Outcreating End-to-End Quality Engineering Transformation for a Global Media and Entertainment 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 developed a holistic Quality Engineering transformation framework anchored around the following key solution components:

• Defined and implemented an automation-first QE strategy across UI, API, and data layers using tools such as Tosca, Robot Framework, PyTest, and Python. This enabled faster validation, broader coverage, and improved consistency across testing cycles.
• Established strong media-domain QE capabilities across streaming platforms, digital applications, media supply chains, and enterprise systems. This brought deeper domain-led validation and improved release stability across complex media ecosystems.
• Advanced future-ready QE through Gen AI, DevOps integration, and talent transformation. The initiative introduced Gen AI-led defect categorization, test optimization, and intelligent reporting while embedding continuous testing into CI/CD pipelines. Functional testers were also upskilled into automation roles to build scalable and sustainable QE capabilities.
• Built a dedicated real-device testing capability through an exclusive Living Room Device Lab. This enabled validation across smart TVs, streaming devices, and set-top boxes, improving the detection of device-specific issues and reducing production defects and post-release incidents.
• Standardized QE processes, tools, frameworks, and governance practices across platforms, including ServiceNow, Appian, Salesforce, and data systems. Centralized reporting through ServiceNow, qTest, and Jira improved visibility, reduced duplication, and strengthened operational control.

Key Benefits

• Achieved more than 85% regression test coverage through an automation-first approach, ensuring consistent quality across applications, platforms, and releases.
• Delivered more than 30% productivity gains by standardizing test processes and reducing manual effort, resulting in improved delivery efficiency.
• Developed AI-driven accelerators to enhance release predictability. This resulted in nearly 70% improvement in defect categorization and reduced defect triage effort by 25%, accelerating time to market.
• Built a future-ready and well-governed QE organization through centralized dashboards, metrics, and reporting. This improved visibility while strengthening governance, scalable quality assurance, and operational excellence.

Conclusion

Redefined Quality Engineering from fragmented, manual practices to a unified, AI‑ and automation‑led model, integrating reporting, DevOps, and standardized frameworks to significantly improve quality, operational efficiency, and release velocity across the 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