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What is MOSTLY AI?

As customer interactions shift from physical to digital spaces, there is a pressing need to evolve past conventional in-person discussions. Today, customers express their preferences and needs primarily through data. Understanding customer behavior and confirming our assumptions about them increasingly hinges on data-centric methods. Yet, the complexities introduced by stringent privacy regulations such as GDPR and CCPA make achieving this level of insight more challenging. The MOSTLY AI synthetic data platform effectively bridges this growing divide in customer understanding. This robust and high-caliber synthetic data generator caters to a wide array of business applications. Providing privacy-compliant data alternatives is just the beginning of what it offers. In terms of versatility, MOSTLY AI's synthetic data platform surpasses all other synthetic data solutions on the market. Its exceptional adaptability and broad applicability in various use cases position it as an indispensable AI resource and a revolutionary asset for software development and testing. Whether it's for AI training, improving transparency, reducing bias, ensuring regulatory compliance, or generating realistic test data with proper subsetting and referential integrity, MOSTLY AI meets a diverse range of requirements. Its extensive features ultimately enable organizations to adeptly navigate the intricacies of customer data, all while upholding compliance and safeguarding user privacy. Moreover, this platform stands as a crucial ally for businesses aiming to thrive in a data-driven world.

What is GenRocket?

Solutions for synthetic test data in enterprises are crucial for ensuring that the test data mirrors the architecture of your database or application accurately. This necessitates that you can easily design and maintain your projects effectively. It's important to uphold the referential integrity of various relationships, such as parent, child, and sibling relations, across different data domains within a single application database or even across various databases used by multiple applications. Moreover, maintaining consistency and integrity of synthetic attributes across diverse applications, data sources, and targets is vital. For instance, a customer's name should consistently correspond to the same customer ID across numerous simulated transactions generated in real-time. Customers must be able to swiftly and accurately construct their data models for testing projects. GenRocket provides ten distinct methods for establishing your data model, including XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, and Salesforce, ensuring flexibility and adaptability in data management processes. These various methods empower users to choose the best fit for their specific testing needs and project requirements.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Microsoft Azure

Integrations Supported

Amazon Web Services (AWS)
Microsoft Azure
BlueSwan
Capgemini Intelligent Automation Platform
Chef
Cloud Foundry
Decision Moments
Docker
Eclipse IDE
Eightfold.ai
GitEye
HCL Accelerate
Mercurial
Nexus AP & Payments Automation
Puppet Enterprise
Rackspace OpenStack
Scratchpad
Stash
Vagrant
Visual Studio

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

MOSTLY AI

Date Founded

2017

Company Location

Austria

Company Website

mostly.ai/synthetic-data-platform/

Company Facts

Organization Name

GenRocket

Date Founded

2012

Company Location

United States

Company Website

www.genrocket.com/enterprise-features/

Categories and Features

Big Data

Not specified

Test Data Management

Not specified

Categories and Features

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