Realistic test data, on demand:
without a single real customer record
A team testing a new feature or demoing a product usually ends up either using real customer data, which is a privacy risk, or hand-rolled fake data that does not resemble real usage patterns closely enough to catch real bugs. An agent generates synthetic data that is structurally and statistically realistic, with zero real customer information inside it.
The process today
A development or QA team testing a new feature usually faces an uncomfortable choice: use a copy of real customer data, which creates a privacy and compliance risk the moment it sits in a test environment, or build fake data by hand, which tends to be too clean and too typical to catch the bugs that only show up with messy, realistic, edge-case data.
The second cost is that hand-rolled test data goes stale. The same handful of sample records get reused across tests for months, so new code gets tested against the same narrow set of scenarios repeatedly, missing whatever the real data’s actual variety would have caught.
The third is that demos suffer the same problem from the other direction: a demo running on obviously fake-looking data, three rows of “Test User 1,” “Test User 2,” undersells a product that would look much more convincing running against data that actually resembles a real deployment.
What the agent does
The agent generates synthetic data matched to your real schema’s structure and statistical patterns, record counts, typical value ranges, relationships between tables, without ever touching or copying actual sensitive records. Generated sets deliberately include edge cases and rare patterns, not just the easy, typical rows, since those edge cases are usually what actually breaks a system in testing.
Volume generation supports load testing at a scale a hand-built dataset never could, and a refresh pipeline means a fresh dataset is available on demand rather than requiring a new export or anonymization request every time. Every generated set is checked against your real data before delivery to confirm nothing in it resembles an actual identifiable customer record.
Typical use: QA environments that need realistic data without a privacy risk, sales demos that should not run on three rows of obviously fake users, load testing that needs volume a manually built dataset cannot provide.
What stays with humans
Reviewing the generator’s output against what your team considers a realistic and useful test scenario stays with your QA or engineering team; the agent generates the data, your team decides if the coverage is right for the test in question. Any decision about what counts as too close to a real record, which affects the privacy check threshold, is set and can be tightened by your team. Which schemas and data types are worth building a generator for is a prioritization call your team makes.
Guards
Every generated dataset is checked against your real data before delivery to confirm no record is close enough to be identifiable as an actual customer, and that check result is documented and delivered alongside the data. The generation process never requires or stores a copy of your actual sensitive records, only the schema and statistical shape needed to build a realistic generator. The generator itself is tested and reviewed before being handed off for ongoing use, and documentation states clearly what scenarios it covers and what it does not.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $700 | One schema, generation pipeline, privacy check, documentation | 1 to 2 weeks |
| Department package | from $2,800 | Multiple interconnected schemas, load-testing volume, refresh pipeline across teams | 3 to 6 weeks |
Running cost is usually $15 to $50 a month in model usage depending on how often fresh datasets are generated.
Related
This pairs well with test generation for the testing side that consumes this synthetic data, and with computer-use agent for legacy systems for systems where test data needs to be entered through a screen rather than an API. See the AI agents service page and the automation-everything overview for full package details. For real builds on realistic demo environments and legacy system recovery, see the multi-vendor marketplace platform demo case study and the factory ERP recovery case study.
Testing against real customer data or three rows of obviously fake users? Get in touch and we will look at your schema.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does it cost to set up synthetic test data generation?
From $700 for one data schema and one generation pipeline, live in 1 to 2 weeks. Multiple interconnected schemas, such as a full e-commerce order flow, usually run $1,500 to $2,800.
How long before it is live?
1 to 2 weeks once we understand your real data's schema and statistical shape, without needing access to the actual sensitive records themselves.
Do you need our real data to build this?
We need to understand the schema and general statistical patterns, counts, distributions, common value ranges, but not the actual sensitive records. Where a sample is useful, we work with an already-anonymized extract.
Can it generate edge cases, not just typical data?
Yes, and that is usually the point. Realistic test data needs to include the rare patterns that break systems, not just average-looking rows, so we build the generator to include those deliberately.
How do we know there is no real data mixed in?
Every generated dataset is checked against your real data before delivery to confirm no record matches or resembles an actual customer closely enough to be identifiable, and that check is part of what you receive.