Human‑Machine Coupling in Residential Services: AI‑Driven Quality Improvement at Beike

In the rapidly evolving field of artificial intelligence applications, the concept of Human‑Machine Coupling (HMC) is presented as a key trend for modern service industries, especially high‑value residential services where low tolerance for error is essential.

The article defines service as paid labor that creates benefit and satisfaction, highlighting its intangibility, heterogeneity, simultaneity, and perishability. Residential services are described as a specialized branch involving activities such as client consultation, property tours, negotiations, and internal training.

Four challenges of residential services are identified: complex professional knowledge and policies, numerous external decision‑making factors, low consumer familiarity, and high financial stakes, leading to four corresponding requirements for service providers: specialization, collaboration, AI‑assisted work, and continuous learning.

Human‑Machine Coupling is introduced with a five‑level taxonomy (L0–L5) inspired by autonomous driving, ranging from no AI involvement to deep automation where AI and humans mutually adapt. Beike currently operates at level L3 (partial automation).

Beike’s methodology for empowering service providers follows a Classification × Elevation × Allocation framework, aiming for devotion, professionalism, and collaboration. Service quality is precisely modeled through a five‑dimensional attribution model (basic competence, knowledge, skills, norms, safety) that quantifies the gap between expected and actual experience.

The service‑quality attribution model is built from real‑world data (e.g., VR tours, virtual calls) and adheres to five requirements: real‑scenario data, avoidance of spurious causality, comparability, objectivity, and interpretability. The resulting service score follows a normal distribution with high consistency and effectiveness.

A feedback‑based service‑quality control system treats service delivery as a control problem: perceived service, error detection, negative feedback to a controller, and corrective actions (ability training, process assistance, or managerial intervention). Two control dimensions are defined: efficiency (task scheduling) and quality (store reputation).

Task scheduling is formulated as a dynamic optimization problem balancing short‑term revenue, long‑term learning, and global benefits, with constraints on agent capacity, ability, and profit‑vs‑effort trade‑offs. The objective function integrates immediate task assignment rewards, future expected gains, and default gains for unassigned tasks, expressed with binary decision variables X ijk .

Experimental results show that the combined optimization outperforms greedy allocation in the long term, improving both gross merchandise value and unilateral cooperation ratio.

The intelligent training ground provides AI‑driven dialogue training for agents, using a PID controller to align conversation content with desired learning curves and a pre‑trained KeBert model for semantic understanding. Training progresses through five stages (L0→L4) and simulates diverse client personas across demographics, preferences, personality, and random events.

Process assistance leverages the service‑quality attribution model and user portraits to generate AI‑driven response recommendations at appropriate HMC levels, reducing error rates and enhancing consistency.

Diagnostic reports generated by the control system pinpoint low‑quality interactions down to individual utterances, enabling targeted managerial feedback and further training.

Overall, Beike’s AI‑enabled system demonstrates significant improvements in service‑quality characterization and control, accelerating agent skill growth, reducing variance, and achieving scalable, high‑quality residential services.

The article concludes with key takeaways: human‑machine coexistence is inevitable in service domains, a staged HMC roadmap is essential, precise service‑quality modeling is the cornerstone of AI empowerment, and the combined strategy of grading, elevation, and scheduling drives sustainable growth.