AI & ML Infrastructure

Training data that's already in the right format

Token components map directly to model input features — no separate preprocessing pass. Federated training across organisations exchanges tokenised residuals, not raw datasets.

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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

5×

Faster data preparation

Structure already captured

−90%

Data movement in training

Residuals, not raw datasets

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 tokens, sensitive content isolated in the residual.

Transmit

Only residuals train the model

Raw datasets stay on-site. Only tokenised residuals are exchanged across organisations. Model inputs are generated without raw data ever moving.

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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