AI and Analytics

Compute-to-data supports workloads that benefit from local access to protected records.

Inference

Run an approved model inside clinical, industrial or research environments and return only permitted predictions or aggregates.

Training and fine-tuning

Train models where authorised data resides. Model checkpoints and metrics can be stored as governed artifacts without exporting the training records.

Distributed training

When a workload and network configure an aggregation protocol, multiple nodes can produce authorised model updates while retaining their local datasets.

Analytics and extraction

Produce statistics, features, derived datasets and reports under an explicit output policy.

Validation and transformation

Run compliance checks, quality assessment, schema conversion and enrichment with a verifiable execution trail.

AI outputs may be probabilistic or hardware-dependent. NOOSChain records their execution provenance; it does not describe arbitrary AI computation as deterministic smart-contract execution.