PROJECTS
Synthetic test data for systems that fail on tidy demos.
generate-data.com is a synthetic test data platform for teams evaluating matching, scoring, and other intelligent systems. Unlike generic faker tools, you can configure quality characteristics such as exact duplicates, fuzzy duplicates, and realistic field-level variation, so test datasets look like the messy production inputs your models and pipelines will see. That makes regression suites and QA datasets honest about failure modes tidy unique rows would hide.
The problem it solves
Most synthetic generators invent clean, unique rows. Matching systems, quality scorers, and evaluation harnesses fail on duplicates, typos, and field-level drift. If your tests never inject that mess, they pass while production still breaks. generate-data treats input quality as configuration, not an afterthought script.
Faker-style libraries invent plausible unique people. generate-data.com lets you dial in exact duplicates, fuzzy duplicates, and per-field quality so matching and evaluation tests exercise the same failure modes you will see in production. For a deeper take, read Why Faker fails entity resolution tests.
Who it's for: Engineers and researchers testing matching and quality systems. QA teams building regression datasets for intelligent pipelines.