Enterprise synthetic data collaboration
Your data. Further possibilities.
Give partners synthetic data to build with, a controlled workspace to work in, and a governed path to prove results.
Enterprise-controlled access. Purpose-specific datasets. Reviewed results.
Fictional demonstration
Turn these customer files into synthetic data so Lumen can build a churn model.
customers.csv, transactions.csv
I’ve looked through your files. Names and emails will be left out, and the link between customers and transactions is kept.
Create a synthetic data product
Needs your confirmation- 2,400 customers and 17,000 transactions
- Naledi Mokoena approves before anyone can use it
Create itCancel
Built around the value of your data.
Say what you need in plain words. The assistant does the preparation, people make the decisions, and the controls are enforced by the platform rather than the assistant.
Create once. Put it to work.
Attach a file and describe its purpose. The assistant leaves out personal details, builds a synthetic version and checks its quality, privacy and usefulness.
Invite expertise. Define the boundaries.
Ask the assistant to give a partner access. It proposes the strictest sensible settings; security approves before anything starts.
Prove the result. Review the release.
Partners test models on your real data without seeing it. Nothing leaves their workspace without your review.
A working asset, every day.
Every decision lands in one Inbox with a summary and a recommendation. Administrators see every assistant action, model and dataset in a dedicated governance view.
- Assistants act with each person’s own permissions
- Every change is confirmed by a person
- Approvals stay human, with separation of duties
- Real records are never sent to a language model
One dataset. One partner. One defined outcome.
A controlled pilot starts with your security configuration, not your production data.
