When is synthetic data preferable to redacting production rows?

Synthetic generation excels when volume, regulation, or air‑gap constraints prevent PII export. Synth‑Data pairs generators with parity checks against production slices.

Can you enforce differential privacy or cohort rules?

Yes. Privacy budgets, cohort caps, and governance milestones are first‑class in the delivery plan, not an afterthought.

How do you validate realism before models ship?

Bajpai Labs ships evaluation harnesses that compare synthetic cohorts to holdout real data on domain metrics before sign‑off.

Pilot flagship · Pilot available

Synth-Data (Synthetic Data)

Generate statistically faithful datasets when real records are scarce, toxic to move, or blocked by GDPR-class obligations, so teams train models without touching raw PII.

THE C‑SUITE HEADACHE

"We cannot share customer data with vendors, but our models starve without volume."

Privacy-safe dataset scalePrivacy-first trainingExpert data architecture

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Capabilities

High-fidelity synthetic datasets when real records are scarce, regulated, or toxic to move.

Differential privacy and cohort controls

Domain-specific generators (tabular, text, sensor)

Constraint engines for business-rule fidelity

Rare-event upsampling for long-tail defects

Evaluation harness vs. holdout real slices

Boutique data architect engagement

Use cases

Healthcare ML without PHI export

Defense-adjacent scenario generation

Insurance fraud pattern expansion

Fraud and AML model enrichment

Integrations

Fits ML pipelines, feature stores, and governance checkpoints you already enforce.

Feature stores (Feast, Tecton)Lakehouse catalogsMLflow / Vertex registrySagemaker PipelinesOn-prem air-gapped training clusters

Train models without touching the sensitive rows.

Synth-Data generates realistic distributions while Bajpai Labs architects parity with your edge cases.

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