SynthData – AI-Powered Synthetic Test Data Generation Engine

Overview: SynthData is an AI-powered synthetic test data generation engine that createss production-realistic test data at any scale. Unlike manual test data creation or risky production data copies, SynthData generates 100% synthetic data with guaranteed referential integrity — no real data involved.

The Problem SynthData Solves: • Manual test data creation — hours of spreadsheet effort replaced by seconds • Production data copies — eliminates compliance risk with fully synthetic data • LLM-generated data breaks FK integrity — SynthData guarantees referential integrity • Test data doesn’t reflect real-world patterns — statistical pattern learning from CSVs • Schema changes break test data — automatic schema evolution handling • No test data in CI/CD — one-click CI/CD pipeline integration • LLM row limits (100–500 max) — SynthData generates 100K+ rows in seconds

Core Capabilities:

1. SQL Schema → Instant Test Data Paste CREATE TABLE DDLs. Auto-detects tables, columns, primary & foreign keys, and unique constraints. Generates data in correct dependency order (parents → children). Supports complex multi-table schemas with cascading relationships.

2. CSV Pattern Learning Upload domain CSVs. Learns statistical distributions, cardinality, null ratios, and cross-column correlations. Generates synthetic rows that statistically match real data patterns. Original data is never stored or transmitted — ensuring data privacy.

3. Natural Language → Python Code Describe requirements in plain English. Produces a fully runnable Python script. Zero LLM dependency after generation — scripts run independently.

4. CI/CD Integration & Schema Evolution Auto-generates standalone Python scripts for version control and CI/CD pipelines. Automatic schema evolution handling — test data regenerates correctly when schemas change.

Industry Applications: • Banking & Financial Services — Synthetic transaction, customer, and account data for testing core banking migrations, fraud detection models, and regulatory reporting pipelines. Guaranteed FK integrity across complex banking schemas. • Insurance — Synthetic claims, policy, and policyholder data for testing Guidewire/Duck Creek migrations. Statistical pattern learning ensures realistic actuarial test scenarios. • Travel & Hospitality — Synthetic booking, passenger, and loyalty data for reservation system migration load testing. Generates millions of booking records with realistic GDS-format data. • Healthcare — Synthetic patient, encounter, and claims data for EMR migration testing. 100% synthetic PHI-free data eliminates HIPAA compliance risk.

Business Benefits: • Eliminates compliance risk — 100% synthetic, no real data involved • 100K+ rows in seconds — unlimited scale beyond LLM row limits • Guaranteed referential integrity — no orphan records or failed joins • Deterministic, repeatable results — consistent data from same schema • CI/CD ready — auto-generated Python scripts integrate into pipelines • Zero vendor lock-in — standalone scripts with no runtime LLM dependency

Cloud-Native Deployment on AWS: Deployed on Amazon EKS. Amazon Bedrock powers NL-to-code. Amazon S3 stores generated datasets. Integrates with AWS CodePipeline and CodeBuild for CI/CD.