AI & ML Infrastructure
Training data that's already in the right format
Token components map directly to model input features, not a separate preprocessing pass. Federated training across organisations exchanges tokenised residuals, not raw datasets.
Business Impact
What changes when raw data stops moving
Eliminate preprocessing pipelines for structured data
Train models across organisations without sharing raw data
Reduce data preparation time significantly
Keep sensitive training data on-site throughout
0
Preprocessing passes
Tokens are model-ready
0
Training loss gap
When labels follow the structured component
50 to 250x
Less data moved for training
Coefficient features only, measured on sensor streams
100%
Raw data stays on-site
Federated by construction
The challenge
Training data is scattered, sensitive, and never in the right format
Building ML models at scale requires large, diverse datasets. Raw training data is often sensitive, siloed across organisations, and requires significant preprocessing before it can be used. Moving it creates risk; leaving it in place limits model quality.
Approach
How Datasent enables this use case
Encode
Agree on the model.
Participating organisations establish a shared basis upfront. Training data is encoded against this basis: structure is captured as coefficients, the unpredictable part as the residual. Both are treated as private.
Transmit
Only residuals train the model
Raw datasets stay on-site. Organisations exchange tokens, not raw records, and the coefficient features feed training directly.
Reconstruct
Exact data when authorised
The basis is regenerated locally. Raw training examples can be recovered exactly when access is granted, for validation, auditing, or retraining.
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