Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • MuukTest Reviews & Ratings
    29 Ratings
    Company Website
  • Testsigma Reviews & Ratings
    65 Ratings
    Company Website
  • Parasoft Reviews & Ratings
    120 Ratings
    Company Website
  • LambdaTest Reviews & Ratings
    2,246 Ratings
    Company Website
  • NeoLoad Reviews & Ratings
    360 Ratings
    Company Website
  • Kualitee Reviews & Ratings
    169 Ratings
    Company Website
  • Sauce Labs Reviews & Ratings
    181 Ratings
    Company Website
  • qTest Reviews & Ratings
    Company Website
  • Safetica Reviews & Ratings
    356 Ratings
    Company Website
  • Boozang Reviews & Ratings
    14 Ratings
    Company Website

What is Distributional?

Traditional software testing is predicated on the idea that systems will act in expected manners. However, AI systems frequently demonstrate unpredictability, uncertainty, and inconsistencies, which can pose serious risks for products that incorporate AI technologies. To confront these hurdles, we are developing an innovative platform specifically aimed at the testing and assessment of AI, with the goal of improving safety, resilience, and reliability. It is crucial to ensure that your AI solutions are trustworthy prior to their launch, and it is equally important to uphold that trust over time. Our team is diligently enhancing the most extensive enterprise AI testing platform now available, and we are enthusiastic about receiving your feedback. By registering, you can access our prototypes early and help shape the future direction of our product development. We are a passionate team focused on solving the intricate challenges of AI testing at an enterprise level, drawing inspiration from our valued customers, partners, advisors, and investors. As AI capabilities continue to grow in various business functions, the resultant risks for these enterprises and their customers are also on the rise. With fresh reports surfacing daily that bring attention to concerns such as AI bias, instability, and errors, the demand for effective testing solutions has reached an unprecedented level. Meeting these challenges is not merely an objective; it is essential for the responsible advancement of AI technologies. The commitment to address these complexities will ultimately pave the way for enhanced trust and reliability in AI applications across industries.

What is ContextQA?

ContextQA is a cutting-edge solution aimed at empowering organizations to enhance their automation testing processes, elevate software quality, speed up product release schedules, and significantly lower the expenses related to upholding software standards by utilizing AI-driven SaaS technology. By converting manual test cases and user narratives into automated tests, AI agents optimize the testing workflow. In addition, ContextQA collects evidence and performs root-cause analysis when issues arise, effectively pinpointing critical user experiences and revealing weaknesses in the software testing framework. With its thorough end-to-end testing capabilities, including contract tests, there is no longer a need for separate tools for testing the front end and back end, facilitating a more cohesive testing environment. This solution not only identifies problems and enhances performance but also guarantees seamless user experiences across different browsers, mobile devices, and operating systems. Moreover, ContextQA simplifies the integration of test cases, allowing for a rapid expansion of automation coverage for your products and services, ultimately driving increased productivity and operational efficiency. The innovative approach of ContextQA ensures that organizations stay competitive in a fast-paced technological landscape.

Media

Media

Integrations Supported

AWS CodePipeline
Azure DevOps Projects
Bitbucket
GitHub
Google Chrome
Jenkins
Jira
Microsoft Edge
Mozilla Firefox
Safari
Salesforce
Slack

Integrations Supported

AWS CodePipeline
Azure DevOps Projects
Bitbucket
GitHub
Google Chrome
Jenkins
Jira
Microsoft Edge
Mozilla Firefox
Safari
Salesforce
Slack

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Distributional

Company Website

distributional.com

Company Facts

Organization Name

ContextQA

Date Founded

2022

Company Location

United States

Company Website

contextqa.com

Categories and Features

Categories and Features

API Testing

Functional Testing
Fuzz Testing
Load Testing
Penetration Testing
Runtime and Error Detection
Security Testing
UI Testing
Validation Testing

Automated Testing

Hierarchical View
Move & Copy
Parameterized Testing
Requirements-Based Testing
Security Testing
Supports Parallel Execution
Test Script Reviews
Unicode Compliance

Mobile App Testing

Functional Testing
Installation Testing
Interruption Testing
Memory Testing
Performance Testing
Usability Testing

Web Accessibility Testing

ADA Compliance
Alerts / Notifications
Automated Testing
Mobile Accessibility Testing
Ongoing Accessibility Monitoring
Reporting / Analytics
Visualize Accessibility
WCAG Compliance

Popular Alternatives

Popular Alternatives

Testim Reviews & Ratings

Testim

Tricentis
Stellar Reviews & Ratings

Stellar

Vstellar