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.

We introduce Earth Virtual Expert (EVE), the first open-source, end-to-end initiative for developing and deploying domain-specialized LLMs for Earth Intelligence. At its core is EVE-Instruct, a domain-adapted 24B model built on Mistral Small 3.2 and optimized for reasoning and question answering. On newly constructed Earth Observation and Earth Sciences benchmarks, it outperforms comparable models while preserving general capabilities.We release curated training corpora and the first systematic domain-specific evaluation benchmarks, covering MCQA, open-ended QA, and factuality. EVE further integrates RAG and a hallucination-detection pipeline into a production system deployed via API and GUI, supporting 350 pilot users. All models, datasets, and code are publicly available.