Outcreating End-to-End Quality Engineering Transformation for a Global Media Leader

About Client

The client is a leading global media and entertainment enterprise delivering premium content across digital streaming platforms. Its ecosystem spans web, mobile, and living-room devices, serving a large global audience.

Given the scale and frequency of releases, platform performance, stability, and quality are critical to business success.

Market Context

Media and entertainment platforms are under increasing pressure to deliver seamless, high-quality digital experiences across devices and geographies.
Frequent content releases, live events, and platform upgrades demand rapid validation cycles with minimal tolerance for defects.

As streaming ecosystems expand, quality engineering must evolve toward automation, continuous testing, and AI-driven insights. Organizations that rely on fragmented tools and manual processes struggle to maintain consistency, speed, and visibility, making quality engineering transformation a strategic priority.

Key Challenges

The client’s quality engineering landscape faced several constraints across teams and platforms:

• QE and testing teams relied heavily on manual testing, increasing regression timelines and effort.
• Automation engineers worked with fragmented tools and frameworks, leading to duplication and inconsistent practices.
• DevOps and engineering teams lacked seamless integration of testing into CI/CD pipelines, limiting early defect detection.
• Program managers and stakeholders had limited visibility due to fragmented reporting and inconsistent metrics.
• Release management teams operated under tight timelines due to frequent platform upgrades and content launches.
• Device testing teams lacked standardized environments for validating performance across living-room ecosystems, impacting consistency in streaming platform testing.

LTM’s Solution

LTM delivered a comprehensive quality engineering transformation built on automation, standardization, and scalability.

Key solution components included:

• Automation‑first QE strategy: Implemented an automation-first QE strategy across UI, API, and data layers using Tosca, Robot Framework, PyTest, and Python.
• Strong media QE expertise: Established strong media-domain QE practices across streaming platforms, digital applications, and enterprise systems.
• Dedicated living room device (LRD): Built an exclusive LRD testing lab to enable real-device validation across Smart TVs, streaming devices, and set-top boxes.
• Enterprise application coverage: Enabled QE coverage across enterprise platforms including ServiceNow, Appian, Salesforce, and data systems.
• Gen AI enablement: Introduced Gen AI-led capabilities for defect categorization, test optimization, and intelligent reporting.
• Process and tool standardization: Standardized tools, frameworks, and reusable utilities to reduce duplication and improve consistency.
• DevOps and continuous testing: Integrated testing into CI/CD pipelines with automated build validation, nightly runs, and continuous testing practices.
• Upskilling and cross skilling: Upskilled functional testers into automation roles, strengthening QE capabilities across teams
• Governance and reporting: Centralized governance and reporting using ServiceNow, qTest, Jira, and Report Portal.

This approach established a scalable, automation-led QE ecosystem aligned to high-performance digital platforms and evolving streaming platform testing needs.

Business Benefits

• Achieved over 85% regression test coverage, ensuring consistent quality across releases
• Accelerated release cycles through automation and CI/CD-driven validation
• Delivered over 30% productivity gains through automation-led execution
• Strengthened platform stability during critical releases, upgrades, and large-scale rollouts
• Enabled real‑world validation across living‑room devices, improving end-user experience accuracy
• Increased release confidence through early detection of device-, OS-, and platform-specific issues
• Improved governance with centralized dashboards, metrics, and reporting visibility
• Established a future-ready QE organization through investments in Gen AI, DevOps, and continuous upskilling

Conclusion

The transformation redefined quality engineering from a fragmented, manual process to a unified, automation-led model. By integrating AI, DevOps, and standardized frameworks, the organization improved quality, efficiency, and release speed across its digital ecosystem.

Ongoing efforts focus on expanding AI-driven testing, enhancing platform observability, and scaling automation across new content and device ecosystems. This foundation positions the client to deliver consistent, high-quality streaming experiences at global scale.

Quote

“As the software industry shifts with AI, cloud-native architectures and continuous delivery, we built a holistic Quality Engineering framework to support this transition. This approach has enabled seamless convergence across technology & industry domains helping drive consistency, speed and quality at scale.”

- Pavan Madappa, Account Portfolio head at LTM