How does AI improve software testing and QA?

AI improves testing through intelligent automation, predictive defect detection, self-healing test scripts, and faster validation across development pipelines.

Why do enterprises invest in quality engineering services?

Enterprises invest in quality engineering to improve product reliability, accelerate releases, reduce defects, and ensure performance across digital platforms.

Which industries benefit from AI-driven QA and testing?

Healthcare, fintech, MedTech, SaaS providers, ISVs, and enterprise technology organizations benefit from AI-powered quality engineering and continuous testing.

Do you support continuous testing and DevOps quality integration?

Yes. Innominds integrates automated testing, DevOps pipelines, performance engineering, and quality transformation to support continuous delivery.

Intelligent Quality Engineering | Automated Testing Solutions

Q: What is AI-powered quality engineering?

AI-powered quality engineering uses machine learning, automation, and analytics to improve software testing, reliability, and release quality.

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Streamline Testing with AI and Scriptless Methods

Mastering the complexities of large enterprise applications and workflows is essential for quality engineering, especially when digitizing and automating enterprise processes. Prioritizing applications that integrate connected devices, cloud, data and apps can give you a competitive edge.

Innominds’ AI-powered quality engineering approach enables you to deliver exceptional digital experiences that captivate users and drive business success. By leveraging cutting-edge AI technologies and fostering a culture of continuous improvement, we ensure your products are market-ready and of the highest quality. Innominds revolutionizes quality engineering with AI and a scriptless methodology. By integrating GenAI and promoting human-AI collaboration, we significantly enhance various QE functions.

Outcomes Delivered: Enhanced Efficiency and Superior Quality

Accelerated Time to Market

Our faster, more efficient testing cycles ensure your products reach the market sooner, allowing you to capitalize on time-sensitive opportunities.

Reduced Total Cost of Ownership

By minimizing defect seepage and focusing on proactive measures, we significantly improve software quality and reduce overall quality engineering costs.

Boosted Productivity

AI automation cuts down test script creation time by up to 80%, freeing your team to focus on strategic testing initiatives. With minimal human involvement, we reduce the chances of human error and ensure consistent, reliable results.

Enhanced Maintenance Efficiency

Embracing scriptless testing reduces the burden of script maintenance by up to 60%, allowing your team to develop and implement high-level testing strategies. Ensure Complete Coverage: AI helps create comprehensive testing scenarios, providing complete coverage and identifying potential issues early in the development cycle.

360° Coverage

AI helps create comprehensive testing scenarios, providing complete coverage and identifying potential issues early in the development cycle.

Our Offerings

AI-powered Test Automation

Enhance test coverage, reduce costs, and speed up time-to-market with AI-powered quality management. Integrate TDD/BDD methodologies for comprehensive full-stack efficiency.

AI-powered Test Automation

User Experience Excellence

Guarantee seamless omnichannel and customer journey experiences through extensive testing across connected devices, enterprise applications, and data.

User Experience Excellence

Testing Center of Excellence (CoE)

Leverage our virtual lab and dedicated CoE to ensure flawless functionality across all devices.

Testing Center of Excellence (CoE)

“We estimate that as much as 90 percent of the failures in ML development come not from developing poor models but from poor productization practices and the challenges of integrating the model with production data and business applications, which keep the model from scaling and performing as intended. Effective productization requires developing an integrated set of components to support the model (or, often, set of models) such as data assets, ML algorithms, software, and user interface.” - McKinsey & Company

Success Stories

Transformed the QA Function, Increased Test Coverage by 90% and Cut Testing Costs by 27%

Innominds implemented an end-to-end QA transformation—setting up functional testing, performance testing, automation testing, upgradation and patch testing, mobile testing, performance testing, security testing, SIT, and UAT.

End-to-End Quality Engineering Services for Retail MNC Help Save Up to 40% in Testing Costs

The client's customer satisfaction index increased with quick implementations and strong frameworks. The performance metrics and suggestions gave insights into the application's overall performance and usage statistics.

View all case studies

Maximize Coverage, Minimize Maintenance

Harness the power of automated testing with iHarmony, our comprehensive, open-source AI platform. Seamlessly create and execute tests across various platforms, including web, mobile, APIs, desktops, and mainframes. iHarmony’s AI-powered approach simplifies test creation and ensures efficient execution through parallel processing. Achieve faster development cycles, broader test coverage, and reduced costs with our innovative open-source solution.

Explore iHarmony™

FAQs

What is AI-powered quality engineering?

AI-based quality engineering uses machine learning, smart automation, and quality systems to make software testing, performance checks, and product releases better. By utilizing AI-based insights, organizations can improve their ability to detect defects in their products while ensuring quality. This approach enables enterprises to improve their development cycles while maintaining quality in their products.

How does Innominds enable intelligent test automation?

Innominds makes test automation smarter by using AI-based testing systems, quality frameworks, and automated testing that work well with DevOps. These capabilities can automate repetitive test cycles while improving the efficiency of application performance and functionality validation.

How does continuous testing improve release cycles?

Continuous testing helps organizations identify defects at early stages in the software development process. As a result, defects can be fixed quickly, and the risk of failure during deployment can be avoided. Continuous testing helps organizations improve their software deployment cycles and deliver high-quality software to end users.

How can enterprises reduce QA costs using AI-driven testing?

Software testing with AI helps organizations save on the overall cost of testing by automating testing activities and minimizing human intervention in the testing process. AI helps organizations improve the overall efficiency of testing and identify defects quickly. As a result, organizations can save on operational costs while maintaining high software quality.

When should organizations adopt quality engineering transformation?

Organizations can choose quality engineering transformation when they face issues like slow development, quality of the product, or increased operational costs with traditional testing methods. Changing to AI-driven quality engineering can help organizations scale testing, improve the reliability of releases, and speed up digital product development.

How do you integrate QA with DevOps and CI/CD pipelines?

QA can be achieved by integrating DevOps and CI/CD with automated testing frameworks, continuous integration, and real-time performance monitoring. With this approach, organizations can deliver faster releases with consistent quality and performance of the developed software systems.

Clients

Our AI-driven Digital Quality Engineering services create tangible outcomes for leading global enterprises.

Strategic Technology Partners

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