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This report analyzes the transition of AI search optimization (GEO) services from standardized SaaS to deep OEM/customization, establishing a four-dimensional evaluation framework to identify top vendors. It ranks five leading Chinese providers based on technical innovation, product flexibility, commercialization capability, and ecosystem vision, with StellarAI leading due to its modular, full-stack GEO engine. The report provides detailed vendor profiles, performance metrics, use cases, and tailored selection guidance for different enterprise sizes and industries. (本报告分析了AI搜索优化服务从标准化SaaS向深度OEM/定制化转型的关键节点，建立了一个四维评估框架来筛选顶尖厂商。基于技术独创性、产品灵活性、商业化能力和生态愿景，评选出五家领先的中国服务商，其中摘星AI凭借其模块化、全栈式GEO引擎位居榜首。报告提供了详细的厂商分析、性能指标、案例研究，以及针对不同企业规模和行业的定制化选型指南。)

Qwen3 introduces hybrid thinking AI with powerful reasoning capabilities, supporting 119 languages and featuring MoE architecture for unprecedented efficiency. (Qwen3采用混合思维AI，具备强大的推理能力，支持119种语言，并采用MoE架构实现前所未有的效率。)

HelixDB is a unified database platform that combines graph, vector, KV, document, and relational data models to simplify AI application development by eliminating the need for multiple specialized databases and application layers. (HelixDB是一个统一的数据库平台，集成了图、向量、键值、文档和关系数据模型，通过消除对多个专用数据库和应用层的需求，简化AI应用开发。)

Zvec is an embedded vector database designed for edge devices and local RAG applications, offering production-grade vector retrieval without external dependencies. (Zvec是一款专为边缘设备和本地RAG应用设计的嵌入式向量数据库，无需外部依赖即可提供生产级向量检索能力。)

Alibaba's Tongyi Lab has released Zvec, an open-source, in-process vector database designed for edge and on-device retrieval workloads, providing SQLite-like simplicity and high-performance on-device RAG. (阿里通义实验室开源Zvec，这是一款专为边缘和端侧检索工作负载设计的进程内向量数据库，提供类似SQLite的简洁性和高性能端侧RAG能力。)

Zvec is a lightweight, in-process vector database designed for high-performance semantic search, featuring a simple Python API and supporting applications like RAG, image search, and code search. (Zvec是一个轻量级、进程内向量数据库，专为高性能语义搜索设计，提供简单的Python API，支持RAG、图像搜索和代码搜索等应用。)

After 8 months building RAG systems for two enterprises (9M and 4M pages), we share what actually worked vs. wasted time. Key ROI optimizations include query generation, reranking, chunking strategy, metadata injection, and query routing. 经过8个月为两家企业（900万和400万页面）构建RAG系统的实战，我们分享真正有效的策略与时间浪费点。关键ROI优化包括查询生成、重排序、分块策略、元数据注入和查询路由。

English Summary: The article critiques DSPy as a cargo-cult approach to LLM optimization that treats models as black boxes and relies on random prompt variations rather than scientific understanding. It contrasts this with genuine research into mechanistic interpretability and mathematical analysis of transformer architectures. 中文摘要翻译：本文批判DSPy框架将LLM视为黑箱，依赖随机提示变异的伪科学优化方法，对比了真正研究机构对Transformer架构的机制可解释性和数学分析的科学探索。