Nicolai Baldin

Synthesized builds synthetic data generation software for financial services and enterprise organisations that need to share, test, or analyse sensitive data without exposing real customer information. The London company platform generates statistically representative synthetic datasets that preserve the analytical properties of production data while eliminating personally identifiable information, enabling data science, software testing, and regulatory compliance workflows to proceed without privacy risk.\n\nThe company raised approximately $12 million in a Series A round with investors including Lakestar and Playfair Capital. Synthesized counts financial institutions and data-intensive enterprises among its clients, deploying synthetic data capabilities for use cases including fraud model training, application testing with production-like data, and safe cross-border data sharing under GDPR and similar privacy regulations. The platform supports both tabular structured data synthesis and time-series data relevant to financial transaction modelling.\n\nSynthesized operates in the synthetic data market alongside Mostly AI, Gretel, and Tonic.ai, competing for data science teams seeking privacy-preserving alternatives to data anonymisation. The synthetic data market has expanded significantly as GDPR enforcement and financial regulatory requirements around data privacy have increased the cost and complexity of working with real customer data in development and analytics environments. The company financial services focus aligns with one of the highest-value segments for synthetic data adoption, where the sensitivity of transaction and identity data makes synthetic alternatives particularly valuable.