Your AI needs real data.
Production needs to stay untouched.
Mirror gives your AI a live, governed copy of production, always current, fully isolated, ready to experiment on.
Three ways teams handle this today. None of them work.
Manual dataset copies
Stale by the time your experiment runs. No audit trail, no reproducibility.
Synthetic data
Safe, controlled, and wrong where it matters. The edge cases it skips are the ones your model hits first.
Running on production
Everyone knows it's risky; the alternatives are just worse. One bad run corrupts records or takes production down.
Give your AI production data, not production risk.
Mirror keeps a live, governed copy of your production S3: always current, fully isolated, and automatically logged. Production is never touched, and access controls, data residency, and audit trails carry over.
Connect to your production storage
Point Mirror at your existing S3 bucket. Sync starts immediately. No migration, no changes to how production writes.
Fork a branch and experiment in isolation
Branch instantly, with zero storage until it diverges. Your model or pipeline runs against the fork in full isolation, invisible to production.
Promote or discard. Log is automatic.
Promote outputs through a governed workflow or discard at zero cost. The full record is written automatically.
What current data unlocks.
Model retraining on current data
Retrain against today's production state, not last month's export. No manual copies, no production exposure.
always current · no manual exportsRegulated AI validation
Validate AI against real, current data in an isolated environment, with an audit trail regulators accept. Production is never touched.
HIPAA · EU AI Act · SOC 2The cost your team isn't tracking
Three hidden costs most teams just absorb. Mirror eliminates all three.