Engineering a Sales-Domain Expert Foundation Model via Quantized Cross-Architecture Merging: Part I — Core Capability Construction Through Multi-Phase Model Integration

This paper presents a foundational engineering framework for the development of a super-expert language model tailored to the enterprise sales domain, constituting the initial phase of a comprehensive multi-agent artificial intelligence (AI) ecosystem. The proposed methodology introduces a twelve-phase pipeline that systematically integrates post-training quantization, cross-architecture model merging, and hierarchical data processing to achieve an optimal balance between computational efficiency, model capability, and extensibility. This research addresses critical challenges in constructing production-ready language models capable of supporting high-performance deployment within enterprise contexts while maintaining scalability for downstream specialization. At the core of this approach lies a 4-bit post-training quantization of a large decoder-only foundation model, complemented by the integration of instruction-following capacity derived from a compact encoder-decoder model through a DARE-style merging procedure. This technique yields a unified model architecture that simultaneously preserves broad generative proficiency and domain-specific adaptability. The resulting foundation model serves as a core substrate from which specialized agents can be instantiated to perform distinct sales-oriented functions, including forecasting, customer relationship management, and sales enablement. As the first installment in a multi-part research series, this paper focuses on the initial three phases of the pipeline—base model enhancement, dataset preprocessing, and selective model fusion. In doing so, it situates the work within evolving research on quantization, model merging, and retrieval-augmented generation, thereby establishing the technical and methodological basis for subsequent studies on fine-tuning, domain adaptation, and agentic orchestration within enterprise-grade AI ecosystems.