Our Synthetic Data Solutions

Next-gen artificially generated data delivered on time and according to the best industry practices

What we can deliver

Customizable data generation

Your synthetic data aligned with the target application or domain and based on specific requirements.

Data privacy and security

We use privacy-preserving techniques, you work with data without exposing sensitive information.

Data anonymization and de-identification

Anonymized personally identifiable information for privacy preservation and compliance with regulations.

Realistic data representation

Capturing the nuances, distributions, and correlations present in actual datasets.

Accelerated model training

Ready-to-use and high-quality datasets, to expedite AI model training and development.

Quality assurance

Rigorous quality assurance checks to minimize errors and data discrepancies

Data augmentation techniques

To enhance existing datasets, improve model generalization and performance.

Flexible and scalable solutions

From small research experiments to large-scale AI applications.

Domain-specific solutions

Retail? Healthcare? Finance? Other? We generate data for specific industries

Embrace the Advantages

Secure, flexible, and privacy-preserving

Diverse, as-good-as-real,

and available data 

We generate synthetic data to represent a wide range of scenarios and edge cases that may be challenging to encounter in real-world data. This enables you to test models in diverse situations, improving their robustness and performance. 

Ensured data security and privacy 

Work with artificial datasets that do not contain any real-world sensitive information. Protect your sensitive customer data and minimizes the risk of data breaches or leaks during development and testing. 

Accelerated model generalization and development

By providing readily available, annotated, and diverse datasets, synthetic data expedites AI and ML model training and testing phases. Additionally, augmenting the real data with synthetic data enhances the ability to generalize to unseen data for more accurate predictions and outcomes. 

Efficient costs and time distribution

Acquiring and managing large-scale real-world datasets can be expensive and time-consuming, making our fake data a cost-effective solution that reduces the reliance on expensive data collection efforts. 

Improved data quality and augmentation

We design synthetic data with quality and consistency, ensuring it is free from data entry errors and inaccuracies. By utilizing synthetic data to augment existing real-world datasets, you can increase their size and variety, which, in turn, enhances model performance. 

A solution for data scarcity

In cases where acquiring adequate real-world data is difficult due to scarcity or privacy concerns, we can offer you synthetic data for effective data synthesis. This enables you to train and test your models effectively. 

Tools we utilize

Our commitment to providing synthetic data for AI relies on employing advanced and effective synthetic data generation tools.

Generative Adversarial Networks (GANs)

These powerful deep learning models consist of a generator and a discriminator, working in tandem to create diverse and highly realistic synthetic datasets across various domains through a competitive process

Variational Autoencoders (VAEs)

VAE is a synthetic data generator tool that employs encoder and decoder architectures, introducing probabilistic elements and generating synthetic data with variations while preserving crucial statistical properties.

Data augmentation utilities

Data augmentation techniques enrich existing datasets with synthetic data variations to enhance the size and diversity of training data and improve the generalization capabilities of AI models.

Data privacy and anonymization modules

Incorporating advanced anonymization techniques, we protect sensitive information in synthetic datasets while maintaining the data’s statistical properties without compromising privacy.

Domain-specific data generation

Customizable for various industries and use cases, our tools create synthetic data tailored to match the unique characteristics of each domain — be it healthcare, finance, manufacturing, retail, cybersecurity, entertainment, or NLP.

Quality assurance frameworks

Our quality assurance frameworks thoroughly validate the generated datasets, ensuring they are reliable and free from inaccuracies and data entry errors.

good to know

We employ synthetic data to evaluate data anonymization techniques and ensure the privacy of real data is well-maintained.

Insights

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    FAQ

    Left wanting more? Fast-track your understanding of Synthetic Data possibilities with our quick insights.

    What is synthetic data generation?

    Synthetic data generation is the process of creating artificial data that mimics the statistical properties and patterns of real-world raw data. This generated data is not collected from actual observations but is instead produced using algorithms, mathematical models, or other computational techniques. The purpose of synthetic data generation is to use this artificial data for various applications without exposing sensitive or private information present in real datasets. 

    How do you generate synthetic data for machine learning?

    The process of generating synthetic data involves understanding the underlying patterns and distributions of the actual data. Generating synthetic data for machine learning uses algorithms and statistical techniques to create artificial data that resembles the characteristics of real-world data. 

    What are the most common techniques for generating synthetic data?

    The most common techniques for generating synthetic data include: 

    • Generative Adversarial Networks (GANs) — GANs are a popular approach for generating synthetic data. They consist of two neural networks: a generator and a discriminator. The generator generates synthetic data, while the discriminator tries to distinguish between real and synthetic data. The two networks are trained together in a competitive process until the generator produces data that is indistinguishable from real data. 
    • Variational Autoencoders (VAEs) — VAEs are another type of generative model used for synthetic data generation. They are based on autoencoders, which consist of an encoder and a decoder. The encoder compresses the input data into a latent space, and the decoder reconstructs the data from the latent space. VAEs introduce probabilistic elements, allowing them to generate new data points from the learned latent space. 
    • Data augmentation — While not strictly generating entirely new data, data augmentation techniques modify existing data to create variations. Common augmentations include rotations, translations, flips, changes in brightness or contrast, and adding noise. Data augmentation is often used to increase the size of the training dataset and improve model generalization. 
    • Interpolation and extrapolation — For structured data, interpolation techniques like linear interpolation can be used to generate synthetic data points between existing data points. Extrapolation can be used to extend the data distribution beyond the observed range. 
    • Monte Carlo simulation — Monte Carlo methods involve using random sampling to simulate complex systems. In the context of synthetic data generation, Monte Carlo simulations can be used to model uncertain or probabilistic data points based on known distributions. 
    • Copula Models — Copulas are statistical models used to describe the dependence structure between variables. They can be used to generate synthetic data that preserves the correlation structure of the original dataset. 
    • Rule-based systems — In some cases, synthetic data can be generated using rule-based systems that capture specific domain knowledge or patterns. For example, synthetic data for a customer database could be generated based on specific rules and distributions of customer attributes. 
    • Mixing real data — In certain situations, you can combine real data from different sources or datasets to create synthetic data. This method is particularly useful when dealing with data privacy concerns, as it ensures no individual’s data is entirely present in the synthetic dataset. 

    These techniques address data privacy, scarcity, create representative training datasets, and test algorithms/models in different scenarios. The choice of technique depends on the specific use case, data type, and generation goals. 

    What are examples of synthetic data generation providers?

    There were many commercial and academical synthetic data generation providers and platforms that offered solutions to generate synthetic data for various use cases. Some of the most popular include: 

    • OpenAI — The organization behind the GPT-3 language model has been actively working on research related to synthetic data generation and privacy-preserving AI. 
    • DataGen — DataGen is a provider of synthetic healthcare data. They specialize in generating realistic and representative synthetic medical data for research and analysis purposes while ensuring patient privacy. 
    • BizDataX — BizDataX is a data masking and synthetic data generation tool that aims to protect sensitive data and ensure data privacy. It provides capabilities for creating synthetic datasets that closely resemble real-world data for various industries. 
    • Synthetic Data — The company focuses on generating synthetic data for various industries, including finance, insurance, retail, and healthcare. They use advanced algorithms to create data that retains the statistical properties of real data. 
    • Safeguard Cyber — Safeguard Cyber offers synthetic data generation for training machine learning models used in cybersecurity applications. Their platform helps improve the accuracy and robustness of AI-driven security systems. 

    How is synthetic data used for NLP?

    Synthetic data is used in Natural Language Processing (NLP) to enhance the performance of NLP models and address various challenges, including data scarcity, privacy concerns, and model performance. Here are common applications of how synthetic data offers advantages in NLP: 

    • Data augmentation — Increasing training dataset size and prevent overfitting. 
    • Privacy preservation — Protecting sensitive information while enabling research. 
    • Domain adaptation — Simulating specific language domains for specialized tasks. 
    • Text generation and language models — Training large language models for human-like language understanding and generation. 
    • Sentiment analysis — Improving translation models using synthetic parallel corpora. 
    • Machine translation — Generating labeled datasets for training sentiment analysis models. 
    • Dialogue systems and chatbots — Enhancing chatbot training with simulated conversation scenarios.