AI-Powered QA Transformation

Elaborate on the Challenge

The customer’s quality assurance processes relied heavily on manual testing and fragmented automation across legacy and modern systems. Document verification was error-prone and time-consuming, with no automation in place to compare multi-format documents at scale. Highly unstructured requirements further slowed analysis and test design, limiting test coverage and increasing dependency on manual effort.

As the organization accelerated digital initiatives, API integrations, and platform upgrades, QA struggled to keep pace. Manual test creation and high automation maintenance reduced efficiency, while complex system integrations led to gaps in regression, security, and data-quality testing. The absence of proactive monitoring, standardized QA processes, and automated regression increased the risk of defects reaching production and delayed time-to-market.

Objective Summary

The customer aimed to transform its QA function by leveraging AI-driven automation to improve test coverage, accuracy, and speed while reducing manual effort. Key objectives included automating document validation, accelerating test case generation from unstructured requirements, and enabling scalable, low-maintenance automation frameworks.

In parallel, the customer sought to establish standardized, end-to-end QA delivery with continuous monitoring, proactive defect detection, and consistent reporting. The goal was to support frequent releases and complex integrations while achieving higher quality, lower cost, and faster delivery.

Business Impacts

Eliminated 80–90% of manual QA effort through AI-driven automation

Reduced test creation effort by 85% using AI-assisted requirement analysis

Achieved 90%+ test coverage across critical systems and integrations

Enabled faster time-to-market with consistent, high-quality releases

Achieved zero defect slippage across production releases

Delivered 80% cost savings in QA operations

Reduced document validation time from 50 hours to under 4 minutes

Improved early defect detection through continuous monitoring and automated regression

Solution

Moder delivered an AI-powered QA transformation that embedded intelligence across the testing lifecycle. Document automation was introduced using Java-based frameworks to enable fast, accurate comparison of multi-format documents, replacing manual verification and scaling validation across thousands of pages.

To address unstructured requirements, Moder evaluated and implemented AI-assisted analysis using Copilot-driven NLP and tagging. This significantly accelerated requirement understanding and automated test case generation, reducing manual effort and improving accuracy early in the QA cycle.

Moder also implemented AI-driven smart test automation using Playwright combined with Copilot-enabled automation patterns. This approach delivered rapid, self-healing, and security-focused test coverage while dramatically reducing automation maintenance. End-to-end QA delivery was executed across multiple enterprise platforms, including IF, Resolute, MuleSoft, HCM/Win11, and LoanSphere, supporting both BAU and digital initiatives.

To ensure sustainability and continuous improvement, Moder established QA monitoring and operational practices, including daily, weekly, and quarterly health checks, automated regression, enhanced reporting, and Jira-based collaboration dashboards. Continuous monitoring enabled early defect detection and proactive quality management across releases.

By embedding AI across document validation, requirement analysis, test automation, and QA operations, Moder helped the customer transform QA from a bottleneck into a strategic enabler of delivery velocity and quality. The AI-powered approach delivered measurable improvements in speed, coverage, and cost efficiency while significantly reducing manual effort.

This transformation equipped the organization with a scalable, future-ready QA framework which is capable of supporting complex integrations, frequent releases, and ongoing digital innovation with confidence and consistency.

Conclusion

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