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Who will be on the call

A developer data advocate who will walk you through Tonic.ai's data platform solutions, from value prop to differentiators

A Solutions Architect to answer the in-depth technical questions you and your team may have

What we’ll cover

Your business objectives and needs in the realm of quality data for testing and development

Tonic.ai's data platforms, including their capabilities, integrations, and use cases

Licensing options from hosted to self-hosted

Any questions you have specific to your data, infrastructure, or use case

Tonic customers have achieved

600 hrs
Development hours saved
20x
Faster regression
8PB > 1GB
Subset size reduction
Senthil Padmanabhan
Technical Fellow, VP of Eng
“Tonic has an intuitive, powerful platform for generating realistic, safe data for development and testing. Tonic has helped eBay streamline the very challenging problem of representing the complexities contained within Petabytes of data distributed across many environments."
Sebastian Kowalczyk
Senior DevOps Engineer
“With Tonic, we’ve shortened our build process from 60 minutes down to 20. Their subsetting and de-identification tools are a critical part of Everlywell’s development cycle, making it easy for us to get data down to a useful size and giving me confidence it’s protected throughout."
Jordan Stone
VP of Engineering
“If I think about what it would cost for us to build something even remotely viable for us to solve our test data problem in the way that Tonic has solved it for us, it's orders of magnitude more than what it costs us to run Tonic Cloud."
Jason Lock
Senior Software Engineer and Tech Lead
“Tonic drastically reduces the amount of time it takes for a full regression test for all of our core features. Before it was somewhere within a two-week time span for QA to get the data set up; now they are ready to go and have tested all of the core features manually within a half a day.”
Matty Woznick
Enablement Programs Manager
“You can’t tell that our demo environment runs on Tonic data. It is so close to a mirrored experience for what our partners deal with, and that helps us empower them and guide them better. End of story.”
Kevin Paige
Chief Information Security Officer
“Our security team loves it because it solves a complex problem crucial to reducing risk for our company. Infrastructure loves it because it’s on-prem and easily deployed in a container. And our engineers love it because it’s easy to use and integrates seamlessly into our software development lifecycle without asking them to do any extra work.”

Recent blog posts

Explore the latest news in the world of test data and quality engineering, including technical deep dives and industry insight.

Data privacy

Your attack surface is your data. Mythos is the proof.

Data synthesis

Tonic.ai vs. Synthesized.io: Purpose-built depth vs. bundled breadth

Data synthesis

Tonic Fabricate vs. Claude: Why synthetic data generation needs more than an LLM

Data synthesis

Generate referentially intact synthetic data across your entire data ecosystem

Technical deep dive

How to mock the PayPal API with Tonic Fabricate

Data synthesis

Best synthetic data generation tools and platforms compared for 2026

Technical deep dive

Benchmarking OpenAI's Privacy Filter: What it gets right, and where PII detection still needs real data

Generative AI

Synthetic data is all you need for Reinforcement Learning

Data de-identification

The agentification of Test Data Management is here. Meet the Structural Agent.

Product updates

From off-limits to AI-Ready: Preparing unstructured data directly in Microsoft Fabric with Tonic Textual

Data de-identification

How redaction software can help government agencies comply with FOIA

Test data management

Training effective models without the annotation budget

Product updates

Tonic Textual + Haystack: Privacy-safe data for RAG pipelines

Product updates

Tonic Textual + LangChain: secure data for LLM applications

Product updates

Tonic Textual + MCP Server: PII-safe context for AI

Generative AI

Inference protection for LLMs: Keeping sensitive data out of AI workflows

Data privacy

How to de-identify financial documents with Tonic Textual

Test data management

Tonic Structural vs Informatica: Which is better for Test Data Management?

Test data management

Informatica Test Data Management pros and cons: a complete guide

Data de-identification

How to maximize HEDIS scores with synthetic data

Data privacy

How to mitigate the risk of a data breach in non-production environments

Product updates

Introducing the Unstructured Data Catalog: From unknown text to usable data

Data de-identification

Data masking: DIY internal scripts or time to buy?

Data privacy

How data masking & synthesis support Zero Trust

Data synthesis

How synthetic data can help solve AI’s data crisis

Data privacy

Healthcare’s blind spot: What happens after our data is shared?

Product updates

Tonic.ai product updates: January 2026

Data de-identification

A guide to data masking for HITRUST certification

Data de-identification

How to sanitize production data for use in testing

Test data management

How test data generators support compliance and data privacy

Product updates

Guided redaction in Tonic Textual: Human-precision, streamlined by AI

Data de-identification

Transform sensitive text into AI-ready data on Microsoft Fabric

Product updates

Hyper-realistic synthetic data via agentic AI has arrived. Meet the Fabricate Data Agent.

Product updates

Your data, your model: Self-serve custom entity types in Tonic Textual

Product updates

Tonic.ai product updates: October 2025

Generative AI

Preventing training data leakage in AI systems

Generative AI

Best practices for AI model optimization without risking privacy

Data privacy

Navigating the European Union AI Act

Product updates

Tonic.ai + Microsoft: Accelerating AI adoption with privacy-compliant synthetic data

Generative AI

Turn sensitive data into safe AI assets with Tonic Textual in Amazon SageMaker Unified Studio

Generative AI

Tonic Textual on Microsoft Fabric: Now in private preview

Data privacy

How to comply with the NSD's Data Security Program

Generative AI

Ensuring data compliance in AI chatbots & RAG systems

Data de-identification

How to de-identify insurance claims and documents with Tonic Textual

Data de-identification

How to implement data masking to comply with ISO 27001

Data de-identification

Data masking and data governance: Ensuring data integrity

Product updates

Tonic.ai product updates: August 2025

Product updates

Meet Tonic Datasets: Bespoke synthetic datasets for AI training and evaluation

Data de-identification

Building a scalable approach to PII protection within AI governance frameworks

Data privacy

CCPA: Understanding how synthetic data can help achieve compliance

Tonic.ai editorial

Data is the new code: the evolution of software development

Product updates

Tonic.ai product updates: June 2025

Technical deep dive

Deep dive: Small vs large language models for token classification

Data de-identification

Demo: Fine-tuning LLMs with Tonic Textual

Data de-identification

Evaluating open-source tools for data masking

Product updates

Tonic.ai product updates: May 2025

Product updates

Introducing audio synthesis for Tonic Textual: actionable audio, privacy protected

Test data management

Why your competitors are investing in Tonic.ai—and why you should, too

Data de-identification

AI data breaches in healthcare: protecting patient privacy & trust

Product updates

Tonic Textual is now on the Databricks Marketplace: unstructured data, meet easy ingestion

Data privacy

Webinar highlights: Accelerating domain-specific AI model training with private data

Product updates

Tonic.ai product updates: March 2025

Product updates

Tonic.ai product updates: December 2024

Generative AI

The importance of high quality synthesis when creating safe training datasets

Data de-identification

Protecting privacy without hurting RAG performance

Product updates

We are joining forces with Google Cloud to accelerate AI and software development with privacy-first data solutions on Google Cloud Marketplace

Product updates

Tonic.ai product updates: October 2024

Generative AI

LLM RAG vs fine tuning: which method is best?

Technical deep dive

Building a RAG system on Databricks with your unstructured data using Tonic Textual

Healthcare

Using synthesized data for HIPAA expert determination

Product updates

Creating unstructured data pipelines for retrieval augmented generation

Technical deep dive

The challenges of preparing unstructured data for Generative AI

Product updates

Tonic.ai product updates: July 2024

Technical deep dive

Sensitive data in text embeddings is recoverable

Technical deep dive

How to create de-identified embeddings with Tonic Textual & Pinecone

Product updates

Tonic.ai product updates: May 2024

Test data management

Best test data management solutions

Product updates

Tonic Textual available as Snowflake Native App to enable secure AI development

Test data management

Achieving test data management totality: the difference between total coverage and "close enough"

Product updates

Tonic.ai product updates: April 2024

Data privacy

How to de-identify legal documents with Tonic Textual

Data privacy

Top 5 risks of not redacting sensitive business information when machine learning

Product updates

Tonic.ai product updates: March 2024

Product updates

De-identifying Salesforce data for testing and development. Tonic Structural now connects to Salesforce

Product updates

Tonic.ai product updates: February 2024

Test data management

De-identifying test data: K2View’s entity modeling vs Tonic’s native modeling

Product updates

Tonic Validate is now on GitHub Marketplace! (Part 2)

Product updates

Tonic Validate is now available on GitHub Marketplace!

Technical deep dive

RAG evaluation series: validating the RAG performance of OpenAI vs CustomGPT.ai

Data de-identification

Redacting sensitive text data in JSON with Tonic Textual

Technical deep dive

RAG evaluation series: validating the RAG performance of OpenAI’s RAG Assistant vs Google’s Vertex Search and Conversation

Technical deep dive

RAG evaluation series: validating the RAG performance of Amazon Titan vs Cohere using Amazon Bedrock

Technical deep dive

Leveling up your test environments with OCI artifacts

Product updates

Tonic x Shipyard: a modern platform for secure, agile testing

Test data management

Using data upsert to optimize test data management

Technical deep dive

Elevating RAG system reliability: integration testing with Tonic Validate & LlamaIndex

Tonic.ai editorial

How 2024 will impact DevOps teams

Data de-identification

Tonic Textual: Document redaction to de-identify PDFs and Word docs

Tonic.ai editorial

How 2024 will impact quality engineering teams

Technical deep dive

Redacting sensitive free-text data: Build vs buy