What is Synthetic Data?

Synthetic data is information that mimics the patterns, distributions, and relationships of real-world datasets without containing any actual personal or sensitive records. It lets you work with realistic data for software development, testing, and AI model training while keeping real user details out of non-production environments. 

In this blog, you'll learn how to generate synthetic data using rule-based methods, model-based approaches including agentic AI, transformative techniques like de-identification, and hybrid workflows—plus how Tonic.ai streamlines every step.

Types of synthetic data

Synthetic data falls into three broad categories. The type you choose to work with should be based on whether you have production records to leverage, need net-new datasets, or want a mix of both. Let’s consider:

• Net new synthetic data generated from scratch
• Derived synthetic data generated based on existing data
• Hybrid synthetic data

Net new synthetic data generated from scratch

Net-new synthetic data is created without reference to production records. Often, you start with a schema definition—tables, columns, constraints—and a generator crafts each value so that statistics like means, variances, and correlations match your target domain. 

This approach is ideal when you can't or don't want to use real data, for example, in greenfield feature development, AI model testing, or scenarios where production data is entirely off-limits.

Derived synthetic data generated based on existing data

Derived synthetic data uses a real dataset as its blueprint. A synthesis engine analyzes distributions, relationships, and patterns in the source data, then generates new records that preserve statistical shape while replacing identifiable details.

Use this method when you need to balance realism and safety; it captures production nuance but removes direct identifiers. 

Note that this approach can be somewhat limited in scope. Algorithms can manage individual columns, several related columns, or even complete tables of related data. But due to the curse of dimensionality, the holy grail of deriving an entire synthetic database from an existing production database via a single modeling engine is still the object of many a data scientist’s quest.

Hybrid synthetic data

Hybrid synthetic data combines net-new and derived approaches. For example, you might synthesize core tables from scratch while deriving a lookup table from masked production data, or you could generate base datasets and then inject derived records to fill gaps in rare-value scenarios. 

Hybrid workflows let you supplement sparse or sensitive areas with fresh synthetic records alongside production-based data, ensuring both coverage and realism where you need them most.

Benefits of synthetic data

Synthetic data offers concrete advantages over using raw datasets. Let’s look at a few core benefits.

Unlimited data access

With the right tools in place, you can generate as much synthetic data as you need on demand. Unlimited volume lets you: 

• stress test systems under high load,
• simulate rare scenarios that production logs barely capture, and
• explore edge cases your QA team knows exist but has never seen in a development environment.

Increased data privacy and reduced security risks

Since synthetic records contain no real user identifiers, you reduce the privacy leakage risks associated with copying production snapshots. Synthetic data supports compliance-aligned workflows under GDPR, CCPA/CPRA, HIPAA, and other frameworks by keeping real personal information out of your training and test datasets.

Accelerated product development

With instant, self-serve data generation, your team can prototype features and validate workflows in parallel rather than sequentially. You won't be blocked by data access gates or manual masking processes that delay sprints. Synthetic datasets let you iterate faster, shorten feedback loops, and ship features with confidence.

Optimized model training

High-quality synthetic data provides balanced class distributions, controlled label noise, and systematic coverage of rare patterns. You can train AI models on a continuous stream of fresh, domain-tailored data without risking model memorization of real examples or biasing results through underrepresented cohorts. 

Better software testing coverage

Synthetic data's controllable nature makes it straightforward to bake edge-case tests into your suites. You can craft records with specific null rates, boundary values, or relational anomalies to trigger code paths that production sampling might never surface. This:

• improves test coverage,
• reduces production bugs, and
• gives you confidence that your application handles outliers.

Synthetic data use cases

Common applications of synthetic data include:

• Greenfield product or feature development
• Agentic workflow development and testing
• AI model training and testing
• Sales demos and customer onboarding

Greenfield product or feature development

Scenario: You're building a payments platform but have zero transaction data because you haven't launched yet. Your QA team needs realistic payment records—credit cards, ACH transfers, international wire transfers with currency conversions—to test checkout flows. 

Synthetic data solution: Rather than waiting months for real customers or manually creating hundreds of test fixtures, generate synthetic transaction data that mirrors expected production patterns: 70% credit card, 25% ACH, 5% wire transfers, with realistic amounts, timestamps, and failure scenarios.

Agentic workflow development

Scenario: Your team is building an AI agent that processes customer support emails, extracts intents, and routes tickets to appropriate departments. The agent needs training data: thousands of realistic support emails spanning billing questions, technical issues, feature requests, and complaints. 

Synthetic data solution: Use Tonic Fabricate's Data Agent to generate synthetic email datasets (EML files) complete with realistic subject lines, body text, attachments references, and sender patterns—all structured to train your agent without exposing real customer communications.

AI model training and testing

Scenario: You're training a fraud detection model but production only contains 0.1% fraudulent transactions—not nearly enough examples to train effectively. 

Synthetic data solution: Oversample synthetic fraud cases with realistic patterns: unusual transaction amounts, rapid-fire purchases from multiple locations, compromised account behaviors, etc. Generate 10,000 synthetic fraud examples alongside 1 million legitimate transactions to train a balanced model, then validate on real production data before deployment.

Sales demos and customer onboarding

Scenario: Your sales engineer is demoing your e-commerce analytics platform to a retail prospect. They need a demo environment populated with realistic product catalogs, customer orders, inventory movements, and sales trends—but using real customer data would violate privacy rules and potentially expose competitive information to prospects. 

Synthetic data solution: Generate synthetic retail data matching the prospect's industry: fashion, electronics, groceries—with seasonality patterns, promotion effects, and realistic customer behavior that makes the demo feel authentic without any compliance risk.

How to generate synthetic data

Generating synthetic data typically involves one or more of these approaches. Here are a few ways to set yourself up for testing and development success:

Synthetic data via agentic AI

Using an AI agent is the latest evolution in synthetic data generation. For simple needs and smaller synthetic datasets, you can experiment working directly with an LLM. To ensure referential integrity or generate complete synthetic databases, meanwhile, Tonic Fabricate’s Data Agent enables you to generate net-new synthetic data from scratch, through a conversational chat interface. Tell the Agent your requirements—schema structure, volume, distributions, relationships—and it will leverage LLM domain expertise combined with Tonic.ai's industry-leading synthetic data generators to produce hyper-realistic fully relational databases and unstructured datasets in minutes.

Rule-based data synthesis

To implement rule-based generation, you define explicit patterns and constraints for each field in your schema. You specify that user_id generates UUIDs, email follows <firstname>.<lastname>@domain.com, age ranges between 18–75, and created_at timestamps fall within the past 90 days.

Tonic Fabricate also offers a rule-based data generation workflow, to streamline the process via its AI-powered UI.

Model-based data synthesis

Implementing model-based generation involves training a statistical or machine learning model (GAN, VAE, or Bayesian network) on sample data or schema abstracts. The model learns distributions and correlations—that purchase amounts correlate with customer tenure, that certain products frequently appear together—then generates new records preserving those patterns.

As mentioned earlier, this approach runs into limitations at scale and currently is better suited to smaller datasets and specific categories of data.

Synthetic data generated from production data

Instead of creating data from scratch, with this approach you generate synthetic replacements for values in or apply advanced de-identification to existing datasets. By stripping away personally identifiable information (PII) and replacing it with contextually accurate synthetic values, you preserve the statistical behavior, complexity, and specific edge cases of your original data without compromising privacy.

Achieving this at scale requires automation. Tools like Tonic Structural streamline the process for structured databases, scanning schemas to detect sensitive columns and generating high-fidelity replicas that maintain referential integrity across complex foreign keys. For unstructured data—such as free-text fields, logs, or documents—Tonic Textual applies similar transformative principles, identifying and synthesizing sensitive text entities to ensure your data is protected across the entire ecosystem.

Hybrid

To implement hybrid workflows, combine methods strategically for comprehensive coverage. For example, you could use Tonic Structural to transform core production tables (customers, orders), then use Tonic Fabricate's Data Agent to generate additional synthetic records for rare scenarios.

Generate accurate synthetic data with Tonic.ai

Synthetic data can transform how you build, test, and train—but only if generation is fast, reliable, and integrated into your workflows. Tonic.ai combines the best of all synthesis methods: 

• Agentic AI for net-new data
• High-fidelity de-identification for production-derived data
• Unstructured data synthesis to generate secure training datasets
• Hybrid pipelines that scale across structured, semi-structured, and unstructured formats. 

You get self-serve, chat-driven data generation with Fabricate's Data Agent, referentially intact masking and validation in Structural, and context-preserving unstructured synthesis in Textual. No more manual scripts, no more privacy risk, and no more data bottlenecks holding back product velocity.

Ready to see it in action? Connect with our team and discover how you can generate safe, high-fidelity synthetic data in minutes—so you can let the builders build.