Deepslate Raises €7.7M Seed Led by 42Cap for Native Speech-to-Speech AI

Deepslate Raises €7.7M Seed Round

Deepslate, a Berlin-based voice AI lab, has raised €7.7 million in Seed funding. The round was led by 42Cap, with Alstin Capital and SIVentures also participating.

What Deepslate Does

Deepslate trains its own native speech-to-speech model, Opal. Rather than chaining separate speech-to-text, language-model and text-to-speech stages — the common architecture behind many voice AI products — Opal processes audio directly. That native approach is the core technical differentiator the company is building around.

Model Capabilities

• Language coverage: 27 European languages
• Deployment: Runs entirely on EU infrastructure, with self-hosting options extending up to air-gapped deployments
• Latency: Recorded a 440 ms time-to-first-audio in the September 2026 Artificial Analysis benchmark — the fastest measured at the time

Latency and deployment control are two of the most common friction points in enterprise voice AI adoption. A 440 ms time-to-first-audio figure, measured in a third-party benchmark, speaks directly to conversational responsiveness, while EU-hosted and air-gapped options address data residency and security requirements that often block voice deployments in regulated sectors.

Go-to-Market

Deepslate sells through a self-service platform and API, alongside enterprise deployments. The company reports that its technology is used in production by insurers, contact centres and platforms — segments where voice interaction volume is high and where compliance and infrastructure requirements tend to be strict.

Deal Summary

• Company: Deepslate
• Round: Seed
• Amount: €7.7 million
• Lead investor: 42Cap
• Additional investors: Alstin Capital, SIVentures
• Founded: 2024
• Headquarters: Berlin, Germany
• Website: deepslate.eu

Why It Matters

Most voice AI products are assembled from a pipeline of separate models, which introduces latency and adds layers of infrastructure the buyer must trust with audio data. Deepslate's bet is that a single native speech-to-speech model — trained in-house, hosted in the EU, deployable down to air-gapped environments — is a better fit for European enterprises, particularly in regulated industries.

The combination of a self-service API motion and enterprise deployments gives Deepslate two routes into the market: developers and smaller teams can adopt through the platform, while larger organisations can run the model under their own infrastructure constraints. With production usage already reported among insurers, contact centres and platforms, the Seed round provides capital to push the model and its deployment options further into the European market.