MOSTLY AI
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About
MOSTLY AI is a synthetic data platform that helps organizations generate high-quality, privacy-safe synthetic versions of their datasets. It uses advanced generative AI models to create synthetic data that closely mirrors the statistical properties of real data, without containing any personally identifiable information (PII). This allows for safe data sharing, collaboration, and training of AI/ML models. The platform offers a free plan with limited usage and various paid plans with increased credits for larger datasets. The company values transparency and has a publicly available company handbook.
Platform
Task
Features
• natural language interface
• synthetic data generation
• data anonymization
• data sharing
• python client
• software testing
• ai/ml model training
• privacy preservation
FAQs
What is synthetic data, and how does it differ from real-world data?
Synthetic data is artificially generated, not collected from real-world events. It's often used when real data is hard to get or privacy is a concern. Modern synthetic data uses AI to create complex, realistic data that mirrors real data's statistical properties, but without PII.
How is synthetic data generated, and what techniques are involved?
Common methods include statistical/rule-based approaches, generative models (like GANs and VAEs), agent-based modeling, and simulation. MOSTLY AI uses Generative Models/Machine Learning.
Where is synthetic data used, and what are its main benefits?
Synthetic data is used for AI/ML model training (especially with scarce or sensitive data), data sharing while preserving privacy, and software testing. Key benefits include enhanced privacy, improved data availability, control over data characteristics, and reduced bias.
Which industries currently benefit most from synthetic data, and why?
Finance, insurance, healthcare, retail, and the public sector benefit most. These industries often deal with sensitive data or have difficulty obtaining sufficient real-world data.
How do you ensure the quality and realism of synthetic data?
To ensure quality and realism, we validate synthetic data against real data by comparing statistical properties and testing model performance (Train Synthetic, Test Real).
What are some potential risks or challenges when using synthetic data?
Risks include inaccurate representation of real-world data, model overfitting to synthetic data, and underperformance on real data. High-quality synthetic data generation tools minimize these risks.
How does synthetic data solve data-sharing challenges in light of data privacy regulations?
Synthetic data helps address data-sharing challenges by allowing organizations to share data with statistical properties preserved but without sensitive PII. This is key for compliance with regulations such as GDPR and CCPA.
What measures can be taken to ensure that synthetic data does not reveal sensitive information?
To guarantee privacy, we use techniques like GANs and VAEs to generate synthetic data that resembles real data statistically without direct replication. Re-identification tests help validate that no sensitive information is revealed. Strong governance and oversight are also essential.
How does using synthetic data affect AI model performance in real-world settings?
The impact depends on the quality of the synthetic data. High-quality synthetic data can lead to models performing similarly to models trained on real data, sometimes even better due to balanced representation. Poor-quality data leads to poor real-world performance.
What are the downsides of using synthetic data to train AI models?
A major downside is that synthetic data does not represent real individuals or events. Insights from synthetic data need to be transferred back to the real world, and discrepancies between synthetic and real-world performance can occur if the synthetic data isn't accurate.
How does synthetic data facilitate data sharing and innovation?
Synthetic data allows sharing without privacy risks, fostering collaboration on research projects and open data initiatives. This allows for faster advancement and more robust solutions compared to working with limited datasets.
How can synthetic data help reduce bias in AI?
Synthetic data can help mitigate bias by generating datasets that represent all classes or populations accurately. It also helps in creating data for rare scenarios and analyzing the impact of bias in models.
How can we prevent synthetic data from amplifying existing biases?
To avoid amplifying bias, ensure the synthetic data generation process accurately represents the original data's characteristics, including biases. Verify this by comparing the original and synthetic datasets and the performance of models trained on each.
What steps promote transparency and accountability when using synthetic data in AI systems?
Transparency involves documenting data generation, stating synthetic data use in reports, and validating that no sensitive information is present.
How does synthetic data support responsible and transparent AI?
Synthetic data enhances responsible AI by ensuring data privacy, improving transparency through documentation, promoting robustness and fairness through diverse testing, and enabling replicability in research.
Pricing Plans
Team
$3.00 / per credit• Increased data generation capacity
• Cloud Marketplace deployment
• KeyCloak/ActiveDirectory authentication
• Email/Dedicated support
• API & Python client
Enterprise
$5.00 / per credit• Dedicated customer success team
• Synthetic data superuser training
• Cloud Marketplace or Custom Deployment
• KeyCloak/ActiveDirectory authentication
• Email/Dedicated support
• API & Python client
Job Opportunities
General opportunity
MOSTLY AI generates high-quality, privacy-safe synthetic data for AI/ML, data sharing, and testing. Free and paid plans available.
Benefits:
Remote work
Phantom Stock Option Plan
Home office support (€/$1000 + €/$300 yearly)
Health coverage
Lunch subsidy (€/$100 monthly)
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Ratings & Reviews
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