Realistic test data, on demand,
with no real customer record anywhere near it
Testing against real customer data is a real risk. Testing against data that is obviously fake, three rows of Lorem Ipsum, misses the edge cases that actually break software. A synthetic data agent generates realistic test data, orders, conversations, user records, shaped by your real schema and distributions but containing no actual customer information. Every record is clearly tagged as synthetic everywhere it lands.
Why fake-looking data is not enough
Testing against real customer data carries a privacy risk that most teams know they should avoid, and sometimes do anyway. Generating realistic alternative data by hand is slow, and a handful of manually written test rows rarely cover the actual variety of what customers really do.
Obviously fake placeholder data fails too, the same three names repeated, round numbers everywhere. It never exercises the edge cases that matter. An order with an unusual discount stack. A conversation that goes off-script. A user record with a field combination nobody thought to write by hand.
Training or evaluating a model also needs volume a person cannot write by hand in reasonable time. That either stalls the work, or pushes a team toward using real data they should not be using for that purpose.
What the agent generates
The agent generates data shaped by your actual schema and realistic value distributions: orders, conversations, user records, transactions, without deriving any of it from real customer information. It can deliberately weight toward edge cases your team wants tested: an unusual combination of fields, a rare conversation path. That beats generating only typical-looking, middle-of-the-road rows that miss what actually breaks software.
Every record is tagged as synthetic in whatever environment it lands in, so it can never be mistaken for real data downstream. The generator is documented too, so your team knows exactly what scenarios it covers and where its coverage stops.
Typical scope: test data for QA, realistic scenarios for training or evaluating an agent, and data sets for load testing that need volume without privacy risk. One of our own builds replayed hundreds of real and realistic conversations before a sales agent went live. This agent brings that same discipline as a standalone capability.
Where judgment stays with your team
Deciding what counts as “realistic enough” for a given test, and whether a specific edge case genuinely needs coverage, is a judgment your team makes. Any case where testing against actual anonymized customer data is genuinely necessary goes through a separate, explicitly approved process, not this agent’s default path.
What keeps real data out of it
Generated data is never derived from real customer records without a separate, approved anonymization step first. Every synthetic record is tagged clearly in every environment it reaches. Volume is capped to what the stated testing or training purpose actually needs, not generated without a defined use in mind.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Agency runs it | from $1,800 + support plan | Generator built and maintained by us, monthly coverage review | 1 to 2 weeks |
| Full control, handover-ready | from $2,800 | Same generator on your own infrastructure, documented schema, your team runs it | 2 to 3 weeks |
Running cost is usually $10 to $40 a month in model usage, depending on generation volume.
Related
See the AI agents service page and development for the surrounding build. In the same group, the QA and test agent and the prompt and model evaluation agent are the agents most likely to consume what this one generates. For a related one-time setup, see automate synthetic test data generation. The real testing discipline behind this page is the seven-channel AI sales agent case study, tested against hundreds of real and realistic conversations before launch.
Stuck testing against real customer data because generating fake data by hand is too slow? Get in touch and we will look at your schema first.
FAQ
How much does a synthetic data agent cost?
From $1,800 to build a generator for one data type against your schema, live in 1 to 2 weeks. Multiple data types or more complex distributions usually run $2,800 to $4,000.
How long before it is producing usable test data?
1 to 2 weeks. Most of that time goes into matching your actual schema and realistic value distributions, since data that looks plausible but is structured wrong defeats the purpose.
What kind of data can it generate?
Orders, user records, conversations, transactions, anything with a defined schema. It can also skew toward specific edge cases your team wants tested, a rare combination of fields, an unusual conversation path, not just typical-looking rows.
Is this really safe from a privacy standpoint?
Yes, by design. It is not derived from real customer records unless your team separately approves an anonymization step first. Even then, every record stays clearly tagged as synthetic, so it is never mistaken for real data downstream.
Where does the synthetic data end up, and can it leak into production?
It stays in the environment it was generated for: test, staging, or a training pipeline. Every record carries a visible tag identifying it as synthetic, which makes an accidental mix-up with real data easy to catch.