The Model

End-to-End Voice AI.
As It Should Be.

End-to-End Voice AI.
As It Should Be.

Deepslate is a native speech-to-speech model. Audio in, audio out. No middleware.

How It Works

One Model. Zero Middleware.

Traditional voice AI stitches together three separate systems. Deepslate replaces the entire chain with a single model that thinks in audio.

Traditional Pipeline
3 models chained together
~800ms+
Audio In
Speech to Text
Transcription
Text
LLM
Text
Text to Speech
Synthesis
Audio Out
Why text as an intermediate fails Voice AI
Latency Stacking
Each conversion adds 200–300ms. Three models in sequence create delays that break natural conversation.
Emotion Lost
Text strips tone, sarcasm, and urgency. The AI responds to words, not meaning.
Error Compounding
Transcription errors with accents, names, and addresses compound through the chain.
Deepslate Model
Single native speech-to-speech
250ms
Audio In
Deepslate
Speech
Encoder
Embedding
LLM
Embedding
Speech
Decoder
Audio Out
Why embeddings unlock the full potential of Voice AI
Semantic Meaning
Embeddings capture full meaning directly from audio — no lossy transcription needed.
Prosody & Emotion
Rhythm, emphasis, intonation, and pauses preserved. The AI hears how something is said.
Zero Conversion Loss
One model, end-to-end. No error compounding, no latency stacking.

Performance

Numbers That Speak for Themselves.

We don't just claim to be better — we prove it. Here's how Deepslate performs against the industry's biggest names.

250ms
End-to-End Latency
From the moment your customer stops speaking to the moment they hear a response. Fast enough for truly natural, human-like conversations — no awkward pauses, no waiting.
Tau-Bench
Beats GPT 5.1 in Intelligence
On the Tau-Bench agentic benchmark, our model outperforms million-dollar models. It handles multi-step reasoning, verification, and information retention — natively, in audio.
CoVoST2
Outperforms Industry Benchmarks
Deepslate leads the CoVoST2 benchmark across European languages. Lower word error rates mean fewer misunderstandings in production — and less need for human fallback.
BIG-Bench
Superior Speech Reasoning
BIG-Bench Audio tests whether AI can reason directly from speech. While competitors lose up to 40% accuracy when switching from text to audio, Deepslate was built for audio-first reasoning.

How It Works

One Model. Zero Middleware.

Traditional voice AI stitches together three separate systems. Deepslate replaces the entire chain with a single model that thinks in audio.

Traditional Pipeline
3 models chained together
~800ms+
Audio In
Speech to Text
Transcription
Text
LLM
Text
Text to Speech
Synthesis
Audio Out
Why text as an intermediate fails Voice AI
Latency Stacking
Each conversion adds 200–300ms. Three models in sequence create delays that break natural conversation.
Emotion Lost
Text strips tone, sarcasm, and urgency. The AI responds to words, not meaning.
Error Compounding
Transcription errors with accents, names, and addresses compound through the chain.
Deepslate Model
Single native speech-to-speech
250ms
Audio In
Deepslate
Speech
Encoder
Embedding
LLM
Embedding
Speech
Decoder
Audio Out
Why embeddings unlock the full potential of Voice AI
Semantic Meaning
Embeddings capture full meaning directly from audio — no lossy transcription needed.
Prosody & Emotion
Rhythm, emphasis, intonation, and pauses preserved. The AI hears how something is said.
Zero Conversion Loss
One model, end-to-end. No error compounding, no latency stacking.

How It Works

One Model. Zero Middleware.

Traditional voice AI stitches together three separate systems. Deepslate replaces the entire chain with a single model that thinks in audio.

Traditional Pipeline
3 models chained together
~800ms+
Audio In
Speech to Text
Transcription
Text
LLM
Text
Text to Speech
Synthesis
Audio Out
Why text as an intermediate fails Voice AI
Latency Stacking
Each conversion adds 200–300ms. Three models in sequence create delays that break natural conversation.
Emotion Lost
Text strips tone, sarcasm, and urgency. The AI responds to words, not meaning.
Error Compounding
Transcription errors with accents, names, and addresses compound through the chain.
Deepslate Model
Single native speech-to-speech
250ms
Audio In
Deepslate
Speech
Encoder
Embedding
LLM
Embedding
Speech
Decoder
Audio Out
Why embeddings unlock the full potential of Voice AI
Semantic Meaning
Embeddings capture full meaning directly from audio — no lossy transcription needed.
Prosody & Emotion
Rhythm, emphasis, intonation, and pauses preserved. The AI hears how something is said.
Zero Conversion Loss
One model, end-to-end. No error compounding, no latency stacking.

Languages

27 Languages. European DNA.

US-trained models struggle with European names, addresses, and dialects. We trained Deepslate specifically for the way Europe actually speaks — across 27 languages, with deep focus on regional nuance.

European Focus

Deepslate doesn't just translate — it understands. Trained on European speech patterns, dialects, and cultural context to handle real conversations with real customers.

Names, Addresses, Emails

The details that matter most in business calls — and where most voice AIs fail. Deepslate handles European names, street addresses, and email spelling with precision.

Made in Germany. GDPR by Design.

Self-host on your own hardware or run on our EU-based cloud. Zero data leaves Europe. Zero third-party dependencies. Your customers' data stays exactly where it should.

Ready to Build the Future

of Voice AI?

If you have questions email us at info@deepslate.eu

© 2026 Deepslate. All rights reserved.

Ready to Build the Future

of Voice AI?

If you have questions email us at info@deepslate.eu

Ready to Build the Future

of Voice AI?

If you have questions email us at info@deepslate.eu

© 2026 Deepslate. All rights reserved.