How Large Language Models Boost Huolala’s Customer Service Efficiency

Introduction

In the increasingly competitive logistics industry, Huolala, a technology‑driven logistics company, uses large‑model AI to improve customer‑service efficiency and service quality.

Challenges and Opportunities

The customer‑service team handles massive queries about shipment, order tracking, vehicle selection, fee inquiries, and more. Peak periods cause response delays, and the diversity of issues demands high expertise, raising training and management costs. Large‑model technology, with its strong natural‑language understanding, can quickly grasp user intent and provide precise, professional answers, creating opportunities for efficiency and personalization.

Specific Applications

1. Incoming Intent Smart Enhancement

The LLM extracts core user intent from conversations, generates concise summaries, maps them to predefined tags, and recommends standard operating procedures (SOP) to accelerate handling.

LLM Conversation Summary : extracts core user demand (e.g., “driver asks about account freeze”).

Intent Tag Matching : maps the summary to preset AI‑summary categories (e.g., “unfreeze account”).

SOP Recommendation : automatically pushes the appropriate SOP, shortening response time.

2. Order Diagnosis and Emotional Comfort

The model detects user emotions, adjusts reply tone, and provides soothing responses while diagnosing driver order status and offering solutions, thereby reducing anxiety and improving trust.

Information Extraction : intent recognition, sentiment analysis, and state management (guide, clarify, answer, end).

Instruction Decision : combines SOP pool, comforting script pool, backend diagnosis, and dialogue state to build prompts.

Reply Generation : the LLM generates multiple replies, a red‑line module corrects them, and the best reply is selected.

3. Vehicle Recommendation Assistant

Through multi‑turn dialogue, the model gathers cargo type, weight, volume, and distance, then combines platform vehicle data and transport rules to recommend the optimal vehicle, adjusting recommendations as users provide more information.

Results

Coverage Rate Increase : AI‑summary coverage rose from 22.2% to 47.6% (+25.4%).

Efficiency Optimization : average handling time (AHT) dropped markedly with SOP assistance.

Accuracy Improvement : conversation‑summary accuracy rose from 66% to 83.1%.

Benefits

The large model boosts efficiency, enhances service quality, and reduces operating costs by automating repetitive tasks, lowering staffing needs, and minimizing error‑related waste.

Future Outlook

Huolala will deepen LLM research, expand to scenarios such as virtual digital humans, improve emotion recognition and personalized recommendations, and strengthen human‑AI collaboration, while also contributing to industry standards and promoting intelligent logistics across the sector.