Case Study

AI-Driven Product Optimization: Transforming POS Performance for a Global CPG Leader

A global CPG leader leveraged AI-driven predictive analytics to enhance product offerings, reduce inventory waste, increase revenue, and improve customer satisfaction, all within an efficient eight-week rollout.

Leading Insurer Uses AI to Bring Churned Clients Back to Service

A leading insurer harnessed AI-driven predictive analytics to reduce the number of churned clients. This was accomplished by first generating a list of returning clients. After which, sales and marketing efforts targeted these clients. The predictive model was ready for deployment in less than two weeks.

AI QA Test Optimization for Mobile Carrier

A leading mobile carrier benefited from AI-driven predictive analytics to determine which QA tests will have a high rate of defects along with reasons. Employees were directed to fix such faults before they occur. It took just under a month to build a predictive model that was ready for deployment.

Reduced Losses for Manufacturing Production Line

Qualitest’s AI solution helped an IC manufacturer predict failures, optimize root cause analysis, and improve product quality, boosting yield and efficiency with automated, no-code modeling.

Predictive Maintenance for Electrical Facilities Challenge

A nationwide electricity provider faced a challenge to ensure high service SLA for electric power distribution while increasing technical teams’ productivity. The company’s transformers are located in various locations. Keeping the transformers in optimal health was a costly task as technical teams were responding to infrastructure failures as they occurred.

Machine Learning Solution Maximizes Marketing Growth for a Leading Mobile Carrier

Qualitest provided telecom company with a sophisticated predictive model solution that increased sales and ROI for digital marketing. The solution proved to be accurate, efficient, and powerful, increasing sales by enhancing the effectiveness of sales calls.

No-Code ML Solution: Optimizing Options Trading Model for Financial Institutions

Qualitest empowered financial institutions to swiftly build stable, high-performing options trading models with its no-code ML solution. By leveraging over 40 algorithms, rapid deployment, and minimized risks, we accelerated revenue growth.

Building a Vote Tabulation Solution for US Election Night Readiness in Record Time

When a not-for-profit news agency needed to thoroughly validate its vote tabulation platform with simulated real-time data volume, Qualitest exceeded performance targets by creating a configurable synthetic data simulation engine that generated high-volume, multi-format data with accurate real-time functionality.

Qualitest Enables Seamless Expansion and Localization for a Global Apparel Brand in the Canadian Market

When our Client expanded into the Canadian market by validating and localized their key e-commerce systems, Qualitest ensured compliance and delivered a seamless customer experience. This allowed our Client to confidently launch their operations in Canada.

Mission Impossible: Automating Everything in ServiceNow To Achieve 100% Maximum Defect Coverage While Dodging Schema Traps

Our Client, a long-established leader in the media services industry, achieved 100% requirement testing coverage resulting in maximum defect coverage (60+ defects) found.

Leading Technology Organization Enhances Document Processing Capabilities with Testing Applications Infused With AI

When our Client, a leading technology organization renowned for its advanced document processing capabilities, we helped them to streamline and enhance the overall quality of the AI model before deployment.

Public Sector Institution Implements DevPerfOps Framework to Accelerate Digital Transformation

Qualitest addressed the Client’s challenges in establishing effective performance testing with a KPI-driven optimized integrated automated solution encompassing performance engineering and cyber assurance, enabling real-time monitoring while eliminating the daily three-hour manual effort previously required for data generation.

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