MOSTLY Generate

Access, create, and analyze data using an AI assistant via simple natural language input to run Python code.

Allows secure access and work with production data within your environment.

Generates high-fidelity, privacy-safe synthetic data.

Facilitates easy analysis and sharing of data across teams.

Platform is built with agentic data science at its core to accelerate AI innovation.

Enables organizing, managing, and collaborating on shared assets with a team.

Scalable and secure deployment options on Kubernetes, OpenShift, or a VM.

Ability to create privacy-safe synthetic data and share it globally.

Designed for ease of use for everyone, from beginners to experts.

Accelerates AI workloads by creating necessary data for teams.

A fully permissive Apache v2 licensed SDK for local synthetic data generation.

Powers synthetic data generation for high fidelity and built-in differential privacy.

Enables rapid training of synthetic data generators.

Supports sophisticated sampling techniques for synthetic data.

Handles complex tabular and textual datasets.

Creates synthetic data locally within your Python environment, keeping data in your control.

Exports Generators from the SDK and uploads them to the MOSTLY AI Data Intelligence Platform for exploration and sharing.

Proprietary algorithms ensure the highest accuracy in synthetic data, acting as a seamless drop-in replacement.

Anonymizes original data, learns patterns without re-identification risk, prevents overfitting, and safeguards against outliers.

Provides comprehensive reports on synthetic data quality, including univariate and bivariate distributions and correlations.

Synthesizes data containing events over time, such as customer behavior and transaction data, with high quality.

Works with numerical, categorical, date-time variables, and other structured data.

Defines and maintains relationships between tables (e.g., customer-to-transaction) for data coherence.

Adjusts variable distributions in synthetic datasets to diverge from original data for specific use cases or upsample minority classes.

Synthetically imputes missing data points using Generative AI for statistically appropriate and contextually relevant values.

Seamlessly integrates with existing data storage sources (e.g., direct query access, direct write access for connectors, AWS infrastructure, Databricks, Snowflake, BigQuery, PostgreSQL, Apache Hive, MariaDB).

Provides a self-contained helm-chart for installation on Kubernetes clusters.

Can be installed via Minicube on a Single VM if no cluster is available.

Provides programmatic access to platform features, including table schema data and live probing of generators.

Allows for generating synthetic text based on specified conditions.

Automatically identifies text columns for synthesis.

Enables saving AI Assistant conversations as notebooks.

Provides a visual representation for object storage.

Works with any S3-compatible storage.

Facilitates searching for generators, synthetic datasets, and connectors.

Continuously enhanced algorithms for better synthetic data quality.

Ongoing improvements in privacy safeguards.

Offers granular control over rebalancing synthetic data.

Allows generating synthetic samples based on specific seed values.

Enables exporting and importing generators as unencrypted ZIP files.

Follows semantic versioning for software releases.

Includes a modernized user interface for improved ease of use.

Ability to create mock data specifically for software testing applications.

Moves beyond traditional anonymization by creating statistically similar synthetic data.