QEnix AI – The Next-Gen Quality Engineering Accelerator

Reimagining Quality Engineering through Autonomous Intelligence

QEnix AI is Edvensoft’s next-generation AI-powered Quality Engineering accelerator, designed to modernize and unify testing across manual, automation, and API layers.

Transform Your Operations

QEnix AI is Edvensoft’s next-generation AI-powered Quality Engineering accelerator, built to transform testing across manual, automation, and API landscapes. Powered by large language models and intelligent agents, QEnix AI brings autonomy, speed, and precision to every aspect of the quality lifecycle.

It enables test case generation directly from images, documents, or natural language prompts, and integrates seamlessly with PM tools like Jira, ClickUp, and Figma to create and synchronize test cases. Through bi-directional integration with test management systems such as Zephyr, TestRail, and Xray, QEnix AI ensures real-time alignment between design, execution, and reporting.

On the automation front, QEnix AI autonomously executes test cases across multiple frameworks, supports self-healing, and continuously updates results back into integrated systems. For API testing, it connects with Swagger and Postman to automatically discover APIs, generate test data, and execute validations on demand.

Its unified reporting dashboard delivers intelligent insights across manual, automation, and API executions—helping teams analyze efficiency, performance, and quality trends in real time. Designed for modern Agile and DevOps environments, QEnix AI empowers organizations to move from traditional testing to autonomous, AI-driven Quality Engineering—accelerating releases while reducing effort and cost.

KPIs Delivered as a measurable, enterprise-grade efficiency accelerator:

QEnix AI reduces test design effort by 80–90% by automatically generating test cases from documents, images, and natural language prompts.
It accelerates automation cycles by 60–75%, thanks to autonomous execution, reusable scripts, and self-healing capabilities.
Teams typically see a 40–50% improvement in early defect detection, driven by AI-generated test cases and continuous validation.
By reducing rework and minimizing reruns through intelligent self-healing, the platform cuts execution overhead by 30–40%.
Organizations achieve 35–45% cost savings and realize strong ROI within the first six months of adoption.
Release readiness improves by up to 50%, with faster regression cycles and automated cross-environment validation.
Its unified dashboard provides instant visibility into execution trends, enabling teams to compare manual versus automation performance in real time and make faster quality decisions.

Case Study 1: Accelerating QE Maturity for a Retail POS Platform

Problem:

A leading retail POS and store operations platform struggled with fragmented test management, slow regression cycles, and a manual-intensive testing process across multiple regions. Lack of integration between Jira, Zephyr, and automation tools caused poor visibility and inconsistent quality outcomes.

What QEnix AI Did:

Deployed QEnix AI Accelerator to automate test case creation from Jira user stories and POS workflow documents.
Enabled bi-directional synchronization between Jira, Zephyr, and automation pipelines.
Integrated Swagger-based API validation and autonomous regression execution via QEnix AI’s LLM engine.
Implemented a unified dashboard to monitor execution trends and release readiness.

Impact:

• 70% faster regression execution across POS modules.
90% test coverage achieved through automated generation and execution.
45% reduction in QA effort, with complete traceability from requirements to results.
30% improvement in release predictability and zero major UAT defects in subsequent rollouts.

Case Study 2: Enabling Autonomous Testing for a Fleet Management Enterprise

Problem:

A fleet leasing and management company faced challenges in maintaining test consistency across their Driver View and Fleet View applications. Test cycles were slow, manual updates in Zephyr led to duplication, and API validations were ad hoc without proper integration.

What QEnix AI Did:

Implemented LLM-driven test generation from application screens, Swagger documentation, and Jira epics.
Automated API test data creation and execution using QEnix AI’s agent-based framework.
Integrated bi-directionally with TestRail and Jira for continuous updates and analytics.
Deployed unified reporting for performance, coverage, and automation yield.

Impact:

60% reduction in manual regression effort.
2x faster test execution through autonomous orchestration.
Improved defect detection by 50% through early AI-driven test generation.
Consolidated visibility across manual, API, and automation results via a single dashboard.