About
Synthesized is a comprehensive Test Data Management (TDM) platform built for data-driven enterprises. It leverages generative AI to create high-fidelity, production-like datasets on demand — eliminating the delays and compliance risks associated with using real production data in lower environments. At its core, Synthesized supports three primary workflows: **data generation**, **data masking**, and **data subsetting**. Engineers specify data requirements using LLM-assisted YAML configurations or Python DSL, then run automated jobs as part of CI/CD or data pipelines to deliver compliant, realistic data directly to the destination system. The platform's 'Data as Code' methodology allows teams to codify complex regulatory and compliance requirements into reusable data transformation policies. Masking rules encode standards like GDPR and HIPAA directly into the pipeline, drastically reducing exposure to legal and breach risk. Synthesized integrates natively with major enterprise databases — including SAP HANA, Oracle, PostgreSQL, MySQL, DB2, and SQL Server — and supports applications like SAP S/4HANA, Oracle Fusion, Workday, Microsoft D365, and ServiceNow. Its cloud-native GenAI engine enables database generation, intelligent masking, and subsetting at scale, reportedly saving over 70% in cost per application dev and test lifecycle. Ideal for DevOps teams, QA engineers, database administrators, and compliance officers in regulated industries who need continuous, safe, and realistic data availability for development, testing, and agentic AI workflows.
Key Features
- AI-Powered Test Data Generation: Use LLM-assisted YAML configs or Python DSL to generate high-fidelity, production-realistic datasets on demand for any development or QA use case.
- Intelligent Data Masking: Encode compliance rules (GDPR, HIPAA, etc.) into reusable masking policies to protect sensitive data while keeping test data realistic and usable.
- Data Subsetting: Provide teams with targeted, role-specific subsets of data — not the entire database — balancing usability with security and relevance.
- CI/CD & Pipeline Integration: Run data provisioning jobs natively within CI/CD pipelines or data workflows, ensuring test data is always current and available during automated build and test cycles.
- 'Data as Code' Compliance: Codify complex regulatory requirements into version-controlled data transformation configurations, ensuring test environments remain compliant and auditable at all times.
Use Cases
- A bank's QA team needs production-realistic transaction data for regression testing without exposing real customer PII — Synthesized generates masked, compliant datasets automatically.
- A software team integrates Synthesized into their CI/CD pipeline so every pull request test run has fresh, realistic database snapshots available without manual data prep.
- A healthcare company codifies HIPAA masking rules into Synthesized policies to ensure all lower-environment data is automatically de-identified before it reaches developers.
- An enterprise running SAP S/4HANA uses Synthesized to subset and provision relevant data slices for individual feature teams, preventing over-exposure of the full production schema.
- A fintech startup accelerates its development cycle by using Synthesized to generate synthetic financial datasets that mirror production complexity, enabling realistic load and integration testing.
Pros
- Massive Cost & Time Savings: Reported savings of over 70% per application dev and test lifecycle by automating data provisioning that previously took months.
- Enterprise Database & App Coverage: Supports a wide range of enterprise databases (Oracle, SAP HANA, PostgreSQL, MySQL, DB2, SQL Server) and business applications (SAP, Workday, Salesforce, D365).
- Built-in Compliance Automation: Regulatory rules are codified directly into masking pipelines, reducing manual compliance effort and the risk of data breaches in non-production environments.
- Self-Service & Developer-Friendly: YAML-based configurations and native UI enable engineers and testers to get realistic data without waiting on DBA or ops teams.
Cons
- Enterprise Pricing with No Public Tiers: Pricing is not publicly listed and requires contacting sales, making it inaccessible or opaque for smaller teams or individuals evaluating the tool.
- Complex Initial Setup: Integrating with existing enterprise databases, pipelines, and compliance frameworks can require significant upfront configuration and onboarding time.
- Primarily Targeted at Large Organizations: The platform's feature set and pricing model are optimized for enterprises, making it potentially over-engineered for smaller startups or individual developers.
Frequently Asked Questions
What is Synthesized used for?
Synthesized is used to automatically generate, mask, and provision production-like test data for software development, QA testing, and agentic AI workflows — without using real sensitive production data.
Which databases does Synthesized support?
Synthesized supports SAP HANA, DB2, MySQL, Oracle, PostgreSQL, and SQL Server, among others. It also integrates with enterprise applications like SAP S/4HANA, Oracle Fusion, Workday, Microsoft D365, and ServiceNow.
How does Synthesized ensure data compliance?
Synthesized uses a 'Data as Code' approach where compliance rules (e.g., GDPR, HIPAA) are codified into masking policies. These policies are applied automatically during data provisioning, ensuring all test data meets regulatory standards.
Can Synthesized integrate with CI/CD pipelines?
Yes. Synthesized is designed to be cloud-native and integrates natively with CI/CD pipelines and data workflows, enabling automated, on-demand test data provisioning as part of every build or deployment cycle.
How is test data specified in Synthesized?
Data requirements are specified using LLM-assisted YAML configuration files for databases or Python DSL for datasets. The platform's built-in LLM helps streamline writing these configurations, reducing manual effort.
