Share what your AI learns.
Keep your data where it lives.
Package, sign and distribute approved knowledge updates between sites and jurisdictions, without pooling raw source data into a central repository. Every update is evaluated, approved and evidenced before it moves.
Deltas, not datasets
Only approved changes move. Source data stays in its origin zone
Promotion-gated
Nothing propagates unevaluated
Jurisdiction-bound
Residency rules are enforced at every node
How propagation stays governed
Delta packages, not raw data
Approved changes are serialized into versioned, cryptographically signed packages. Source data stays in its origin zone.
- Signed and versioned
- Verified on receipt before use
> Receiving delta package...
> Verifying signature...
> Integrity verified.
> Awaiting policy check.
Approval before propagation
Updates pass an evaluation gate and an administrator approval step before any node accepts them. Policy decides which nodes may receive what.
Residency enforced per zone
Restrict propagation by data classification and jurisdiction, so an update only reaches the zones it is allowed to reach.
Contain, roll back, revoke
Every node keeps prior versions. A faulty update can be quarantined, rolled back or revoked before it spreads.
From one node's learning to every approved zone
- Step 1
Learn
A node produces an improvement, from new documents, corrections or feedback.
- Step 2
Evaluate
The update is tested against your acceptance criteria before it can move.
- Step 3
Sign & package
Approved changes are versioned and signed as a delta package.
- Step 4
Propagate
Policy decides which zones receive it; delivery is logged.
- Step 5
Verify or roll back
Receiving nodes verify before use; any node can roll back or quarantine.
Runs where your AI already runs
Private, air-gapped or hybrid
Deploys into your VPC, on-premise environment or air-gapped site, alongside your existing models and retrieval stores.
Evidence your regulators recognize
Every package, approval and rollback is logged to an audit trail mapped to MAS TRM and PDPA obligations.
Ready to share learning without sharing data?
We are taking on a limited number of design-partner engagements in Singapore. Start with a fixed-scope design review of where your AI learning needs to travel, and where it must not.