Case Studies

QualityKiosk deploys AI-driven Watermelon solution to help a leading Life Insurance company optimize testing across 600+ Products

Industry & Segment

Life Insurance

Objective

The objective was to revamp client’s testing workflows and enhance quality standards across a wide and diverse range of products

Partners

Watermelon

The client is a prominent life insurance company in India that provides a range of individual and group life insurance products. The client has partnered with us with the aim of improving its operational efficiency and product quality through an AI-driven quality engineering strategy

Challenges

Managing Testing Across 600+ Diverse Products

Limited Scalability of Existing QA Processes

Heavy Dependence on a Small Group of Technical Testers

Complexity of Legacy and Desktop Applications

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Strategic Goals and Platform Choice

Prioritized the adoption of an AI-driven automation platform to strengthen the quality engineering framework across 600+ products without increasing costs.
Chose the Watermelon platform after a structured technology evaluation, identifying it as the best fit for meeting both short- and long-term testing objectives.

Short‑Term Strategic Goals

Enhance scalability for regression testing.
Expand test coverage across a broad product portfolio.
Realize measurable ROI within 6–8 months of project initiation.

Long‑Term Strategic Goal

Achieve a 5× acceleration in test execution within three years

Our Strategy

Watermelon implementation

The client partnered with QualityKiosk to implement the Watermelon platform for ( zero-code) no code test case development, emphasizing complex no-code actions and reusability with snippets. Execution at scale involved using device labs, grids, emulators, and local on-premises browsers, with test cycle planning and collaboration as key components

Phase One - Assessment and Baselining

• The assessment and baselining phase of the project involved evaluating the current state of the client’s Life Asia products.
• It further involved setting up reusable functional test snippets for New Business and Day 1 features for ULIPs and Conventional applications. This emphasized no-code actions and snippet reuse.
• Additionally,early performance insights about the legacy and desktop application testing were provided.

Phase Two- Build and Run

• Our team established essential hardware and software configurations to take advantage of Watermelon’s AI capabilities.
• This phase also included the Build team project planning with 6 to 8 milestones, focusing on basic script design and regression testing for 450+ product codes.
• The Run team conducted product testing across three major projects, covering 180+ product codes, where 10,000 test cases were reused.

Phase Three – Handover to Business as Usual (ongoing operations) Team

• This stage involved implementing test data management and user adoption strategies.
• Watermelon’s low-code/no-code interface allowed all testers to engage effectively, marking a significant shift from previous automation attempts that relied on a limited number of technical testers.
• Continuous automation building for Day 2 features was transferred to the BAU team.

Technical Results

63%

TAT Reduction – Project 1

62%

TAT Reduction – Project 2

58%

TAT Reduction – Project 3

Improved Test Coverage

Enabled extensive regression testing across diverse product codes at no additional cost.

Project Highlights

A lean team of four members focused on designing reusable tests within the Watermelon platform, covering over 600+ products.

Developed a comprehensive test pack with 1100 test cases with unique conditions, built with snippets, that could be used across all products.

Prepared test data by retrofitting production data with minimal changes, saving another 45% of test data creation time.

Conducted a requirement study and leveraged existing automated test cases, reducing design time by over 70%. Our AI-led strategy is helping the client further optimize test case creation.

Executed tests iteratively (up to four iterations) to ensure quality before production release, achieving desired results within the first two months for three project cases.

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