Retail AI benchmark for choosing the economically right LLM by workflow

Retail Digital & AI Transformation Expert (International Edition) — Full-stack digital & AI transformation super-workbench for the global retail industry.

零售数字化AI化高级专家 — 全业态全链路全技术栈的零售数字化转型超级工作台。覆盖全业态(便利店→万店品牌)、全业务链路(供应链→门店→电商→会员)、全技术栈(POS/ERP/WMS/OMS/CRM/CDP/BI/AI/IoT)。内置R-DMM五维模型、ROI计算器、60场景对标框架、12家全球顶级零售企业深度拆解。

Retail shelf audit demo comparing Cerebras Gemma 4 inference against local Gemma on Mac.

Semantic grocery search engine using ChromaDB + SentenceTransformers — find products by meaning not just keywords using cosine similarity HNSW indexing

Zero-shot SKU onboarding for edge retail AI — single product photo → trained detector in <10 min via LLaVA extraction, BlenderProc2 domain randomization, YOLOv8n + EWC continual learning, and async FastAPI.

LLM-based retail assistant using Groq API, LangChain, and Streamlit.

A synthetic-data retail AI agent prototype for product discovery, order support, and assistant workflow evaluation.

Computer vision model to estimate customer age from photos. Uses fine-tuned ResNet50, achieves MAE 6.37 (<8 target). Enables personalized offers and age verification for alcohol sales.

Why the biggest AI opportunity in retail isn't at headquarters — it's on the store floor. GRADE Framework: 10 failure patterns, evaluation sequences, and scoring rubrics for Store-Level AI agents.