Governable Embeddings
Named, composable operators applied at inference
A patented embedding space where semantic dimensions — sentiment, intent, bias — are accessible as named operators you can apply to any query or document vector. Shift a query toward positive sentiment before retrieval. Redirect a user's intent from 'complaint' to 'refund offer' for counterfactual retrieval. Subtract a gender dimension to measurably debias a CV-screening pipeline. Every intervention is a vector operation, auditable per call, at 100% flip accuracy on held-out test data across Zulu, Swahili, Xhosa.
Properties
What this core unit gives you
The guarantees this core unit provides when composed into a production system.
100% sentiment-flip accuracy on AfriSenti Zulu and Swahili
100% intent-redirect accuracy across 4 tested transitions
93–99% cross-lingual transfer from Zulu-trained operators
Every operator shift logged for audit + compliance
Composes With
The other core units it pairs with
Core units are decoupled by design. These are the ones we've validated in production compositions — but you can compose with your own stack too.
Used In
Where this core unit ships today
Use cases that include this core unit as part of the composition.
Compose Governable Embeddings into your stack.
Available via API, on-prem license, or as part of a composed packaged product. Talk to us about the right entry point.