Our machine learning technology generates privacy-enhancing synthetic data that maintains the properties of real datasets. Replica Synthesis provides fast and effective access to high utility data while meeting regulatory obligations. Our methods include privacy assurance to ensure minimal privacy risks.
There is no one-to-one mapping between original and synthetic data, so data generated by Replica Synthesis is considered non-identifiable. Our solutions are scalable and will work with both large and small datasets.
We aim to make data synthesis and data sharing simple for end-users.
Query and integrate data from multiple data sources and generate synthetic variants of the cohorts.
The software is built for small and large structured data and can handle complex datasets.
Automate a synthesis report to describe data, methodology, synthesis and utility results, and any limitations.
Re-run analytics code on the original datasets.
SDKs support multiple data science and software engineering end-users.
Comprehensive REST API for integration with multiple and varied front ends.
Replica Synthesis is the future of data sharing and access. Our technology is innovative, trustworthy and privacy-enhancing.
Replica Synthesis is built to handle complex structured data to produce trustworthy, non-identifiable synthetic datasets.
Privacy assurance technology provides the evidence needed to demonstrate compliance with various legal regimes.
Through our Simulator Exchange, generative models created during the synthesis process can be saved as data simulators for data users to generate synthetic data on demand.
Replica Synthesis allows you to synthesize data anywhere with complete flexibility.