Tonic is a cloud‑hosted platform that generates high‑fidelity synthetic data modeled on production patterns or created from scratch, preserving statistical properties and referential integrity while removing sensitive information. It lets engineering, QA, and data‑science teams replace real data dependencies with privacy‑preserving datasets, accelerating development, testing, and AI model training. Integrated APIs and SOC 2‑certified security enable on‑demand scaling and seamless incorporation into CI/CD pipelines.
Funding
$35M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
3OFounders
Product
Problem
Engineering teams often face bottlenecks because real production data is either unavailable, too large, or contains sensitive information that cannot be used in development, testing, or AI model training. This leads to delayed releases, higher defect rates, and limited ability to experiment with AI-driven solutions.
Solution
Tonic provides a cloud‑hosted platform that generates high‑fidelity synthetic data modeled after production patterns or created from scratch. The synthetic datasets retain the statistical properties and referential integrity of the original data while removing any personally identifiable or confidential information. By supplying realistic test and training data on demand, Tonic enables faster feature development, more thorough QA cycles, and safe AI model training without exposing sensitive records. The platform integrates with existing data pipelines, allowing teams to replace real data dependencies with synthetic equivalents and accelerate release cycles across the organization.
Target Audience
Primary customers are software engineering, QA, and data science teams that need realistic, privacy‑preserving data for development, testing, and AI model training, particularly in regulated industries.
Features
- Automated generation of synthetic datasets that mirror production schemas, distributions, and relationships
- Support for creating data from scratch using configurable pattern libraries
- Secure, SOC 2‑certified cloud environment that guarantees data privacy and compliance
- APIs and connectors for seamless integration with CI/CD pipelines, data warehouses, and ML training workflows
- On‑demand scaling to produce datasets ranging from multi‑petabyte sources down to gigabyte‑size samples
- Built‑in data cleaning and transformation tools to refine source data before synthesis