Most translation pipelines are slow because each stage waits for the last to finish. TransVoix streams speech recognition, translation and cloned-voice synthesis in parallel, so the conversation keeps its rhythm.

Stage What happens
Capture Streaming voice-activity detection; chunked at phrase boundaries.
Speech → Text Multilingual ASR with overlap windowing for late corrections.
Translate Glossary-aware NMT, streaming token-by-token.
Voice Clone Pre-warmed speaker embedding for the cloned voice.
Render & Stream 48kHz output spliced back into the existing call leg.
End-to-end Real-time Across our 30 production language pairs.
LAYER 02 · Language

Eighteen seconds in, Rosa clarifies what she meant. We don’t rewrite the past — we append a correction that’s timestamped, visible, and part of the record forever.

Append-only correction

We don't edit the past. We add to it.

Streaming translators get faster by guessing, and slower by walking the guesses back. TransVoix never rewrites what was said. When a later word changes the meaning of an earlier one, we issue a correction that's appended to the transcript, timestamped, and visible to both parties. Nothing disappears.

Live transcript · call_8f3e REC
00:14.220 Rosa
Necesito hablar sobre mi cuenta.
I need to talk about my account.
00:18.880 Rosa
…la cuenta de mi madre, en realidad.
…my mother's account, actually.
00:19.120 ↳ correction Correction · "my account" → "my mother's account"
00:23.410 Maya
Of course. Can you give me her account number?
Por supuesto. ¿Me puede dar el número de cuenta?
Language coverage

6 live · 8 next · 22 on the roadmap.

Voice-to-voice across 30 bidirectional pairs. Glossary support for industry-specific vocabulary. LLM-Judge benchmark: 4.72 / 5 average.

See all languages →
LAYER 03 · Trust

At 4:02, the call ends. The cloned voices are deleted from the inference pool. The transcript ships with a C2PA-signed manifest binding every word to its source.

Trust · provenance

Every word, signed.

Every translated utterance ships with a C2PA-signed manifest binding it to the speaker, the source audio hash, the model version, and the timestamp. If a recording shows up later in court, in compliance review, or in the press, you can prove what was said, who said it, and that nothing in between was altered.

✓ Verified · C2PA v1.4
{
"speaker_id": "agt_maya_8f3e" ,
"source_hash": "sha256:9c3a…b21f" ,
"model": "tvx-clone-v3.2" ,
"language": "en-US → es-MX" ,
"timestamp": "2026-04-27T14:22:08Z" ,
"corrections": 2 ,
"signed_by": "transvoix.ai"
}

What's in the manifest

Speaker identity binds the cloned voice to a verified agent or caller, not a free-floating impersonation.

Source hash is a fingerprint of the original audio — proof the translation is downstream of a real utterance, not synthesized from text.

Model version means the same input, verified six months later, produces the same output. Reproducible, not magic.

Corrections count tells you whether the line you're reading is the first take or a revision — and lets you replay the full append-only history.

LAYER 04 · Integration

We’ve been describing one call through Twilio. The pipeline is the same regardless of how the audio arrives — what changes is the adapter, not the model.

Integrations · where it plugs in

Six surfaces. One conversation.

TransVoix lives where your conversations already happen. The pipeline above is the same regardless of how a call enters or exits, what changes is the adapter, not the model. See full integration matrix on /for-business →

CCT
Contact Center
Genesys · Five9 · Talkdesk
TEL
Telephony
Twilio · Vonage · SIP trunks
AIV
AI Voice Agents
Vapi · Retell · custom LLM
CRM
CRM
Salesforce · HubSpot · Zendesk
EHR
EHR
Epic · Cerner · athenahealth
BRW
Browser
Chrome extension · WebRTC
Data handling · residency

Where your data lives, and how it leaves.

TransVoix processes voice in the region you choose and never trains on customer audio. You can also run the whole pipeline in your own VPC, the architecture is the same; only the operator changes.

Regional processing

US region today (us-east-1). Multi-region (EU, APAC) is on the architecture roadmap — when it ships, audio won't cross regions and cloned voice models will be pinned to the source region's keys.

today: us-east-1 · roadmap: eu · apac

Customer-controlled deployment

Roadmap: a customer-VPC deployment of the same pipeline so you keep the audio, the keys, and the audit log. Available today: tenant isolation via RLS and US data processing; for healthcare, we take your compliance team through the BAA and exactly what it covers.

deploy=managed · vpc=roadmap

See it on a real call.

Your own traffic, your own glossary, your own metrics. Scope, duration and success criteria are set with you on a scoping call.