Syntho for Web

Product snapshot — Syntho

Syntho is a browser-based platform for creating synthetic datasets that preserve the statistical characteristics of original data while protecting sensitive information. It helps teams produce realistic, privacy-preserving data for development, testing, and analytics without exposing real personally identifiable information (PII).

Privacy-focused dataset creation

• Recreates the statistical relationships present in source datasets so synthetic records behave like production data.
• Masks or substitutes sensitive identifiers to prevent exposure of PII while keeping analytical value.
• Enables rule-driven generation so produced datasets follow custom constraints and business logic.

Control over test data

• Fine-grained options let users tailor synthetic output to match schema, distributions, and dependency rules.
• Templates and configurable parameters speed up the creation of test datasets for different environments or scenarios.
• Export and format choices support downstream use in staging systems and development workflows.

Automated PII detection and masking

• An AI-powered scanner locates likely PII fields and applies de-identification techniques automatically.
• Configurable masking strategies allow teams to choose how data is obfuscated or replaced.
• Auditing capabilities show which fields were modified and why, supporting compliance needs.

Quality checks and validation

• Built-in metrics measure fidelity, statistical similarity, and generation performance to assess dataset quality.
• Options for third-party validation let organizations independently verify how well synthetic data matches production characteristics.
• Performance reporting provides insights on generation speed and resource usage.

Integration, deployment, and connectivity

• Assistance with deploying the platform in local, cloud, or hybrid environments is available.
• Prebuilt connectors make it easier to pull source data and push synthetic output to target systems.
• Support for multiple data types and formats ensures compatibility with common data stacks.

Onboarding resources and demonstrations

• Comprehensive documentation covers setup, best practices, and configuration examples.
• Live demonstrations and sample projects help new users evaluate capabilities quickly.
• Support channels and professional services can supplement in-house expertise during rollout.

Typical industries and use cases

• Particularly useful where privacy and compliance are critical, such as healthcare and financial services.
• Valuable for product testing, analytics model development, and training machine learning systems without exposing real customer data.

Notes on licensing and options

• The platform is offered with paid tiers; organizations can choose plans that match scale and support needs.
• Alternatives and complementary tools exist for teams seeking different pricing, feature sets, or deployment models.