Synthetic Data Generation for Enterprise POC Testing
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Solution Overview
Problem
Current methods for proof-of-concept (POC) testing in enterprise environments are inefficient, as they often require exposing sensitive data and APIs, and lack automated tools for generating representative data and APIs without coding, making it difficult for startups to test their software products effectively in simulated production environments.
Innovation Solution
A system and method that uses processor circuitry to analyze enterprise data and APIs, generating metadata to create artificial data and APIs that mimic the enterprise's production environment, allowing for POC testing in a simulated cloud environment, enabling efficient evaluation of software products by enterprises without exposing sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real enterprise data and APIs are used for POC testing, then testing accuracy and realism are improved, but data security and confidentiality deteriorate
Solution Approach 1:
The patent creates artificial copies of enterprise data and APIs that replicate the structure, relationships, and behavior patterns of real production data without containing actual sensitive information. The artificial data generation engine produces synthetic datasets that mirror the schema, constraints, and interdependencies of real data, enabling realistic testing while maintaining security through complete data anonymization.
2Adaptability or versatility
If manual API creation is used for simulated environments, then API functionality and customization are improved, but development time and complexity deteriorate
Solution Approach 1:
The system performs self-service by automatically generating artificial APIs through metadata-driven code generation. The API generation engine consumes metadata definitions and automatically produces functional API implementations, eliminating the need for manual coding while maintaining full functionality and adaptability. This automated approach reduces development time from weeks to minutes while preserving complete customization capabilities through configurable metadata.
3Reliability
If complete production environments are replicated for POC testing, then testing comprehensiveness is improved, but resource consumption and cost deteriorate
Solution Approach 1:
The patent extracts only the essential elements needed for testing from the complete production environment. Instead of replicating entire production systems with all their dependencies, the system extracts and replicates only the data schemas, relationships, and API interfaces necessary for POC validation. This selective extraction maintains testing comprehensiveness for software evaluation while dramatically reducing resource consumption by eliminating unnecessary production infrastructure.
4Productivity
If artificial data is generated without metadata-driven approaches, then data generation speed is improved, but data quality and representativeness deteriorate
Solution Approach 1:
The system performs preliminary action by establishing comprehensive metadata definitions that capture the complete structure, constraints, relationships, and business rules of production data before generation begins. This pre-defined metadata framework guides the artificial data generation process, ensuring that generated data automatically conforms to quality standards and accurately represents production data patterns without requiring post-generation validation or manual adjustments.
Data Source
AI summary
A system comprising a platform configured for communicating with enterprise end-users and for allowing the enterprise end-users to perform proof-of-concept testing for startups which provide respective enterprises with software products to be evaluated by the respective enterprises, the platform including processor functionality configured to analyze available information on enterprise data and, accordingly, generate metadata characterizing the enterprise data; generate artificial enterprise data conforming to the metadata; analyze available information on enterprise APIs and, accordingly, generate metadata characterizing the enterprise APIs; and generate at least one artificial API conforming to that metadata.