Specialize models.Connect learning.

Physical AI needs models suited to real-world tasks and ways to learn from distributed experience. NexuML and NexuFL address these two independent challenges.

Two technologies. Independent by design.

NexuML

Build specialized AI.

Reusable machine-learning components for centralized training, faster experimentation and application-specific model development.

Centralized learning

Pool raw observations where data access and permissions allow.

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NexuFL

Enable collaborative learning.

Federated training and aggregation, with research into local adaptation and personalized learning.

Federated learning

Raw data stays local; compatible models train at each site. Evaluate the returned model locally.

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Follow the model information

What moves. What stays.

Select a phase

Raw data stays inside each machine

CoordinatorModel information only

01 / Local experience

Experience stays where it happens.

Each system collects observations from its own operating conditions.

Local experience

Each system collects observations from its own operating conditions.

Local training

Each participating system trains a compatible model on its own data.

Model exchange

Participating systems send model information—not raw measurements—to the coordinator.

Aggregate & distribute

The coordinator combines compatible updates and returns a shared candidate for local evaluation.

Personalize locally

Each system can adapt the candidate and compare it with a local baseline before deciding whether to use it.

Practical boundaries

Agree on the boundaries.

Compatible tasks. Local decisions.

Agree on objectives, model architecture and feature representations. Different machine types do not automatically share an interchangeable model.

Evaluate every candidate against local baselines before adoption; personalization and continual learning need checks for drift and forgetting.

Local data is not a privacy guarantee.

Model updates can reveal information despite local raw data. Design client authentication, transport and access controls, update protection and a threat model explicitly.

Secure aggregation and differential privacy are not automatically included. Agree any diagnostic transfers and retention separately.

Use suitable on-site compute.

Local execution and training can use existing industrial computers or on-site infrastructure, subject to compute and integration requirements.

Bring a real-world learning task.

Explore the application directions or discuss the first industrial validation path.