K2view Synthetic Data Generation Tool

01 AI-Powered Generation 
02 Rules-Based Generation
03 Data Cloning
04 Intelligent Data Masking

01AI-Powered Generation 

Trains a model on your masked production data. Best for production-like volume and statistical accuracy.

Extract a relevant subset of your production data for model training
Train the model and generate the synthetic data
Apply post-generation business rules to sharpen accuracy and fidelity

Synthetic data generation Tools: How GenAI generates accurate and compliant data

02Rules-Based Generation

Generates specific datasets from defined rules. Best when production data does not exist, or for exact edge and negative testing.

Generate valid records from rules alone.
Configure parameter-based rules without code
Let testers set parameter values for their own scenarios

Synthetic data generation tools: Rule-based generation is most useful for new functionality and negative testing

03Data Cloning

Replicates an entity's dataset at volume. Best for scaling fast for load and performance testing.

Identify any business entity using business rules
Mass-clone it across systems, instantly and securely
Generate unique IDs per clone to preserve referential integrity

Synthetic data generation tools: Data cloning rapidly generates compliant data for high-volume load testing

04Intelligent Data Masking

Replaces sensitive values with realistic synthetic ones. Best when real data works but must be made compliant.

Automatically discover and classify PII in structured and unstructured sources
Apply pre-built masking functions to comply with privacy regulations
Maintain referential integrity of the masked data across systems

Synthetic data generation tools: Data masking replaces sensitive data with fake but realistic data

Entity-based, not column-based

The entity data model is the blueprint for generation, so synthetic data keeps its relationships and referential integrity across every system.

Every generation method, one platform

AI, rules, cloning, and masking in one product, blended per scenario instead of forced into a single method.

No ETL, end to end

One platform handles everything around generation, prep and masking before, provisioning and refresh after, all compliant, no ETL tool required.

From test case to synthetic data, autonomously.

A K2view data agent reads your test cases and determines the test data required for full coverage. The agent then selects and configures the right generation methods, executes them , and provisions the synthetic entity data to the downstream environment.

The result: one complete, compliant dataset, with referential integrity, ready for your test cases.