Berlin-based Deepslate has raised €7.7 million in seed financing to develop its speech-to-speech AI platform, expand European-language training data and grow its commercial and infrastructure capacity. The round was led by 42CAP, with Alstin Capital, existing investor SIVentures and several business angels participating, according to Tech.eu on October 1, 2026.
What does Deepslate do?
Deepslate develops speech-to-speech models: the system takes spoken audio as input and generates spoken audio as output. The company offers access through an API and describes the product as suitable for integration into voice-agent platforms and other enterprise applications.
For platform customers, Deepslate says the model can run inside the customer’s orchestration layer. That setup can leave the customer in control of its prompts, retrieval-augmented generation pipeline and tool calling, rather than requiring those components to be handed over to a separate application. The company’s platforms page outlines this approach.
Integrations and deployment
Deepslate’s product materials list WebSocket and WebRTC, SIP, Python and Node SDKs, plus plugins for LiveKit and Pipecat. The company also describes EU cloud hosting and enterprise self-hosting as deployment options. These are company-listed capabilities, not an independent assessment of security, compliance or service performance.
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Deepslate markets its model for use in platforms, contact centres, insurers and other enterprise voice-agent deployments. Co-founder Jan Brachthäuser has said the company is already in production with insurers, contact centres and platforms. The statement is company-reported; the material available does not name customers or give independently measured outcomes. See Brachthäuser’s LinkedIn post.
Who invested in Deepslate?
The €7.7 million seed round was led by Munich-based technology investor 42CAP. Alstin Capital, existing investor SIVentures and several business angels also participated, according to Tech.eu and the company co-founder’s announcement. The sources do not establish the company’s valuation, investor-by-investor contributions, dilution or full list of angel investors.
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How will Deepslate use the €7.7 million?
Deepslate says it will use the funding to advance model research, expand its European training data, hire in sales and marketing, and build out infrastructure in European data centres. The combination reflects a product strategy that depends not only on model development but also on language coverage, enterprise sales and deployment capacity.
Why is Deepslate emphasizing European languages?
The company says it is training for European languages as well as the accents, names, addresses and dialects that can be important in real conversations. It lists support for 27 languages on its product site, accessed October 3, 2026. That figure is a company product claim; it does not by itself show how well the model performs in each language or dialect.
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Deepslate’s geographic pitch also includes EU-based hosting and self-hosting for enterprise customers. Those options may matter to organizations with data-residency or infrastructure requirements, but the available product descriptions do not establish specific retention practices, certifications or regulatory compliance.
What do Deepslate’s published performance figures show?
Deepslate’s product site presents benchmark figures for its Opal model, attributing the underlying benchmark data to Artificial Analysis. The figures below were presented by Deepslate in September 2026; they have not been independently reproduced here, and the underlying methodology and test conditions are not established by the cited company page.
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| Measure | Figures presented by Deepslate |
|---|---|
| Time to first audio | Opal: 440 ms. Selected comparison models: 960–1,340 ms. |
| Full Duplex Bench | Opal: 85.7%; GPT-Realtime-2.1 (Minimal): 92.7%; Gemini 3.1 Flash Live variants: 74.3% and 72.3%. |
| BIG-Bench Audio | Opal: 85%; GPT-Realtime-2.1 (Minimal): 87%; GPT-Realtime-1.5: 81%; Gemini 3.1 Flash Live (Minimal): 71%. |
The comparison is limited to the models and numbers displayed on Deepslate’s page. It does not establish a universal ranking: Opal is below some listed competitors on the two percentage-based measures, while the page reports a lower time-to-first-audio figure than its selected comparison models. A meaningful procurement comparison should check model versions, benchmark definitions, language coverage and test settings directly.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is speech-to-speech voice AI?
In a speech-to-speech system, spoken input is processed into spoken output without presenting the product as a chain of separate user-facing transcription, text-response and text-to-speech steps. That can be useful for voice applications where conversational timing, interruptions and natural turn-taking matter. It does not, on its own, prove lower latency or better conversation quality; those depend on the model, deployment and evaluation conditions.
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For a business assessing Deepslate, the useful questions extend beyond a headline benchmark: how the model handles interruption and pauses, how it performs in the specific languages and accents customers use, what data is retained, and what service levels and commercial terms apply. The published figures do not answer all of those questions.
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What remains unclear?
- The round’s valuation, dilution and detailed financing terms have not been disclosed in the cited coverage.
- The company has not named the production customers referenced by its co-founder or provided independent outcome data in the cited materials.
- Deepslate’s benchmark page and product descriptions are company sources; the figures and feature claims should be treated accordingly.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
