Smart Homes & IoT — ELOQUENCE
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Voice AI that learns
without watching.

ELOQUENCE brings federated learning to smart home voice assistants — models that improve with every interaction, without a single byte of raw user data ever leaving the device.

Request a smart home demo → Try the Playground
0
Bytes of raw user data leave the device
35+
EU languages for voice interaction
FL
Federated Learning — on-device model training
GDPR
Privacy by architecture, not by policy
The challenge

Smart home AI has a
privacy problem.

Voice assistants in the home are collecting intimate data — conversations, routines, preferences. Users are increasingly aware of this, and regulators are catching up. The choice between a capable AI and a private home shouldn't exist.

🎙️
Audio streams sent to the cloudTraditional voice AI sends recordings to central servers for processing — exposing intimate home conversations.
🔒
GDPR exposureCentralised data collection from home environments creates significant compliance risk for platform operators.
🌍
Poor performance in minority languagesRegional languages and accents are underrepresented in training data — leading to worse performance for millions of EU users.
📡
Centralised single point of failureIf the cloud goes down or is breached, the entire assistant ecosystem is compromised.
The ELOQUENCE solution
Federated Learning
Models train on-device — only parameter updates are shared, never raw data
Each device learns from local interactions. The aggregation server only ever sees model weight updates.
Full audio pipeline
Keyword spotting, ASR, diarization — all on-device or encrypted
Complete voice processing stack without centralised audio storage. Works for Telefónica AURA and similar platforms.
Multilingual voice
Native support for 35+ EU languages including minority languages
Slovak, Catalan, Basque, Welsh — users interact in their home language without quality degradation.
Collective improvement
The more devices, the better the model — with zero privacy cost
Federated aggregation means every device benefits from the collective learning of the entire fleet.
How federated learning works

Better AI. Zero data exposure.

Federated learning is the key innovation that makes privacy-preserving voice AI possible at scale. Here's how ELOQUENCE implements it.

01
Local interaction
User interacts with the voice assistant. Audio is processed entirely on-device — never sent anywhere.
02
On-device learning
The local model updates based on the interaction. Only the model parameters change — not the data itself.
03
Encrypted update shared
Encrypted parameter updates — not audio, not transcripts — are sent to the aggregation server.
04
Global model improves
The aggregated model gets better for everyone. The improved model is pushed back to all devices.
🔐
What never leaves your deviceRaw audio recordings, voice transcripts, user commands, home routines, personal preferences — none of it.
📤
What does leave your deviceEncrypted model parameter updates only — mathematical abstractions with no personally identifiable information.
Outcomes

Built and validated
with Telefónica.

ELOQUENCE's smart home pilot was led by Telefónica Innovación Digital — the team behind AURA, one of Europe's largest smart home AI platforms — specifically designed for privacy-preserving deployment at scale.

0
Raw data exposure
By design — the federated architecture makes centralised data collection structurally impossible, not just policy-restricted.
35+
EU languages for voice
Including minority and regional languages where mainstream voice assistants deliver poor or no support.
AURA
Validated on Telefónica's platform
Tested in a real smart home environment with one of Europe's largest telecoms operators — not just in a lab.

Build voice AI your
users can actually trust.

Talk to us about deploying ELOQUENCE's federated learning framework in your smart home or IoT platform — we'll show you how it works in practice.

Request a smart home demo →
Privacy architecture walkthrough — exactly what stays on-device and what doesn't.
Platform integration discussion — how ELOQUENCE maps to your existing IoT stack.
GDPR compliance review — we'll walk through how the architecture satisfies regulatory requirements.
48-hour response — from the team that built the system.