Tonic Fabricate Data Agent

Product Introduction

1. Tonic Fabricate Data Agent is an AI-powered synthetic data generation tool that enables users to create realistic, scalable datasets through conversational interaction. It supports relational databases, unstructured data formats like PDFs and DOCX, and mock APIs, eliminating the need for manual data engineering or pre-existing datasets. The product uses natural language processing to interpret user requests and generate contextually relevant synthetic data tailored to specific domains.
2. The core value lies in accelerating innovation by providing developers and AI engineers with on-demand synthetic data that mirrors real-world complexity. It removes bottlenecks in product development, AI model training, and compliance-sensitive workflows by generating structurally intact, privacy-safe datasets without exposing sensitive information.

Main Features

1. The Data Agent generates relational databases with preserved foreign key relationships and schema integrity for platforms like PostgreSQL, MySQL, Databricks, and Oracle. It automatically populates nested JSON structures with varied synthetic data to enable comprehensive testing of application data layers.
2. It produces unstructured data outputs including PDFs, DOCX files, EML emails, and mock APIs, allowing users to simulate real-world documents and API responses. The system supports custom formatting rules and domain-specific terminology for use cases ranging from healthcare records to financial transactions.
3. Users can export datasets directly into CI/CD pipelines or development environments in formats like CSV, SQL dumps, or cloud database instances. Integration capabilities include automated data hydration for demo environments, AI training pipelines, and testing frameworks requiring dynamic dataset updates.

Problems Solved

1. The product addresses the challenge of obtaining production-like data for development and testing without compromising sensitive information or requiring lengthy data anonymization processes. It eliminates dependency on scarce or restricted real-world datasets that often delay project timelines.
2. Primary users include software developers needing test databases, AI/ML engineers requiring training data for models, and product teams creating demo environments. Compliance officers in regulated industries like healthcare and finance also benefit from its privacy-safe data generation.
3. Typical scenarios include generating synthetic patient records for healthcare app testing, creating mock financial transactions for fraud detection algorithms, and producing demo datasets for sales enablement without exposing customer PII.

Unique Advantages

1. Unlike static synthetic data tools, Fabricate Data Agent uses conversational AI to iteratively refine datasets based on natural language feedback, enabling real-time adjustments to data distributions, formats, and relational constraints. Competitors typically require predefined templates or manual schema configurations.
2. The platform uniquely combines structured and unstructured data synthesis in a single workflow, automatically maintaining relational consistency across database tables while generating matching unstructured documents like PDF invoices or clinical notes.
3. Competitive differentiation includes native support for 15+ database platforms and file formats, automated JSON structure population with nested entities, and integration with Tonic Structural for hybrid datasets combining synthesized and de-identified production data.

Frequently Asked Questions (FAQ)

1. How does Fabricate Data Agent generate data without existing source material? The system uses large language models trained on public domain datasets and user-provided schemas to create statistically representative synthetic data. Users can input sample table structures or describe desired data characteristics via chat to guide generation.
2. What file formats and database systems does Fabricate support? It exports to PostgreSQL, MySQL, Oracle, Databricks, CSV, JSON, PDF, DOCX, and EML formats. Database schemas can be automatically translated between SQL dialects while preserving relational integrity.
3. How does the tool ensure relational consistency across generated database tables? Foreign key relationships are enforced through constraint-aware synthesis algorithms that maintain referential integrity across tables. Users can customize cardinality ratios and distribution patterns via chat commands.
4. Is Fabricate-generated data compliant with GDPR and HIPAA? All output is synthetic by design, containing no real personal data, which exempts it from privacy regulations. For hybrid workflows, Tonic Structural provides complementary de-identification to meet compliance requirements.
5. Can synthetic datasets be integrated with existing CI/CD pipelines? Yes, the platform provides API access and pre-built connectors for Jenkins, GitHub Actions, and AWS CodePipeline to automatically refresh test databases or training data during deployment cycles.