The DA Data Intelligence Conference in Shenzhen showcased SF Tech’s breakthroughs in large‑model AI, revealing how their proprietary multimodal models, RAG innovations, and agent platforms dramatically improve logistics decision‑making, resource scheduling, and customer service across multiple industries.
Huolala shares its LaLaEval framework, detailing how large‑model applications are evaluated through defined stages—background analysis, metric design, dataset generation, standards setting, and statistical analysis—while illustrating real‑world use cases in freight and driver invitation scenarios, and outlining future automation prospects.
This article details Huolala's exploration of large‑language‑model (LLM) based AI assistants across multiple business scenarios, describing their architecture, implementation challenges, prompt engineering techniques, and the progressive stages from professional assistants to multi‑agent systems that drive efficiency and innovation in logistics.
Huolala, a logistics technology company, shares how it leverages large language models to create personal and office AI assistants across dozens of real‑world scenarios, detailing the underlying platform, prompt engineering, multimodal capabilities, multi‑agent coordination, and the resulting business empowerment.
Alibaba’s logistics AI team shares their year‑long journey building a vertical‑domain large language model for logistics, detailing model alignment, Text2API, RAG, SFT techniques, challenges like accuracy and knowledge‑base maintenance, and showcasing real‑world applications such as chatbots, DingTalk assistants, and custom AI assistants.
The article describes how Dada leverages artificial‑intelligence techniques such as multi‑dimensional supply‑demand forecasting, real‑time dynamic pricing, genetic‑algorithm‑based route planning, and multi‑objective order assignment to optimize its crowd‑sourced instant logistics operations.
At PyCon China 2023, Dada‑JD Daojia’s algorithm team presented their AI‑driven practices for dynamic pricing, route planning, and order assignment, detailing how multi‑dimensional supply‑demand balancing, genetic‑algorithm‑based TSP solutions, and multi‑objective optimization improve real‑time logistics efficiency.
