Tonic.ai

Tonic.ai builds tools that give engineering and AI teams realistic data to work with while keeping sensitive information out of lower environments. Its products generate synthetic and de-identified data that preserves the structure and behavior of production data, so developers can build, test, and train models without handling raw regulated records.

What they provide

The suite spans structured and unstructured data. Tonic Structural transforms and synthesizes data from production databases for development and testing while maintaining referential integrity and supporting subsetting. Tonic Textual uses named-entity recognition and tokenization to detect and redact or synthesize sensitive details in free text, producing compliant datasets for LLM training and retrieval. Tonic Fabricate generates realistic synthetic databases from scratch.

• Segment: Synthetic and de-identified data for dev, test, and AI
• Structured: Test-data management, subsetting, and synthesis (Tonic Structural)
• Unstructured: Text de-identification and synthesis for AI and RAG (Tonic Textual)
• Generation: Synthetic database creation (Tonic Fabricate)

Where it fits in the AI data supply chain

Tonic.ai sits at the synthetic and privacy layer, bridging the gap between data utility and data protection. Its historical strength is software development and testing, giving teams safe, production-like data on demand; that same capability increasingly serves AI initiatives, where clean, de-identified text and structured data are needed to train and evaluate models without leaking personal or regulated information. Buyers are typically enterprise engineering, data, and ML teams operating under compliance constraints.