BlueGen.ai
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About
BlueGen.ai is a synthetic data generation platform that uses AI to create realistic synthetic data that mirrors the statistical properties of real data while preserving privacy. It addresses challenges of data privacy, security, and utility, enabling faster, cheaper, and broader access to data's value. The platform is on-premise, supporting various data formats and integrating with existing pipelines. It uses deep learning and diffusion models, optionally incorporating differential privacy for enhanced security. BlueGen.ai serves multiple industries including energy, insurance, and healthcare.
Platform
Task
Features
• same referential integrity
• same statistical distribution
• highly realistic
• your data across different locations remains on-premise through bluegen's federated learning framework
• your data is properly distributed and includes edge cases through augmentation and conditioning
• your data’s privacy is guaranteed through bluegen’s differential privacy
FAQs
What is synthetic data?
Synthetic data is artificially generated data that looks and behaves exactly like real data.
What can I do with synthetic data?
The most common use cases for synthetic data are to use it as a privacy-safe alternative to real data. For example when training machine learning models, performing exploratory analysis and collaborative research, or in your software development and testing process.
What can I not do with synthetic data?
An intrinsic property of synthetic data is that personally identifiable information isn’t inherited from the real data set. So you can not find and use specific individual records that exist in the real world.
Why should I use synthetic data?
Synthetic data overcomes your data privacy, security, and utility barriers and enables faster, cheaper, and broader access to the value hidden inside your data.
What underlying technology does BlueGen.ai use?
We use various deep learning and data processing techniques in an autonomous system that generates high-quality synthetic data. The generative core of our software runs on state-of-the-art diffusion models and is also ready to be combined with differential privacy and federated learning, to offer more privacy and secure deployments for the most demanding use cases.
What is deep learning?
Deep learning uses artificial neural networks to discover complex patterns in large amounts of data.
How does generating synthetic data with BlueGen.ai work?
1. Download and install the software in your environment 2. Import your data set into the software 3. Configure the model based on your use case requirements 4. Train the model and review its output quality 5. Generate your own synthetic data on demand
How does BlueGen.ai guarantee privacy for synthetic data?
We use both metric- and attack based evaluations to calculate the remaining privacy leakage risks in the synthetic data for singling out, linkability and inference.
What is the difference between your technology and various (pseudo)anonymization methods?
By positioning a generative model between the real and synthetic data it inherently breaks the 1-on-1 relationship between them. This is fundamentally different from (pseudo)anonymization techniques which only alter the real data in various ways and have either lower utility or higher privacy risks.
How do I know that the synthetic data is of the right quality for my use case?
In addition to our extensive default evaluation on resemblance, utility and privacy, we advise you to compare the outcomes of the real and synthetic data for various relevant domain specific business questions.
How does BlueGen.ai report the quality of generated synthetic data?
When our software is trained to generate your synthetic data, it automatically creates a comprehensible PDF evaluation report which you can easily share.
Does BlueGen.ai run in the cloud or on-premise?
The BlueGen.ai software resides in your own secure environment of choice, either in the cloud or on-premise so the real data stays safe where it is.
Can BlueGen.ai integrate with my data engineering or CI/CD pipeline?
As an add-on, the BlueGen.ai software offers various API’s to integrate with your IT environment.
How long does it take to generate the synthetic data?
Once the system is trained, BlueGen.ai can generate thousands of synthetic data rows per second.
How long does it take to train the solution to generate high-quality synthetic data?
Depending on the available computing power and amount of real data, the training process can take from an hour up to a day in the most complex cases.
Job Opportunities
Sales & Business Development Executive
BlueGen.ai generates privacy-safe synthetic data using AI, accelerating data-driven innovation.
Benefits:
Working alongside extraordinary scientists and business professionals with proven track records
Working in a dynamic environment: balancing market demands with science
Working with our innovative enterprise customers
Contributing to the introduction of state-of-the-art technology to an emerging market
Focus on personal development, both professionally and individually
Education Requirements:
Bachelor’s degree or equivalent
Experience Requirements:
Focus on new business sales
Prior sales experience gained within software or solution sales organizations
Proven track record of achieving sales goals
Other Requirements:
Proficient in Dutch and English
You are proactive, collaborative, and inquisitive, with a genuine interest in data privacy, generative AI, and data innovation
You have a start-up mentality with a can-do attitude
You are pragmatic and flexible, and possess strong communication skills
Responsibilities:
Achieving sales goals and targets within assigned industries
Developing a sales strategy with a target prospect list, and sales plan
Devising a regional strategy to leverage Bluegen.ai’s (future) partnerships
Collaborating with the marketing team to develop a marketing plan to drive pipeline
Taking a consultative approach with customers by understanding their current challenges and future strategies to position Bluegen.ai as a solution
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Software Engineer
BlueGen.ai generates privacy-safe synthetic data using AI, accelerating data-driven innovation.
Benefits:
Working in a team with extraordinary scientists and business professionals with proven track records
Working in a varied environment: balancing between the market requirements and science
Working with our innovative enterprise customers
Be part of bringing state-of-the-art technology to an emerging market
Focus on your personal development, both professionally and individually
Experience Requirements:
Relevant practical experience in Python and software development
Experience in API development and management
Strong knowledge of architecture design patterns
Understanding of network and security principles
Proficiency in Docker or similar containerization technologies
Other Requirements:
Experience deploying applications in production environments
Familiarity with machine learning libraries, preferably PyTorch
Experience with complex enterprise environments
Solid understanding of CI/CD tools and practices
You are self-sufficient and an individual contributor
You can adapt to our product vision and technology quickly
You can bring innovation to the team and the product from a technical perspective
You have a start-up and can-do mentality
Responsibilities:
translating new and sometimes experimental technology into a software product that solves real-world problems
working together with our AI Engineers implementing new algorithms for handling different data types
building solutions for complex data workflows
designing robust software architectures that can scale with real-life data
maintaining our CI/CD pipeline
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Machine Learning Engineer
BlueGen.ai generates privacy-safe synthetic data using AI, accelerating data-driven innovation.
Benefits:
Working in a team with extraordinary scientists and business professionals with proven track records
Working in a varied environment: balancing between the market requirements and science
Working with our innovative enterprise customers
Be part of bringing state-of-the-art technology to an emerging market
Focus on your personal development, both professionally and individually
Education Requirements:
You have computer science and artificial intelligence knowledge at the MSc level
Experience Requirements:
You have experience in machine learning and general programming, specifically Python
Experience with machine learning libraries, preferably PyTorch
Familiarity with deep generative models
Other Requirements:
You are self-sufficient and an individual contributor
You can adapt to our product vision and technology quickly
You can bring innovation to the team and the product from a technical perspective
You have a start-up and can-do mentality
You are pragmatic and flexible, and a communicator
Responsibilities:
translating new research into a product that solves real-world problems
implementing new models
designing data encoding schemes
running experiments
deploying optimised inference pipelines and packaging everything in a working project
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