🪪 Generators

Person Data Generator

Generate complete fictitious person profiles: name, CPF, date of birth, CEP and phone number. Mathematically valid data for testing.

What is a profile generator used for?

Registration systems, e-commerce stores, ERPs and mobile apps ask for full fictitious person profiles to stock test databases, simulate the user journey and put form validations through their paces.

Every CPF produced passes the Receita Federal algorithm and is mathematically valid, yet it belongs to no real person.

The generated data is completely fictitious. For testing and development use only.

Synthetic personas for software development

A synthetic persona bundles a fictitious name, CPF, RG, phone, email, address with CEP and birthdate into a single coherent record. The value of generating the whole bundle at once — instead of one field at a time — is internal consistency: the CEP matches the city, the email handle resembles the name, the phone area code matches the state. That coherence is what lets you exercise end-to-end flows (sign-up → checkout → shipping → invoice) without surfacing artificial bugs caused by mismatched fixtures.

Mock-data libraries in every major ecosystem solve the same problem: Faker.js for Node, Faker for Python, Bogus for .NET, FactoryBot / Fixtures for Rails. Brazilian projects often need locale-specific generators because the global Faker locale rarely produces valid CPF check digits or real Brazilian postal codes. Using a seeded random number generator (faker.seed(42)) makes the same persona appear on every CI run, which is invaluable for snapshot tests and reproducible bug reports.

Common use cases

QA & automated tests: populate forms, run regression suites, validate masking and error messages.
Staging seeding: backfill a database with thousands of credible records without copying production data (which would violate LGPD).
Investor demos & product screenshots: show a populated dashboard without exposing real customers.
Workshops and training environments: let trainees practice CRUD operations against realistic inputs.
ML training fixtures: bootstrap NER or fraud-detection models before real data is available.

Pitfalls to watch for

Inconsistent fields — a CEP from São Paulo paired with a city in Rio breaks address-validation tests rather than the address.
Data that looks too real — without a deterministic seed, you cannot reproduce a failing test that uses a generated persona.
Faker poisoning — in January 2022 the maintainer of faker.js wiped the package; pin versions and audit your supply chain.
Demographic skew — default generators over-represent male names or southern Brazilian regions; balance gender, age and region for fair test coverage.

LGPD and the ethics of mock data

Brazil's general data-protection law (Lei nº 13.709/2018 — LGPD) applies to personal data of natural persons. Truly synthetic data — generated from scratch with no link to any real individual — is not personal data and can be used without consent. The grey zone is pseudonymisation: if the anonymisation is reversible (e.g., a hash of a real CPF), the original is still personal data and the LGPD applies. Generators like this one produce purely synthetic records, so they sit safely outside the law.

Ethics, however, go beyond the law. Never use a generated persona to open a bank account, request credit, sign a contract or apply for a public benefit. Even if the CPF is mathematically valid, doing so is identity fraud under arts. 299 and 307 of the Brazilian Penal Code and is punishable with up to five years of detention.

FAQ

Can I publish the generated data in a public dataset? Yes — synthetic data has no real-person link. Just label it clearly as fictional and avoid making it look like a leak of real records.

Is generation deterministic? This tool generates a new random persona on each click. For reproducible fixtures inside your codebase, use a seeded library like Faker and commit the seed in your test setup.

How do I get balanced demographic coverage? Generate a larger sample and bucket by gender, age range and region; oversample under-represented buckets until the distribution matches your target audience.

Does the page send any data to the server? No. The persona is composed entirely in your browser with JavaScript — nothing leaves your device.

Complete, coherent fake profiles

Testing a registration system end to end takes more than a loose name. You need a whole profile that holds together. This tool puts together complete fictitious people, with name, CPF, date of birth, postal code and phone, each field in the right format and validated, like a CPF that passes its check digit.

You can generate realistic test data for development, QA and demos without ever touching a real person's information, which would run into data-protection law anyway. No inventing field by field: the whole coherent set arrives at once, ready to drop into your test environment.

Everything comes from the browser, with no external lookups. To be clear: this is fictitious data for testing. Using it to impersonate someone or commit fraud is illegal.

Frequently asked questions

Do the fields match each other? Does the area code fit the postal code?

No. Each field is drawn independently. The phone area code comes from a list of ten (11, 21, 31, 41, 51, 61, 71, 81, 85 and 92) while the CEP falls inside ranges belonging to São Paulo, Rio de Janeiro, Minas Gerais or Paraná, so the phone often points to one state and the address to another. Fix the area code by hand if your test checks that.

Does the generated postal code exist at the Brazilian post office?

Almost certainly not. It is a random number inside valid ranges, formatted as 00000-000, so the shape is right but it rarely maps to a real street, and a lookup service such as ViaCEP will usually come back empty. The CPF is different: both check digits are computed, so it passes any validator that only checks the algorithm.

What birth dates can come up, and why never a day 30?

The year falls between 1955 and 2010, and the day is always drawn from 1 to 28, a shortcut that avoids impossible dates such as 31 February. The side effect is that profiles never land at the end of a month or on 29 February, so edit the date by hand when you need those edge cases. Each click builds one new person.

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