With the rapid evolution of RISC-V extensions such as Vector, Matrix, and other custom instructions, RISC-V platforms are becoming capable of executing modern AI models. However, achieving high-performance deployment while maintaining a unified software stack across diverse extensions remains a key challenge. This paper introduces Buddy Compiler, an end-to-end AI compiler designed to provide a unified interface for AI model integration, multi-level compilation optimizations, and extensible code generation targeting diverse RISC-V extensions. Buddy Compiler adopts a multi-level architecture consisting of a frontend, middle-section, and backend, enabling reusable high-level optimizations while supporting specialized backends for RISC-V architectures. The frontend provides a graph infrastructure that interfaces with mainstream AI frameworks and converts imported models into a unified representation expressed with high-level MLIR dialects. The middle-section is built on MLIR and performs multi-level compilation optimizations, including operator fusion, memory access optimization, and vectorization. The backend implements dedicated MLIR dialects for RISC-V extensions, such as RVV, IME, AME, and Gemmini, and performs target-specific code generation. Through multi-level compilation, Buddy Compiler and its runtime system enable efficient deployment of AI models on RISC-V platforms, achieving performance comparable to manually optimized implementations such as llama.cpp.