The local layer must exist.
Without opposing cloud models.

Cloud models do what they do well.

Very large artificial intelligence models, hosted in remote data centres, mobilise computational power few personal devices can match. They handle generic loads, production tasks, research, and large-scale generation. EVA-I disputes neither their usefulness nor their place.

But a layer is missing between the user and these models.

When a user addresses an artificial intelligence system, they do not do so as an anonymous request. They address it as a person, with their history, preferences, references, memory of previous interactions, and personal context — schedule, documents, correspondence, voice.

This information is, by nature, the most intimate. It is also what determines the real quality of the interaction. Without it, responses remain generic. With it, they become relevant.

This layer of identity and context should not travel to remote servers with every request, nor be aggregated into collective training datasets. It must remain the exclusive attribute of the user, on their device.

EVA-I designs this layer.

The company develops artificial intelligence solutions that run locally, on users' devices, relying on the local inference frameworks available on Apple Silicon platforms. Personalization is performed locally, memory is stored locally, models run locally.

When a call to a cloud model is useful, it is triggered explicitly by the user, from their local layer, to the cloud provider of their choice — without EVA-I interposing or observing the content of the exchange.

What follows operationally.

User conversations, documents and data are never transmitted to EVA-I.
No usage telemetry is collected by the distributed solutions.
No account is required to use these solutions.
The models used come from open, auditable ecosystems.
Personalization remains under the user's exclusive control.

This is not opposition.