Developing enterprise customer-service assistants with Reinforcement Learning (RL) critically depends on high-fidelity user simulators and well-aligned reward functions, since real-customer interactions are impractical for large-scale training and rule-based rewards fail to capture complex business requirements. We present CoEvoLoop, a framework that co-evolves the assistant policy together with these RL components through an expert-calibrated, multi-round loop. Beyond an LLM-based simulator for rollout generation and a multi-dimensional grader for rubric-based evaluation of service quality and compliance, CoEvoLoop integrates an expert annotation interface where sampled trajectories and grader judgments are reviewed and corrected. Each round, expert feedback updates the simulator and grader, after which the assistant is optimized with trajectory-level RL against the updated evaluation signal; monitoring interfaces support rollout inspection, dimension-wise failure attribution, and cross-round tracking. In an industrial evaluation, the system improves multi-turn service quality and reduces compliance violations over training rounds; component ablations and blind expert evaluation confirm that co-evolving the simulator and grader is load-bearing for these gains. We release the system demo code, along with a demo video.