Redefining Quality Engineering with Intelligent, Future-Ready Solutions
Empowering Quality Engineering with Intelligent Solutions for Scalable, Secure, and Next-Generation Applications.
overview
With a focus on cognitive and digital advancements, we transform Quality Engineering to meet the evolving demands of software development. By integrating intelligent automation in testing and optimizing performance and security, we refine traditional practices to ensure your applications achieve superior standards of quality, efficiency, and protection in an ever-changing digital landscape.
Key Offerings
Strategic Consulting
Test Coverage
QE Managed Services
Test Automation
Strategic Consulting
We help organizations adopt advanced technologies such as cognitive and digital solutions to strengthen their Quality Engineering. Our teams work closely with you to evaluate readiness, pinpoint areas for improvement, especially in AI-driven opportunities, and develop customized strategies that align with your business goals. By driving both shift-left and shift-right approaches, we ensure a more seamless integration into your testing ecosystem.
Test Coverage
We provide comprehensive test coverage across enterprise systems (SAP, Salesforce), cloud and infrastructure (AWS, Azure), microservices, data/ETL processes, and emerging technologies (Gen AI tools, digital platforms). Our services span functional, performance, and migration testing, ensuring robust validation across complex enterprise environments.
QE Managed Services
Our Managed Services Model leverages the latest digital tools, including cognitive technologies, to optimize and automate traditional services. Through proactive monitoring, predictive maintenance, and data-driven decision-making, we ensure high-quality service, reduced costs, and increased customer satisfaction, Domain – Verticals.
Test Automation
We deliver advanced test automation powered by cognitive and intelligent technologies. By adopting an SDET culture and In Sprint testing approach, we accelerate testing across the lifecycle, increase accuracy, and adapt to evolving needs resulting in faster testing cycles, higher-quality software, and smoother digital transformations.
Capabilities/Competencies
Cognitive and Intelligent Automation
Implement AI and ML-powered automation to drive efficiencies, reduce manual effort, and ensure continuous quality in testing processes.
Shift-Left and Shift-Right Testing
Embed testing early in the software development lifecycle and extend it post-release to ensure quality at every stage, from development to production.
End-to-End Functional Testing
Capability to perform comprehensive functional testing across enterprise systems like SAP, Salesforce, etc.
Non-Functional Testing (Performance & Security)
Skilled in testing application performance, security, and scalability using advanced monitoring tools.
Microservices Testing
Competency in validating microservices architectures, ensuring independent services work seamlessly together.
Data and ETL Testing
Expertise in testing data pipelines, ETL processes, and ensuring data accuracy in data warehousing environments.
Cloud Testing
Proficiency in testing cloud-based applications and infrastructure on platforms like AWS, Azure, and others.
Device and IoT Testing
Competency in validating applications across diverse devices, ensuring compatibility and performance in real-world conditions.
Migration Testing
Expertise in handling large-scale application and data migrations with minimal disruptions.
Digital Platforms and Gen AI Tools Testing
Competency in testing emerging technologies like digital platforms and generative AI tools for accuracy, security, and performance.
Test Data Management
Effective management of test data, ensuring its availability, accuracy, and relevance across testing phases.
Compliance and Regulatory Testing
Ensuring applications meet industry-specific regulatory standards (e.g., GDPR, HIPAA, etc.).
Performance Optimization and Tuning
Real-time performance testing and tuning to optimize resource utilization and ensure high availability under peak loads.
Predictive Analytics for Testing
Leveraging data-driven insights and predictive models to identify potential testing bottlenecks and optimize test strategies.
SDET Culture (Software Development Engineer in Test)
Building cross-functional teams with coding and testing skills for accelerated, high-quality software delivery.
Challenges & Solutions
- AI-Powered Testing Automation: Use machine learning models to predict and identify defects early in the development cycle, significantly reducing the cost and time required to fix issues later in the process. By implementing intelligent test automation, organizations can accelerate the testing process without sacrificing quality. Automated test case generation, execution, and defect prediction help in maintaining high quality while meeting tight deadlines.
- Intelligent Test Automation: Utilizing AI-driven automation to ensure rapid and accurate testing, reducing time-to-market.
- Adaptive Test Case Generation: AI enables the dynamic creation and updating of test cases in response to changing project requirements, ensuring that testing remains relevant and comprehensive as the project evolves.
- Custom AI Implementation Roadmaps: Develop strategies tailored to handle complex project needs, ensuring that AI integration is aligned with changing business goals.
- AI-Driven Defect Prediction: Use machine learning models to predict and identify defects early in the development cycle, significantly reducing the cost and time required to fix issues later in the process.
- Predictive Analytics and Insights: Leverage AI to forecast potential quality issues before they occur, allowing teams to address them proactively.
- AI-Powered Security Testing: Implement automated, AI-driven security tests that continuously monitor and detect vulnerabilities throughout the development lifecycle, ensuring that security is not compromised even as development speed increases.
- Proactive Performance Optimization: Use AI to monitor and optimize both performance and security, ensuring applications remain secure and efficient under varying conditions.
- Smart Resource Allocation: AI optimizes the allocation of resources based on real-time data and predictive models, ensuring that resources are used efficiently, reducing costs and improving project outcomes.
- AI-Infused Quality as a Service (QaaS): By offering scalable, on-demand Quality Engineering services, organizations can better manage costs while maintaining high standards of quality.
Years in QE
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Our Trusted Partners
Case Study Levers
KPIs
Automation ​Coverage​
In-sprint ​Automation​
Post – Production​ Defects​
Improve Process ​and
establish ​Testing Factory​
Realtime ​Security assessment​
Testing ​Maturity Model​
Before
25%
Longer Release Cycle
​ 10 Weeks Effort​
7%
Siloed Teams​
QEs in Isolated Programs
​
Extensive Production ​Downtimedue ​to Application Vulnerabilities ​
T & M
​Customer Managed Program​
After
Increased to 85%
Accelerated the release cycle by 40% 6 Weeks Effort
0-2%
Shared Services Model
Reduced QEs by 10% with Governance Team
Reduced to 10% Realtime Risk Scoring Incidents ​
Managed Services
​ 100% ​Infinite Accountability​
PLATFORMS / FRAMEWORKS​
- QA Verify, Test Management
- ATDD, BDD, iService
- Infinite Test Practices
- QA Shared ​Services Model​​
- Security Risk ​Scoring Model​​
- Infinite ​TCoE​​
