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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Enterprise synthetic test data solutions. It is essential that test data accurately reflects the structure of your database or application. This means it must be easy for you to model and maintain each project. Respect the referential integrity of parent/child/sibling relations across data domains within an app database or across multiple databases used for multiple applications. Ensure consistency and integrity of synthetic attributes across applications, data sources, and targets. A customer name must match the same customer ID across multiple transactions simulated by real-time synthetic information generation. Customers need to quickly and accurately build their data model for a test project. GenRocket offers ten methods to set up your data model. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce.

Description

Data serves as an essential asset for businesses today. By leveraging the right AI models, organizations can effectively construct and analyze customer profiles, identify emerging trends, and uncover new avenues for growth. However, developing precise and reliable AI models necessitates vast amounts of data, presenting challenges related to both the quality and quantity of the information collected. Furthermore, strict regulations such as GDPR impose limitations on the use of certain sensitive data, including customer information. This calls for a fresh perspective, particularly in software testing environments where obtaining high-quality test data proves difficult. Often, real customer data is utilized, which raises concerns about potential GDPR violations and the risk of incurring substantial fines. While it's anticipated that Artificial Intelligence (AI) could enhance business productivity by a minimum of 40%, many organizations face significant hurdles in implementing or fully harnessing AI capabilities due to these data-related obstacles. To address these issues, ADA employs cutting-edge deep learning techniques to generate synthetic data, providing a viable solution for organizations seeking to navigate the complexities of data utilization. This innovative approach not only mitigates compliance risks but also paves the way for more effective AI deployment.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS)
Ansible
BlazeMeter
Chef
Cucumber
Delphix
Docker
Eclipse IDE
Git
GitEye
HCL Accelerate
IBM Rational Build Forge
IBM WebSphere Application Server
Jenkins
Kubernetes
P4
Puppet Enterprise
Salesforce
Scratchpad
Stash

Integrations

Amazon Web Services (AWS)
Ansible
BlazeMeter
Chef
Cucumber
Delphix
Docker
Eclipse IDE
Git
GitEye
HCL Accelerate
IBM Rational Build Forge
IBM WebSphere Application Server
Jenkins
Kubernetes
P4
Puppet Enterprise
Salesforce
Scratchpad
Stash

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

GenRocket

Founded

2012

Country

United States

Website

www.genrocket.com/enterprise-features/

Vendor Details

Company Name

Sogeti

Country

France

Website

www.sogeti.com/services/artificial-intelligence/artificial-data-amplifier/

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