Scaling Data Generation with AI?
Agentic AI Generates Billions of Test Records Across 12 Simulations at 60% Lower Compute Cost
Case Study Summary
Executive summary
Whizzbridge built an agentic AI system for a software testing client that needed realistic, large-scale test data across complex simulations. Using Large Language Models, custom agent design, and iterative prompt engineering, task-specific agents read and link data via correlation IDs to guarantee precision at scale. The system generated billions of realistic test records across 12 distinct simulations while cutting compute costs by approximately 60% through cost-aware prompt design and optimization, giving the client a flexible foundation for future testing needs.
Billions
Test records generated
Across simulations
12
Distinct simulations
Analytic + numerical tests
~60%
Lower compute costs
Cost-aware prompt design
Challenges
Complex Data Requirements The client needed a system to generate realistic test data for large-scale simulations, supporting up to 12 distinct tests across billions of records.
Scalability and Cost Concerns Generating massive datasets while maintaining performance and controlling costs posed a significant challenge.
Generate large-scale, realistic test data for complex simulations, balancing scalability, performance, and cost-effectiveness.
WhizzBridge’s Solution
Task-Specific Agent Design Custom agents were developed to read and link data using correlation IDs, ensuring precision in large-scale simulations.
Optimized Prompt Engineering We refined AI prompts iteratively to ensure accuracy and scalability in data generation.
Cost-Aware Scalability Through cost modeling and strategic optimizations, we ensured that data generation remained efficient and affordable.
Results We Achieved
- Efficient Test Data Generation: The system generated billions of realistic test records across 12 distinct simulations, meeting all analytic and numerical test requirements.
- Lower Compute Costs: Cost-aware prompt engineering and agent design cut compute costs by approximately 60% versus the initial baseline.
- Enhanced System Functionality: Optimized prompts delivered precise, linked, and diverse data sets across the testing pipeline.
- Future-Proof Design: The agentic AI system was built with flexibility and adaptability, ready to accommodate evolving business needs.
Efficiently created billions of realistic data records, optimizing costs and enhancing large-scale testing capabilities.
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