Our Solution

AI-Powered Synthetic Data Generator

Executive Summary

Enterprises rely heavily on data for development, testing, analytics, and research. However, real production data often contains sensitive information that cannot be shared safely across teams or environments. The AI-Powered Synthetic Data Generator solves this problem by generating realistic, privacy-preserving synthetic data and anonymized PDFs while maintaining data quality, structure, and usability. Built using FastAPI, React, and advanced Large Language Models, the platform enables organizations to safely use data without exposing real customer or business information.

Challenges

Data Privacy Restrictions

Production data contains PII and sensitive information, preventing safe usage in non-production environments and cross-team workflows.

Slow and Error-Prone Anonymization

Manual anonymization processes are inefficient, inconsistent, and difficult to scale across large datasets and documents.

Low-Quality Synthetic Data

Traditional data generators fail to preserve real-world patterns, relationships, and edge cases needed for reliable testing.

Broken PDF Structure

Most PDF anonymization tools disrupt layouts, fonts, and formatting, reducing document usability.

Schema and Consistency Issues

Maintaining schema compliance, uniqueness, and entity consistency across datasets and documents is complex at scale.

Solution Overview

The AI-Powered Synthetic Data Generator is a production-ready platform designed to generate structured synthetic data and anonymized PDFs while preserving realism, compliance, and usability. The system uses AI-driven workflows to analyze schemas, detect sensitive information, enforce consistency, and validate quality before delivering outputs. It supports JSON, CSV, MySQL tables, and PDF documents, enabling enterprises to safely generate data for development, QA, analytics, and training without compromising privacy.

How it Works

Users upload JSON/CSV files, connect to MySQL, or upload PDFs
The system analyzes schema, structure, and domain context
AI workflows generate synthetic records in controlled batches
Entity consistency is enforced across records and documents
PII fields are detected automatically
Selected anonymization strategies are applied
Statistical tests and LLM-based evaluations validate quality
Outputs are exported as files or ingested back into databases

Key Benefits

Privacy-Safe Data Usage

Enable teams to work with realistic data without exposing real customer or business information.

High-Quality, Realistic Outputs

Generate synthetic data that preserves structure, logic, and relationships for real-world use cases.

Schema-Compliant and Reliable Data

Ensure generated datasets follow original schemas, constraints, and data types without manual fixes.

Layout-Preserving PDF Anonymization

Anonymize sensitive documents while maintaining original formatting and visual integrity.

Faster, Scalable Data Generation

Automate data preparation to accelerate development, testing, and analytics while controlling costs.

Outcomes

Privacy-safe synthetic data generation

Realistic datasets are generated without exposing any real customer or production data

Schema-compliant outputs

Generated data strictly follows original schemas, data types, and uniqueness constraints.

High data realism and consistency

AI ensures logical consistency across records, documents, and multi-page PDFs.

Layout-preserved PDF anonymization

Sensitive PDFs are anonymized while keeping fonts, alignment, and visual structure intact.

Built-in quality validation

Statistical tests and AI-based evaluations validate realism, diversity, and completeness.

Multi-format delivery

Outputs are available in JSON, CSV, Excel, SQL, and anonymized PDF formats.

Technical Foundation

FastAPI backend

Asynchronous APIs power scalable synthetic data generation and document processing workflows.

LangGraph orchestration

State-driven workflows manage generation, validation, retries, and consistency enforcement.

React frontend

A simple UI allows configuration, preview, and download of generated datasets and PDFs.

Large Language Models

Bytedance-seed/seed-1.6-flash handles structured data generation, while Claude 3.5 Sonnet manages constrained PDF text replacement.

PDF processing engine

PyMuPDF extracts layout metadata and reinserts synthetic text at exact coordinates.

Conclusion

The AI-Powered Synthetic Data Generator demonstrates how modern AI workflows can solve long-standing enterprise data challenges. By combining structured generation, privacy protection, quality validation, and layout-preserving document anonymization, the platform enables organizations to unlock the full value of their data without compromising compliance or realism. This approach shifts data preparation from a bottleneck into a scalable, automated capability that supports innovation across teams.