MostlyAI
The MOSTLY AI Assistant is an advanced tool designed to generate high-quality, privacy-safe synthetic data for various applications. Using AI-powered technology, it allows organizations to create synt
What is MostlyAI?
MostlyAI generates synthetic datasets that preserve statistical properties and relationships from your original data without exposing sensitive information. It's designed for organisations that need to share data internally, train machine learning models, or run testing without privacy risks. The tool uses AI to create realistic synthetic records that maintain the patterns and correlations of real data whilst removing personally identifiable information. This makes it useful for teams working across data governance, development, and analytics who need usable datasets without compliance concerns.
Key Features
Synthetic data generation
Creates statistically representative copies of datasets whilst removing sensitive information
Privacy-safe sharing
Enables teams to collaborate on data projects without exposing real personal or confidential records
Python client support
Integrates with existing data pipelines and workflows through Python libraries
Multi-purpose application
Supports data sharing, ML model training, QA testing, and self-service analytics
Educational resources
Provides blogs, videos, and reference materials to help teams understand synthetic data concepts
Pros & Cons
Advantages
- Removes privacy concerns when sharing datasets across teams or external parties
- Speeds up development and testing workflows by providing ready-to-use synthetic data
- Maintains statistical relationships and patterns from original data, making synthetic datasets realistic
- Available on a freemium model, allowing teams to try the tool before committing to paid plans
Limitations
- Synthetic data may not capture all edge cases or rare patterns from the original dataset
- Requires understanding of your data structure to configure generation effectively
- Organisations handling highly sensitive or unusual data types may need custom implementation support
Use Cases
Sharing customer or transaction data with development teams without exposing real identities
Creating training datasets for machine learning models when real data is limited or restricted
Running thorough QA and testing scenarios without using production data
Enabling self-service analytics and reporting where business users need realistic data to experiment with
Accelerating data pipeline development by providing immediately available test datasets