In-house experimentation platforms are increasingly used to support continuous, data-driven product development. While prior research outlines general infrastructure requirements, it offers limited insight into why companies build their own platforms, how they design them, and what challenges emerge. This study examines publicly available industry reports and engineering blogs to identify recurring motivations, implementation patterns, and organizational challenges. Companies pursue in-house solutions to gain context-specific functionality, ensure compliance, and embed experimentation into workflows. These efforts lead to modular architectures, custom metrics pipelines, and self-service tooling—but also introduce challenges such as scalability limits, knowledge silos, and cultural resistance. A process model illustrates how motivations shape implementation and how challenges drive iterative refinement. The findings position platforms not as neutral tools but as evolving socio-technical systems embedded in organizational context. The study distinguishes in-house platforms from off-the-shelf solutions and offers practical insights into build-vs-buy decisions, design trade-offs, and long-term experimentation strategy.