In today’s competitive markets, delivering high-quality products that ensure a superior customer experience rapidly is quintessential. Traditional testing methods struggle to keep pace with continuous release cycles, complex architectures, and the growing demand for seamless user experiences across platforms. Hence companies are now leveraging AI-driven automation to bridge this gap. The Global AI in Software Testing Market size is expected to be worth around USD 10.6 Billion by 2033, from USD 1.9 Billion in 2023, growing at a CAGR of 18.70% during the forecast period from 2024 to 2033. (Source: market.us)
eInfochips has developed AI Studio, an advanced multi-agent, AI-driven testing platform designed to automate multiple stages of the software testing lifecycle. AI Studio minimizes manual intervention, accelerates project timelines, and enhances overall testing efficiency and accuracy by leveraging autonomous agents and cutting-edge AI techniques.
Utilize AI-powered document processing to analyze requirement documents, automatically identifying what needs to be tested.
Convert analyzed requirements into well-structured user stories with clearly defined acceptance criteria, ensuring alignment with development teams.
Automatically generate comprehensive test cases from user stories to guarantee thorough coverage and robust quality assurance.
Generate Playwright and Selenium automation scripts from recorded test workflows and captured elements.
Empower AI agents to discover APIs, construct requests, and validate responses, streamlining end-to-end API testing.
Leverage AI-driven multi-agents to intelligently generate and execute mobile test cases across Android and iOS platforms.
Capture manual testing workflows to record user interactions and convert them into automation-ready test scenarios.
Automatically verify and validate web functionality across multiple languages and locales to ensure global readiness.
Analyze test results with AI-driven insights and generate professional reports featuring detailed metrics and quality analytics.
Generate framework-ready test cases that are reusable, well-structured, and aligned with your automation standards.
Achieve up to 40% efficiency gains across all stages of the testing lifecycle, from story creation and manual test case design to automation script development, maintenance, review, and report analysis.
Deploy a local LLM (under 25 billion parameters); this ensures reduced operational costs and enhanced data security, while still delivering reliable performance for test automation and analysis.
Generate real-time, traceable insights through LLM-driven analysis, empowering engineers, and QA teams to enhance test completeness and identify coverage gaps proactively.
The accelerator shortens testing timelines, helping organizations deliver high-quality products faster, without compromising safety or performance.
The platform helps organizations reduce testing effort, improve coverage, and accelerate release cycles by using Agentic & GenAI to assist in test design, execution, and analysis across Web, Mobile, API, Desktop, and Embedded systems.
We propose a proof-of-concept (POC) where outcomes generated by the platform are compared with existing testing approaches to assess effort reduction, coverage, and execution speed.
The solution supports flexible deployment models. This ensures data residency, security, and deployment flexibility:
Yes. A single deployment can support multiple teams, projects, and domains, enabling reuse of test assets, configurations, and knowledge across the organization.
All AI-generated outputs are transparent, reviewable, and editable. Human validation is part of the workflow, ensuring accuracy and control.
Security is addressed through: Deployment within controlled environments, support for on-premise and private cloud hosting, isolation of customer data and enterprise-grade access control.
AI Studio platform has no separate license cost; customers only pay for selected AI model usage (if external), based on usage and features. Customization is optional and scoped separately.
AI Studio supports hybrid testing strategies, combining manual, semi-automated, and simulated test approaches. It does not replace complete hardware constraints instead, it augments testing workflows with AI-driven intelligence, and it helps optimize test design, prioritize high-risk scenarios, and reduce repetitive manual effort even when full automation is limited due to hardware dependencies.
AI Studio uses GenAI to analyze updated requirement and assist in updating impacted test cases , identifying obsolete or redundant tests, suggesting new test scenarios for changed functionality. This significantly reduces the manual overhead of test maintenance.
AI Studio provides end-to-end traceability across requirements, user stories, test cases, and test execution results. This enables audit-ready documentation, impact analysis, and compliance reporting with minimal manual effort.
AI Studio improves GUI test reliability by Assisting in robust locator strategies, identifying flaky tests and timing issues through execution analysis, recommending self-healing or alternative validation approaches. This reduces maintenance effort and improves automation stability.
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