The cases that break an automated workflow are often the ones a team rarely sees: an unusual invoice layout, a missing attachment or a sudden change in customer behavior. Synthetic data can expand testing around these gaps, provided it is used alongside independent evidence from real operations.
On June 1, 2026, NVIDIA announced Alpamayo 2 Super and simulation tools for autonomous driving, including rare-scenario generation. That application is distinct from business analytics. Our takeaway for enterprise teams is the value of deliberately testing situations that ordinary datasets underrepresent.
What makes synthetic data useful for testing?
Synthetic data is generated information designed to represent relevant characteristics of a real process. In workflow testing, it lets a team vary one condition while keeping the rest of a case stable. That can help reveal why a system behaves differently.
For an illustrative document extraction project, take a fictional invoice and change its currency, table layout or missing-field pattern. Each variant should have an expected result, including when the correct response is to request review.
Start with a coverage gap
Generating more rows is not a useful objective by itself. Identify what your existing test collection lacks: uncommon combinations of fields, low-quality scans or a business rule that changed recently.
Describe the scenario before generating examples. Specify valid ranges, relationships and the outcome the workflow should produce. When a variation breaks a real business constraint, label it as an intentional invalid case rather than silently treating it as normal data.
Our guide to the benefits and limitations of synthetic data explains why realism and usefulness need separate checks.
Keep real validation independent
Hold back real cases that were not used to design or tune the generator. Evaluate the workflow on those cases after making improvements with synthetic examples. Otherwise, a system may simply become better at handling the generator’s assumptions.
Check performance across relevant groups and rare cases, rather than relying only on an overall score. Synthetic examples can reproduce biases or omit inconvenient exceptions just as real datasets can.
Check privacy and provenance
Generated records are not automatically anonymous. A process trained on sensitive information may reproduce identifying values or rare combinations. Review the generator, its inputs and the resulting records before sharing a dataset.
Label generated examples clearly and record which scenario produced them. Keep them distinguishable from observed customer behavior in analysis and reporting. That distinction matters when someone later uses the dataset for a different purpose.
Separate robustness tests from estimates of real performance
A synthetic collection can deliberately contain many exceptional cases. That makes it useful for stress testing, but its success rate does not estimate the success rate in production, where case frequencies differ. Report synthetic challenge results and real validation results separately.
Keep a scenario inventory showing which condition changes, why it matters and who verified the expected outcome. For generated invoices, check that amounts, dates and identifiers remain consistent unless inconsistency is the intended test. Apply the document acceptance checks and include the results in a broader AI evaluation plan.
Use synthetic data to ask better questions
A strong first project targets one known failure mode and checks whether additional scenarios improve performance on independent real cases. Explore NextBrain’s synthetic data resources and talk to our team about the coverage, validation and privacy requirements of your use case.