Sovereign AI deployments often require foundation models to operate under constraints overlooked by standard benchmarks: adherence to legislation, multilingual and multimodal grounding in local contexts, institution-specific safety behaviors, and on-premises deployment on power-constrained infrastructure. We present a production case study detailing the multi-stage engineering pipeline that spans trillion-token-scale continued pretraining, multi-billion-token alignment and post-training for a 123B-parameter natively multimodal foundation model, *Phoenix-VL 1.5 Medium*, optimized for Singapore public-sector use. On our novel localized evaluation suite and behavioral safety framework, the model substantially improves localized multimodal, statutory, and public-safety capability (a +0.118 average gain over the strongest baselines) while retaining broad general capability. We also share empirical analysis from a full production deployment currently serving over 30,000 users and processing ∼1 billion tokens over a two-month period. Ultimately, our findings demonstrate that sovereign AI is best approached as an end-to-end systems problem, where data curation, domain training, institutional alignment, and hardware layout interact and cannot be optimized in isolation.

Prompt optimization (PO) provides a practical way to improve response quality when users lack the time or expertise to manually craft effective prompts. Existing methods typically rely on LLMs’ self-generation ability to optimize prompts. However, due to limited downward compatibility, the instruction-heavy prompts generated by advanced LLMs can overwhelm lightweight inference models and degrade response quality, while also lacking interpretability due to implicit optimization. In this work, we rethink prompt optimization through the lens of explicit and interpretable design. We first identify a set of model-agnostic prompt quality merits and empirically validate their effectiveness in enhancing prompt and response quality. We then introduce MePO, a merit-guided, locally deployable prompt optimizer trained on our merit-guided prompt preference dataset generated by a lightweight LLM. MePO avoids online optimization, reduces privacy concerns, and, by learning clear, interpretable merits, generalizes effectively to both large-scale and lightweight inference models. Experiments demonstrate that MePO achieves better results across diverse tasks and model types, offering a scalable and robust solution for real-world deployment. The code, model and dataset can be found in https://github.com/MidiyaZhu/MePO.