ironshard.mirror, live sync

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.

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Production
/contracts/nda_acme.pdf
/datasets/q1/payroll.csv
/models/v4/weights.bin
/reports/audit_2025.json
/datasets/customers/mar.parquet
live sync
Mirror
/contracts/nda_acme.pdf
/datasets/q1/payroll.csv
/models/v4/weights.bin
/reports/audit_2025.json
/datasets/customers/mar.parquet
// the problem

Three ways teams handle this todayNone of them work

01

Manual dataset copies

Stale by the time your experiment runs. No audit trail, no reproducibility.

02

Synthetic data

Safe, controlled, and wrong where it matters. The edge cases it skips are the ones your model hits first.

03

Running on production

Everyone knows it's risky; the alternatives are just worse. One bad run corrupts records or takes production down.

// the solution

Give your AI production data, not production risk

Mirror keeps a live, governed fork of your production bucket: always current, fully isolated, and automatically logged. Production is never touched, and access controls, data residency, and audit trails carry over.

01

Enable Mirror on production

Turn Mirror on for your IronShard production bucket. The fork starts current and stays current, with no changes to how production writes.

always current
02

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.

zero-copy · fully isolated
03

Promote or discard. Log is automatic.

Promote outputs through a governed workflow or discard at zero cost. The full record is written automatically.

immutable audit trail
Branching, snapshots, and point-in-time recovery work on any IronShard bucket, native or mirrored. See how Branch works →
// use cases

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 exports
live data
/datasets/orders.parquet2m ago
/features/users.parquetcurrent
/events/stream.jsonlive
last sync: 4s ago

Regulated 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 2
audit trail
14:02accessed/clinical/*
14:09model runfraud-v7
14:15producedreport.json
14:20promoteda.chen ✓
// the business case

The cost your team isn't tracking

Three hidden costs most teams just absorb. Mirror eliminates all three.

staging infrastructure

Always stale. Costly to run. Trusted by no one.

data preparation overhead

Export, anonymise, re-import. A pipeline that exists only because nothing better did.

production incidents

One bad run on live data: hours of recovery and a call from the regulator.

// built for

Four teams who need this today

ML/AI engineering and agent teams

training & agents
Training runs and AI agents need today’s production state, but every refresh waits on an export-and-anonymise job.
Give models and agents a current, safe target on demand. No export jobs, no waiting, no production exposure.

Healthtech and clinical AI

regulated validation
Synthetic data misses the clinical edge cases that matter; testing on production exposes patient data.
Real, current clinical data in an isolated space, with an immutable record that satisfies HIPAA and the EU AI Act.

Fintech and insurance

shadow testing
Fraud, credit, and underwriting models can’t ship unproven, but testing on real money is a non-starter.
Shadow-test against live data at zero risk, plus the record that shows regulators what the model ran against.

Data and platform engineering

staging replacement
Staging is perpetually stale and costs more to maintain than it's worth.
Retire it. Mirror is continuously current, with no refresh schedule and nothing to drift.