Frequently Asked Questions
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We test web applications, mobile apps, APIs, desktop software, and AI/ML models across a wide range of industries. Whether you’re building a startup SaaS product or an enterprise platform, we tailor our testing approach to your tech stack and business needs.
AI speeds up test creation, automates repetitive tasks, and helps us identify bugs earlier—reducing time and cost. It’s not just faster, it’s smarter. We combine AI with human QA to ensure accuracy, reliability, and complete coverage.
Yes, we offer a smart mix of both. We use automation for speed and scalability, and manual testing where human insight is essential—like exploratory testing, UX flows, and high-risk features. This balance ensures nothing is missed.
Absolutely. We work within your environment—whether you use GitHub, Jira, Jenkins, Azure, or custom tools. Our team plugs into your workflows for seamless collaboration and continuous delivery support.
It depends on your scope, but most clients see value within days. We deliver fast iterations and clear reports from the start, and our AI-enhanced testing can dramatically reduce the time needed to test each release.
Yes. We offer AI Testing Training programs to upskill your in-house QA or dev teams. You’ll learn how to use AI to generate tests, automate workflows, and boost productivity while maintaining quality standards.
We support SaaS, fintech, healthcare, education, e-commerce, and more. Our testing approach adapts to domain-specific requirements like compliance, security, and complex user scenarios.
Our advantage is in how we combine the speed of AI with the critical thinking of expert testers. We don’t just automate—we strategize, adapt, and test with context. You get scalable QA with a human touch that keeps quality real, not just algorithmic.
Our Latest Blog Update
Model Context Protocol (MCP): A Comprehensive Guide for Software Testing Teams
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integrated into modern software products, introducing new challenges for Quality Assurance (QA) teams. Ensuring the reliability and performance of AI-driven applications requires innovative approaches to testing and validation. One such innovation is the Model Context Protocol (MCP), an open standard designed to seamlessly connect AI models […]