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Berlin-based Deepslate has raised €7.7 million in seed financing to develop speech-to-speech AI models aimed at European languages and enterprise voice applications. The round was led by 42CAP, with participation from Alstin Capital, existing investor SIVentures and several business angels, according to Tech.eu on October 1, 2026.

What does Deepslate do?

Deepslate develops speech-to-speech models: the system receives spoken audio and generates spoken audio directly. The company offers access through an API and developer integrations, positioning the technology for platforms and enterprise voice agents rather than as a standalone consumer assistant.

For platform customers, Deepslate says the model can operate inside the customer’s orchestration layer. That arrangement is intended to let the platform retain its own prompts, retrieval-augmented generation (RAG) pipeline and tool calling rather than handing those parts of the application to Deepslate. Its platforms page describes this approach.

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, Tech.eu reported on October 1, 2026. Co-founder Jan Brachthäuser announced the round with the same principal investors in a LinkedIn post.

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The reported information does not establish the company’s valuation, investor dilution, detailed financing terms or the identities of all participating angels.

How will Deepslate use the €7.7 million?

Deepslate says it will put the funding toward four areas:

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  • Further development of its speech models.
  • Expanding training data for European languages.
  • Growing its sales and marketing team.
  • Building infrastructure in European data centres.

The plan connects the company’s language focus with its enterprise deployment pitch: Deepslate says customers can choose EU-based cloud hosting or self-hosting, while the company invests in its own models and European training data.

What is speech-to-speech voice AI?

A speech-to-speech system takes audio as input and produces audio as output. That differs from a pipeline that first converts speech to text, sends text to a language model, then converts the response back to speech. Deepslate’s materials describe its model as handling the audio-to-audio interaction, while allowing platform clients to keep their own orchestration and tools.

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The distinction matters for conversational products because voice systems need to handle timing, pauses and interruptions as well as the content of a reply. The label alone, however, does not establish how well a particular model performs in a given application; that depends on the model, benchmark, language and deployment conditions.

What does Deepslate offer developers and enterprises?

Deepslate’s product site, accessed October 3, 2026, lists support for 27 languages and describes its model, Opal, as intended for European speech, including accents, names, addresses and dialects. These are company product claims, not independent evaluations of language quality.

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The company lists WebSocket, WebRTC and SIP options, Python and Node SDKs, and plugins for LiveKit and Pipecat. It also describes EU cloud hosting and enterprise self-hosting. Organizations assessing those options should confirm deployment, data handling, retention, security and service terms directly with Deepslate; the listed options alone do not establish specific compliance guarantees.

Deepslate says its technology is already in production with insurers, contact centres and platforms. The company and co-founder have made that claim, but the material reviewed does not name customers or provide independently measured outcomes.

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What do the published performance figures show?

Deepslate’s website presents Artificial Analysis benchmark data for September 2026. The figures below are as presented by Deepslate; they have not been independently reproduced here, and the underlying benchmark methodology and test conditions are not established by the cited company page.

Measure Deepslate Opal Selected models listed by Deepslate
Time to first audio 440 ms 960–1,340 ms for selected comparison models
Full Duplex Bench 85.7% GPT-Realtime-2.1 (Minimal): 92.7%; Gemini 3.1 Flash Live variants: 74.3% and 72.3%
BIG-Bench Audio 85% GPT-Realtime-2.1 (Minimal): 87%; GPT-Realtime-1.5: 81%; Gemini 3.1 Flash Live (Minimal): 71%

The numbers suggest that Opal is not ahead on every listed measure: the cited GPT-Realtime-2.1 (Minimal) scores higher on both Full Duplex Bench and BIG-Bench Audio. A useful model comparison would need the benchmark definitions, versions, settings and language coverage, not just headline percentages or a latency figure.

What does Deepslate charge?

As displayed on its product site on October 3, 2026, Deepslate lists a limited-time release offer of €0.02 per minute and a stated regular price of €0.10 per minute. The company says the offer can change for new sign-ups. These are listed prices, not a confirmed quote for every enterprise deployment; prospective customers should verify current rates, volume terms, concurrency, service levels and support with the company.

What remains unknown about the funding and product?

The announced round establishes the amount and participating investors, but not valuation or detailed terms. On the product side, Deepslate’s stated performance figures and production deployments remain company-attributed in the materials cited here: named customer references, customer outcomes and independently reproduced benchmark results are not provided. That makes the funding announcement evidence of investment in the company’s European voice-AI strategy, rather than proof that its models outperform alternatives across workloads.

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