The most magical synthetic data generator there is
How it works
The following explanation illustrates how our product works behind the scenes. Despite the simple user interface, the backbone is extremely sophisticated and complex.
Input
User parameters for the generation
Parameters
Our system takes into consideration a variety of parameters when generating fake data to ensure that the resulting output is as similar to the real data as possible.
The user can enter the following parameters:
- Context (optional): the business context;
- Topic: the schema reference argument. Example: (User, Car, Motorcycle, Apartment, Trip etc.. );
- Language: the language for which to obtain the translated data. Our system contains data in the following languages: English, Italian, French, German and Spanish;
- Numbers of records: the number of rows to generate;
- Schema: the structure of the fake data.
Process
The "magic" we are doing
Engine
Our system takes user-defined parameters into account to generate fake data;
The parameters taken as input will be used by our AI engine to identify the context, the topic and the language of the data that the user wants to obtain;
Once the dataset to be used has been identified, the properties and related types of the schema will be analyzed to ensure the truthfulness of the information produced;
Data processing time may vary based on the size of the schema and the number of records requested;
At present, our AI models are unable to generate data while guaranteeing consistency between the various properties of the schema; it is in our interest to resolve this critical issue.
Output
The results we offer
Result
Once the data has been processed, a json array of length equal to the number of records requested will be returned with a structure defined in the schema.
The returned data can be used according to various needs.
The main use cases can be:
- Testing: the data obtained can be used to prepare tests to ensure the quality of your app;
- Demo app: the data obtained can be used to present a demo app to stakeholders;
- Database population: the data obtained can be used to populate the database with truthful data;
- Data analysis: the data obtained can be used to perform a simple data analysis.
Try now
Start now! Generate your first data in seconds.
Features
Simplify development and QA with realistic, ready-to-use, and highly configurable fake data.
AI Engine
Our Artificial Intelligence engine analyzes the input data to generate realistic data. Our AI models are trained on large and diverse datasets.
Real data
Our system stands out from the others for the quality and the authenticity of the data produced which can be used for various cases of use.. They will be useful for making presentations of demo apps or for populating the database under testing.
Schema preset
There are a number of pre-set schemas divided by topic and context to facilitate data generation. Furthermore, the user has the possibility to save his own schemes for future use.
Qa tests
The veracity of our data allows us to carry out quality tests, in order to improve the QA of your application. It will also avoid entering data manually and will improve the speed of writing tests.
Translated data
The possibility of obtaining multilingual data allows a certain flexibility in the context of use. Currently managed languages are: English, Italian, French, Spanish and German.
Api documentation
Our Api documentation defines the various conventions to be respected in order to interact with our system. To facilitate integration, you can take advantage of our sdk present on npm.
Dataset
The heart of the system is powered by a large and varied collection of data of different types.
In order for the AI system of Magikfake to accurately and consistently extract data, it performs a meticulous analysis of the available datasets.
This process is crucial for the correct association of target schema fields with the relevant information within the data sources.
Artificial intelligence is not limited to simple mapping but actively seeks the correct semantics, ensuring the integrity and usability of the extracted data. The datasets managed by Magikfake are extensive and diverse, covering a vast range of industrial and thematic sectors.
These multi-sectoral data categories include, but are not limited to, the domains of Healthcare (health and medical data), Finance (banking and financial sector), Retail (retail trade and logistics), Manufacturing (production and engineering), Energy (energy and resources), and Governmental/Public Sector (public and administrative data).
This variety is essential to support a wide range of extraction and analysis scenarios.
Mission
Creating the largest engine for generating real data fake using artificial intelligence
Our goal is to develop and implement a real fake data generation engine based on artificial intelligence, which will stand out for the quality and authenticity of the generated data.
We will use artificial intelligence models trained on large and diversified datasets, to ensure the representativeness and reliability of the information produced.
Our technology will be able to generate realistic and meaningful data, based on the generation parameters set by the user.
Furthermore, we will endeavor to ensure the privacy and security of the data generated.
Through our engine we aim to provide a useful tool for software developers to generate data according to their usage needs.
Roadmap
We have many evolutions in mind to make our project unique. Check out the roadmap
2026
Dataset Collection Expansion: Extend the real-world dataset catalog to cover over 500 topics, optimizing data generation quality.
Improve Generated Data Consistency: Refine the analysis of input parameters to ensure more accurate data generation, consistent with specifications.
Data Enrichment with Real Images: Associate real image links with relevant schema fields for a more complete visual context.
New Schema Validations: Implement new validation mechanisms for different field types, directly responding to user feedback and needs.
Java SDK Release: Development and launch of a Software Development Kit (SDK) in Java for integration.
Api
Integrate Magikfake directly into your own products and platforms.
Magikfake is easy to integrate using our API. Through our APIs it is possible to generate truthful fake data and manage the schemas.
Our API have predictable resource-oriented URLs, accept form-encoded request bodies, return JSON-encoded responses, and use standard HTTP response codes.
To take advantage of our APIs, you need to register to view the associated API KEY to authenticate requests.
We have also prepared an SDK to facilitate integration. You can download the library from npm using the following command: npm install magikfake@beta
Consult the docs to interact with our system.
Pricing
Our plans are designed to meet the requirements of both beginners and professionals. Get the right plan that suits you.
Free
€
0
/Month
25.000 tokens/month
to generate fake data included
( no additional token )
- AI engine
- Multilanguage data
- Max records number: 20
- Schema management
All prices are in EUR. All payments are handled securely by stripe
Contact us
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