Synthetic Data Solutions

Privacy-Safe, Bias-Resistant Data for Robust AI Development

At Digital Bricks, we provide high-quality synthetic data generation services that enable safe, scalable AI development—especially when real-world data is limited, biased, or protected by strict privacy regulations.

We build synthetic datasets that mimic the structure, statistical properties, and behavioral patterns of your original data—without exposing sensitive information. This allows you to train, test, and validate AI systems confidently, even in complex, high-risk, or low-data environments.

AI systems can’t afford to rely on poor, incomplete, or restricted datasets. But in many cases, collecting or using real-world data isn’t feasible due to:

• Data scarcity in edge cases or new product domains
• Regulatory constraints (e.g. GDPR, HIPAA, FERPA)
• Bias risks in historical datasets
• Security concerns in production systems

Synthetic data offers a safe, scalable alternative—ensuring models are trained fairly, tested thoroughly, and deployed responsibly.

What We Do

We offer end-to-end synthetic data solutions tailored to your data structure, model goals, and risk profile.

1. Dataset Analysis & Target Definition

We begin by understanding the original dataset’s schema, statistical properties, and data types—structured, tabular, or sequential—defining what needs to be synthesized, retained, or excluded.

2. Synthetic Generation

We use a mix of techniques depending on data type and use case:

• Tabular data → GANs, VAEs, CTGAN, or rule-based generation
• Time series → Sequence models that retain temporal correlations
• Structured NLP → Language models trained on anonymized templates
• Scenario simulation → Event-based agent simulations for training AI under varied conditions

All outputs preserve schema fidelity, distributional similarity, and business logic constraints.

3. Privacy & Bias Evaluation

We validate synthetic datasets against original datasets using:

• Distance metrics (e.g. Jensen-Shannon, Earth Mover’s)
• Membership inference attack testing
• Bias and fairness audits based on protected attributes

4. Delivery & Integration

Datasets are delivered in AI-ready formats (CSV, Parquet, JSON), complete with:

• Synthetic vs real-world divergence reports
• Custom documentation for model integration
• Optional pipeline automation for future synthetic data refresh

Use Cases

• Training copilots or agents where real data is protected
• Testing LLMs or NLP systems in low-data languages or domains
• Generating edge-case scenarios for robustness testing
• Balancing datasets to remove historical bias

Why Digital Bricks?

We combine deep knowledge of AI training practices, data privacy engineering, and the Microsoft AI stack to help you build safer, smarter, and more equitable AI systems.

Whether you're testing at scale, addressing compliance gaps, or de-biasing a model, we build synthetic data that works—without compromise.