On-premise speech AI · STT + TTS

Own your voice stack. Tuned to the language your cloud vendor can't handle.

Live in 3 weeks. You also get the retraining pipeline, so accuracy keeps improving on your own data.

Source + weights
yours permanently, no license expiry
No data leaves
runs inside your infrastructure
Tuned to you
your dialect, jargon, and audio conditions
In production for a national drive-thru chain, a medical-documentation vendor, Gulf enterprises, and others.
The problem

Three ways cloud voice works against you at scale.

01

Your data leaves your walls

Every call and every dictation is streamed to a third party you don't control. In regulated or air-gapped environments, that's a non-starter.

02

Generic models fail your language

Cloud models are trained for the average case. They break on Gulf dialects, code-switching, phone lines and domain vocabulary — exactly where you operate.

03

Cost scales with every minute

Per-minute billing grows with your success and never becomes forecastable. You rent forever and own nothing at the end.

Hear it yourself

Hear it in your language — and your dialect.

Thirteen Arabic dialects reading a customer-service script, and six English voices reading a longer passage per gender. Judge it as a native speaker would.

Language
More languages on request
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Built for the messy, low-resource audio that breaks generic models.

Gulf dialects. Arabic–English code-switching. 8 kHz phone lines. Overlapping speakers and background noise. Clinical and technical vocabulary. We collect and curate the data, fine-tune, and optimize inference until the quality is obvious to a native speaker.

Per-scenario numbers come from the test we run on your audio — not from a marketing page.

Word error rate — noisy drive-thru STT
Illustrative optimization result · lower is better
11.8%
before
→
8.6%
after

Method: TensorRT inference, streaming-pipeline optimization, custom vocabulary, VAD tuning. An illustrative delta from a production project — your figures get measured on your data.

Selected work

Three problems we've already solved in production.

Illustrative results from delivered projects. Your numbers get measured on your data.

A NATIONAL DRIVE-THRU CHAIN

Noise-robust drive-thru STT

PROBLEM
Millions of orders in noise, with overlapping speech and menu-specific vocabulary.
APPROACH
Optimized the full streaming pipeline: TensorRT, custom vocabulary, VAD tuning, dynamic batching.
Streaming latency~420 → ~180 ms
Streams / L40 GPU28 → 70
WER (noisy)11.8% → 8.6%
GPU mem / stream−35%
A MEDICAL-DOCUMENTATION VENDOR

Streaming medical dictation

PROBLEM
Real-time dictation on customer infrastructure, where latency and accuracy both matter.
APPROACH
Medical fine-tuning, custom lexicons, decoder optimization, deployment tuning across hardware profiles.
Streaming latency~320 → ~150 ms
Medical WER9.2% → 5.8%
Throughput2.4×
Timestamp error< 40 ms
A GULF ENTERPRISE

Custom Najdi / Hijazi Arabic TTS

PROBLEM
Commercial TTS reached only moderate quality for the target Gulf dialects.
APPROACH
Benchmarked architectures, curated additional speech data, fine-tuned and optimized inference to target quality.
MOS3.7 → 4.5
Synthesis latency1.9 → 0.8 s
Model size3.2 → 1.6 GB
Reference architecture

Everything stays inside your perimeter.

Audio enters, runs on your private GPU workers, and returns. No third-party API call, no egress.

CUSTOMER PERIMETER · AIR-GAPPED Callers · phone lines · apps Ingress VAD · routing STT workers private GPU TTS workers private GPU Your apps EHR · CRM No third-party API · no egress transcripts & synthesized audio returned to your systems Source + model weights deployed on your hardware — owned outright
Delivery model

What you receive, and how it lands.

A maturity path you can stop at any point — you own everything at each step, and nothing here creates a dependency on us. Live in about three weeks.

01Deploy

Models running on-prem inside your infrastructure. Source and weights owned from day one.

02Tune

Fine-tuned to your audio, dialect and domain — the accuracy jump generic models can't reach.

03Self-improving

A retraining pipeline, handed to you, that learns from your production data so accuracy climbs instead of decaying.

You own & run it. Ongoing support optional — never required.
Handed over
Streaming STT + TTS models
Fine-tuned to your audio, built for live calls, with voice cloning from short reference audio — no sample ever leaves your walls.
Source code + model weights
Yours permanently. Nothing expires, no check-ins with our servers.
Dockerized services
Deployed and running inside your own infrastructure, air-gapped if needed.
Retraining pipeline
The MLOps loop from Phase 3 — yours to run, so accuracy climbs on your production data.
Cost model

Estimate your break-even

Adjust the inputs to your figures. Compute is included; real numbers come from the test on your data.

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See the numbers on your own audio before you commit.

Two ways to start. Both put you in front of the people who build the models.

Primary

Test our models on your data

Send a short brief and a sample. We'll test on your held-out data and show you what it does.

✓ Thank you — we'll reply from an engineering lead, not a sales inbox.
What would you like to test?
Secondary

Talk to the team building it

You'll speak directly with our engineering and research leads.

Scheduling
Pick a time that works for you — synced to our team's real availability.
Book a 15-minute call