Synthetic Data Generation Tools

Generate and manage complete, accurate, and compliant
synthetic data for testing software and training AI/ML models.

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Synthetic data generation tools: patented approach

Industry Patent

Synthetic data generation tools: Generative AI

Generative AI

Synthetic data generation tools: Rules engine

Rules Engine

Synthetic data generation tools: Entity cloning

Entity Cloning

Synthetic data generation tools: Data masking

Data Masking

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Synthetic Data Generation Tools

Balanced, realistic synthetic data


All methods

of data generation

Supporting the 4 key synthetic
data generation techniques


Any use case

with 1 set of tools

Testing apps, training AI/ML models, sharing B2B data, and much more



portal and APIs

Empowering data teams with
full control and automation

Generating synthetic data by business entities

Our patented approach makes all the difference

Model business entities
Model your business entities

Auto-discover the business entity schemas (e.g., customer, device, loan, order, etc.)  for which the synthetic data is needed.

Business entity model
Generate the synthetic data

Apply the appropriate data generation method(s) to the data model, to create the most complete, accurate, and compliant synthetic data possible.

synthetic data delivery tools
Deliver and manage

Deliver the data to the target systems and manage access, reservation, versioning, rollback, and integration with CI/CD and ML pipelines.

Combining all 4 data generation methods

  • 01 Generative AI
  • 02 Rules Engine
  • 03 Entity Cloning
  • 04 Data Masking

01Generative AI

Generative AI is used when there's not enough production data to:

  • Subset the source data needed to train the model
  • Mask the training data to ensure compliance 
  • Train the GPT model to generate the synthetic data
  • Apply business rules to increase accuracy
Synthetic data generation tools - generative AI

02Rules Engine

Rules engines, used for testing new application functionality, must:

  • Generate data based on pre-defined business rules – on demand or via API
  • Create business entities, such as customers, automatically
  • Customize, test, and debug functions, without coding
  • Define business rule parameters
Synthetic data generation tools - rules engine

03Entity Cloning

Entity cloning is used for performance and load testing to:

  • Generate massive datasets on demand
  • Select the most relevant business entity (e.g., a customer with the right criteria for a particular test case)
  • Extract, mask, and clone the entity along with all its data
  • Create unique identifiers for every cloned entity
Synthetic data generation tools - data cloning

04Data Masking

Data masking, which obscures sensitive data, must:

  • Anonymize sensitive data in a very lifelike way
  • Discover Personally Identifiable Information (PII) automatically
  • Customize data masking functions
  • Mask data inflight, as it’s extracted from the underlying source systems
Synthetic data generation tools - data masking
Product tour for synthetic data generation tools


Generate accurate, safe synthetic data

Take a guided tour of K2view Synthetic Data Generation tools:

  • Generate realistic datasets:
    Perfect for software testing and ML model training
  • Ensure data privacy:
    Compliant datasets, free from PII
  • Accelerate innovation:
    Precise synthetic data you need, at an instant 
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Unlock the power of synthetic data

Learn from analyst firm IDC about synthetic data generation tools, methods, best practices, strategies, and how to apply them to software testing and ML model training

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Learn more about
synthetic data generation tools

Synthetic Data Generation


The A-Z of Synthetic Data Generation

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gartner SDG

Gartner Report

Gartner Report on Test Data Management

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Gartner report Data Masking

Gartner report

Market Guide for Data Masking

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