Test scenario matrix
A table of normal flows, boundary values, missing fields, duplicates, and permission scenarios, each tied to the expected application behavior.
Synthetic test data services
We create fictional records for application tests, staging environments, and demos. The files include relationships, expected outcomes, and deliberate error cases.
ScopeA set of test data files or a reusable data generator
Delivery timingA pilot data set shows the integration effort and sets the delivery schedule
How we check itWe check that the data loads, links correctly, produces the expected workflow behavior, and resets reproducibly. Looking realistic is not enough to pass.
Test the difficult cases before your users find them.
An order-processing test may need a valid order, a duplicate, and an order with a missing account. We generate those records from your schema and business rules, keeping valid and deliberately invalid cases separate. The work can include seed scripts that load the data and a way to reset the test environment.
Good fit
- Engineering teams blocked by missing or inconsistent staging data
- QA and product teams testing workflows, permissions, and exceptions
Not the right fit
- Teams that need statistically representative training data rather than test records
- Plans to copy production customer records into an unrestricted environment
What you get
Linked test data package
Versioned synthetic records with stable IDs and expected results, plus seed or import instructions for the agreed test environment.
Generation and reset workflow
Repeatable generation recipes, schema checks, controlled invalid cases, and a documented process to reset the test environment to a known state.
How it works
Review the schema and rules
We review schemas, keys, business rules, access roles, and the workflows that tests or demos need to exercise.
Build realistic test scenarios
We generate valid records and deliberately invalid ones, and keep the links between tables and the expected outcomes consistent.
Run the application’s workflows
We load the data into the agreed test environment, run representative workflows, and confirm that each reset produces the same baseline.
Examples of synthetic test data
The scenario determines the data structure and the acceptance check.
Orders and invoices
Linked customers, orders, line items, and invoices with totals that reconcile, plus separate test sets for missing purchase orders and duplicate documents.
Reservations and service requests
Fictional bookings with valid dates and linked requests, alongside date conflicts, missing preferences, and escalation cases for hospitality software.
Administrative intake
Fictional intake records with complete and incomplete forms, document versions, and routing expectations for nonclinical workflow testing.
Scope, cost, and ownership
What we need from you
- Application schemas and the test environment
- Representative workflows with expected outcomes
- Data-handling requirements and an owner for test resets
What affects cost
- Number of tables, , and cross-system relationships
- Volume, scenario coverage, and environment setup
- Seed-script integration and refresh or reset frequency
Technical scope
- JSONL, CSV, database seed files, or fixtures by agreement
- Fixed seeds so each run produces the same data, referential integrity, uniqueness rules, and isolated negative cases
Support and maintenance
We version the test data alongside application schema changes and agree who resets shared environments and refreshes demo scenarios.
Common questions
How is synthetic test data different from training data?
Test data checks software; training data teaches a model. Test records exercise validation, relationships, permissions, and failure handling. A small, fixed test set can be excellent for regression tests, which catch changes that break existing behavior. The same set may be unsuitable for model training or statistical analysis.
Can you work from a schema without production data?
Yes. We can use schemas, business rules, and fictional scenarios to build test records without copying any production rows. Your team reviews whether the cases cover the behavior that matters.
Can this include demos and performance tests?
Yes. Demo records can tell a coherent fictional story, while volume tests need appropriate sizes and workload patterns. The performance testing itself and the infrastructure to run it are scoped separately.