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Capability — Privacy-Preserving AI
AI that improves
AI that improves
without watching you.
Federated learning lets ELOQUENCE train on real-world data without raw data ever leaving the device. Better models. Zero exposure.
0
Raw data bytes leave the device
FL
Federated Learning — on-device training
GDPR
Compliant by architecture
How it works
Privacy built in.
Privacy built in.
Not bolted on.
Most AI privacy is a policy document. Ours is an architecture. Federated learning makes centralised data collection structurally impossible — not just restricted.
01
Interaction happens on-deviceAudio, text, commands — processed locally. Nothing sent to the cloud.
02
Local model updatesThe device learns from the interaction. Only the model weights change — not the data.
03
Encrypted parameters sharedMathematical weight updates — not audio, not transcripts — are sent to the aggregation server.
04
Global model improvesAll devices benefit from collective learning. Better AI for everyone, at zero privacy cost.
What stays on device
Everything personal
Raw audio, voice transcripts, commands, routines, preferences — never leaves your infrastructure.
What leaves the device
Encrypted model updates only
Abstract mathematical parameters — no personally identifiable information, no reconstruction possible.
Compliance
GDPR & EU AI Act aligned
Privacy by design satisfies GDPR Article 25. Architecture documentation supports EU AI Act compliance.
Validated with
Telefónica AURA — at scale
Tested in a real smart home environment with one of Europe's largest telecoms operators.
Where it applies.
Smart home & IoT
Voice assistants that learn from home interactions without recording them.
Healthcare
Clinical AI that improves from patient interactions without exposing patient data.
Enterprise
Customer service AI trained on real conversations without storing them centrally.
Build AI your users can trust.
Talk to us about deploying federated learning in your platform — we'll show you exactly how it works.