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.
Synthetic data misses what matters. Manual copies are stale on arrival. Running experiments on production is a risk nobody should take.
Three ways teams handle this today. None of them work.
Manual dataset copies
By the time your experiment runs, the copy is stale. No audit trail. No reproducibility. Just yesterday's data.
Synthetic data
Safe, controlled, and wrong where it matters most. The edge cases your synthetic data skips are the ones your model encounters first.
Running on production
Everyone knows it's wrong. It continues because the alternatives are worse. One bad run corrupts records or leaves production broken for hours.
A live copy of production your AI can use freely.
Mirror continuously synchronises your production S3 environment into a governed parallel space. Fork a branch, run your experiment in isolation, then promote or discard. Production is never touched. Every action is logged automatically. Access controls, data residency, and audit trails carry over from production with no configuration.
Connect to your production storage
Point Mirror at your existing S3-compatible bucket. Synchronisation starts immediately. No migration, no restructuring, no changes to how production writes data.
Fork a branch to experiment
Instantaneous. The fork references live data without copying it. Storage overhead is zero until data in the branch actually diverges from Mirror.
Experiment in complete isolation
Your model or pipeline runs freely against the branch. Multiple experiments can run in parallel without interfering. Nothing it does is visible to production.
Promote or discard. Log is automatic.
Promote outputs through a governed workflow or discard the branch at zero cost. The complete record (data accessed, outputs produced, review decision) is written automatically.
What this makes possible.
Model retraining on current data
Retrain against today's production state. No manual exports, no production exposure. When a model misbehaves, the exact data state is preserved.
always current · no manual exportsShadow testing before deployment
Run new model versions against Mirror in parallel. Compare outputs. Promote only when behaviour matches expectations.
parallel validation · zero production riskRegulated AI validation
Current, isolated data with a complete audit trail. Satisfies regulator requirements without touching production.
HIPAA · EU AI Act · SOC 2Reproducible research
Every branch preserves the exact data state. Any collaborator can reproduce the same results, regardless of how much production has changed since.
version-locked · fully reproducibleThe cost your team isn't tracking
Most teams are running three hidden costs: staging infrastructure nobody trusts, pipelines that exist only to prepare experiment data, and the fallout from experiments that shouldn't have touched production. Mirror eliminates all three.