We’re building a new product and don’t have real data yet—how do teams generate realistic test data from scratch?

Most new products stall on the same bottleneck: you can stand up services and frontends in days, but without realistic data, your tests lie and your demos fall flat. At the same time, you can’t safely point a brand-new system at real customer records—even if you had them. So the question isn’t “should we copy production?” It’s “how do we generate realistic test data from scratch so engineering can move fast without creating a privacy problem we’ll regret later?”

Quick Answer: Use synthetic data generation that understands schema, relationships, and statistical patterns—rather than hand-built fixtures or ad hoc scripts—to create production-shaped test data before you have production users. Tonic Fabricate does this with a Data Agent that generates complete relational datasets, files, and mock APIs from natural language prompts, so you can ship a new product without ever exposing real PII.

The Quick Overview

• What It Is: A synthetic data workflow that lets you generate realistic, relational test data from scratch—no production records required.
• Who It Is For: Engineering, QA, and data teams building net-new products, greenfield services, or refactors where live customer data is unavailable or off-limits.
• Core Problem Solved: You need production-like data to test flows, edge cases, and integrations, but you don’t yet have—and shouldn’t manufacture—copies of real user data.

How It Works

The core idea is simple: instead of trying to fake “just enough” data by hand, you describe the system you’re building and let a synthetic data generator produce a fully-populated, relational dataset that behaves like your future production data. Tonic Fabricate centers this around its Data Agent: you talk about your domain in plain English, it designs tables, relationships, and distributions, then iterates with you until the dataset matches the real-world behavior you expect.

1. Describe your domain and workflows:
   Start by telling the Data Agent what you’re building: entities (users, accounts, tickets), relationships (one-to-many, many-to-many), volumes, and key workflows (onboarding, billing, approvals). You can paste schema DDL if you already have it, or let the Agent propose one.

2. Generate a relational synthetic dataset:
   The Agent creates structured tables that respect your schema and foreign keys, populates them with realistic values, and maintains cross-table consistency—so joins work and stateful flows (e.g., sign-up → trial → subscription → churn) look coherent. You can add constraints (e.g., 20% churn rate, 5% failed payments) to match how you expect production to behave.

3. Export into your dev and QA environments:
   Once the dataset looks right, you export in the formats your workflows need—SQL to hydrate staging, CSV/JSON for integration tests, mock APIs for frontend and partner testing, and even realistic unstructured artifacts (emails, PDFs, tickets) when your product needs them. Because this is fully synthetic, there’s no PII or PHI to govern, and you can refresh as often as you want.

Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Data Agent–driven schema & data generation	Lets you describe your domain and test goals in natural language; the Agent designs and populates realistic relational datasets.	Shortens the path from “blank database” to “production-like test data” from weeks of manual work to hours.
Relational, statistically realistic synthetic data	Respects schema, foreign keys, and realistic distributions (e.g., signup cohorts, error rates, transaction volumes).	Your app and tests see data that behaves like real-world usage, reducing defects that only appear under realistic load and shape.
Multi-format export (databases, files, mock APIs)	Exports to SQL/CSV/JSON and generates artifacts like emails, reports, and mock endpoints.	Hydrates every environment—from backend integration to frontend demos—without custom scripting per data consumer.

Ideal Use Cases

• Best for net-new products with no users yet: Because it lets you simulate months or years of user behavior (onboarding, churn, upgrades, edge-case flows) before the first real customer signs up, so you’re not shipping blind.
• Best for replacing brittle test fixtures and hand-built mocks: Because it centralizes your “test universe” in one synthetic dataset that stays coherent across services, instead of scattered YAML/JSON fixtures that drift as schemas evolve.

Why DIY fixtures and scripts usually fail

Most teams start in the same place:

• A handful of dummy users stitched together in a seed script.
• Some QA-only records that one person knows how to reset.
• A work-in-progress schema that changes weekly.

The failure modes show up quickly:

• Broken relationships: Foreign keys don’t line up, so tests pass on fixtures but fail in staging when joins behave differently.
• Sparse coverage: You lack edge cases—no accounts with exactly 3 expired cards, no tickets with 10+ reassignments, no “weird but real” states.
• Stale test data: Every schema migration breaks your fixtures, and nobody has time to keep them updated, so teams quietly spin up their own local snapshots.
• Hidden privacy debt: As soon as you import any real data “just to test,” you’ve created unofficial copies that expand your breach surface area and complicate future compliance.

If you’re early-stage and don’t have real users yet, you have a chance to avoid building this mess in the first place. Synthetic data from scratch is how you keep speed without creating tomorrow’s governance headache.

How synthetic generation from scratch actually works

With Tonic Fabricate, you don’t need existing production data to generate something realistic. The workflow is closer to designing a simulation of your product’s world:

1. Model the entities and relationships

   You either:

   • Paste your existing schema (DDL) into the Data Agent, or
   • Describe the domain: “I have companies, users, projects, and tasks. A company has many users; users can belong to multiple projects; tasks belong to projects and have statuses.”

   The Agent infers tables, primary keys, foreign keys, and cardinalities, and you refine them until they match your architecture.

2. Define behavioral rules and distributions

   Realistic data isn’t just rows; it’s patterns:

   • Sign-up rates over time.
   • Conversion funnels (trial → paid, abandoned).
   • Error rates and edge cases.
   • Multi-tenant skew (a few large customers, many small ones).

   You tell the Agent things like:

   • “Simulate 10K companies, with a power-law distribution—most small, a few very large.”
   • “20% of users never activate; 5% of invoices fail due to payment errors.”
   • “Include some pathological accounts with hundreds of open tasks for stress testing.”

   The Agent encodes these into generation rules and distributions.

3. Generate relational datasets that stay consistent

   Once the rules are set, Fabricate generates records that:

   • Respect referential integrity (no orphan rows, no broken joins).
   • Preserve cross-table consistency (a churned account won’t have new invoices next month).
   • Follow your statistical properties (error rates, seasonality, usage spikes).

   This is where synthetic data beats random data. You get something that behaves like the workload you expect in production, not just noise.

4. Extend into unstructured artifacts

   Many products interact with text and documents long before they interact with large numbers of users:

   • Support tickets and chat logs.
   • Emails, PDFs, and reports.
   • Notes fields, comments, and descriptions.

   When you need realistic text, Tonic’s broader suite comes into play:

   • Use Fabricate to generate synthetic text tied to your structured entities (e.g., tickets with subjects, bodies, and resolutions).
   • Use Textual workflows when your future involves RAG or LLMs and you want realistic-but-fake corpora, with NER-powered entity handling and synthetic replacements.

5. Integrate with your dev, CI, and QA workflows

   The output isn’t a one-off dump; it’s a repeatable asset:

   • Hydrate local dev environments via SQL.
   • Seed test databases in CI pipelines.
   • Feed end-to-end tests with consistent datasets across services.
   • Power sales demos and sandboxes with the same synthetic universe.

   Because there’s no real PII, you don’t need a special approval process for every refresh. You treat test data as code: versioned, reproducible, and disposable.

Features & Benefits Breakdown (Expanded)

Core Feature	What It Does	Primary Benefit
Data Agent–driven schema & data generation	Conversational interface that ingests schema or domain descriptions, then generates relational synthetic data aligned to your architecture.	Dramatically reduces time spent writing and maintaining custom seed scripts, letting engineers focus on features instead of test plumbing.
Relational, statistically realistic synthetic data	Applies rules and distributions to produce coherent state transitions, pipelines, and edge cases (e.g., churn, retries, escalations).	Fewer “works on my fixtures” surprises; more bugs caught in lower environments because test data mirrors real-world complexity.
Multi-format export (databases, files, mock APIs)	Exports to SQL/CSV/JSON and generates synthetic documents and mock API responses aligned to your structured data.	One source of truth for all consumers—backend, frontend, QA, and demos—reducing drift between environments and services.

Ideal Use Cases

• Best for pre-launch reliability testing: Because you can simulate months of activity—signups, upgrades, cancellations, support tickets—before your first real user, so you’re not discovering obvious issues on day one of traffic.
• Best for early AI and analytics prototyping: Because you can generate realistic inputs for future analytics dashboards, reporting pipelines, or RAG/LLM features without waiting on live usage or exposing sensitive sandboxes.

Limitations & Considerations

• Synthetic ≠ ground truth:
  Synthetic data can mirror the complexity and distributions you specify, but it’s still a model of reality. When you eventually have production data (and the governance to handle it safely), you’ll want to validate that your assumptions about distributions and edge cases hold.

• Requires intentional modeling:
  The Data Agent accelerates the process, but you still need to think through your domain: entities, relationships, and expected behaviors. Hand-waving here will create hand-wavy data. Treat this like designing a schema or test strategy—because that’s effectively what you’re doing.

Pricing & Plans

Tonic’s pricing is structured around the scale and deployment model your teams need, across Structural, Fabricate, and Textual. For generating realistic test data from scratch, Fabricate is the primary product, often paired with Structural later once you have production systems online.

• Team / Growth Plans: Best for product and engineering teams needing to unblock development and QA quickly with synthetic datasets and mock APIs, typically in a single cloud environment.
• Enterprise Plans: Best for larger organizations needing multi-environment deployments, custom integrations, and governance features across Tonic’s full suite (Structural, Fabricate, Textual), with options for self-hosting, SSO/SAML, and alignment to SOC 2 Type II, HIPAA, and GDPR requirements.

(Exact pricing is tailored; the demo process is where you map your stack and workloads to the right configuration.)

Frequently Asked Questions

Can synthetic data from scratch really be “realistic” without production as a reference?

Short Answer: Yes—if you encode the right schema, relationships, and behavioral assumptions into the generator.

Details:
Realism isn’t about copying existing user rows; it’s about preserving the constraints and patterns your system will see:

• Your schema defines which entities exist and how they relate.
• Your product strategy defines expected behaviors (conversion rates, churn, usage frequency).
• Your edge-case thinking defines “weird but possible” scenarios you want to test.

Tonic Fabricate’s Data Agent uses this information to generate a synthetic universe that respects referential integrity and follows the distributions you specify. Later, when you do have production data, you can refine these distributions and complement Fabricate with Tonic Structural to transform real datasets—while still keeping them de-identified and compliant.

What happens when our schema changes during development?

Short Answer: You regenerate and evolve your synthetic data alongside your schema, instead of patching brittle fixtures.

Details:
Early in a product’s life, schemas are in motion—tables split, columns move, types change. In a fixture-heavy world, every migration triggers a manual cleanup cycle: rewrite seed scripts, update JSON fixtures, scramble QA data generation.

With Fabricate:

• You update the schema in the Data Agent (or it detects changes from your DDL).
• You adjust any behavior rules impacted by the change.
• You regenerate the dataset, preserving referential integrity under the new model.

Because generation is automated and driven by your domain description, keeping test data aligned with schema becomes a fast, repeatable step—not a multi-day chore before each release.

Summary

If you’re building a new product and don’t have real data yet, you’re actually in an ideal position: you can design a test data strategy that maximizes speed and quality without ever creating risky copies of PII. The key is to generate synthetic test data from scratch that still looks and behaves like the production world you expect—relational, statistically realistic, and dense with edge cases.

Tonic Fabricate’s Data Agent turns that into a repeatable workflow: you describe your domain, it generates a production-shaped synthetic universe you can hydrate into dev, CI, QA, and demos. As your product and schema evolve, you evolve the synthetic model with them. When production usage finally arrives, you complement this with Tonic Structural and Textual to transform real data safely—without abandoning the speed you’ve built into your pipelines.

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