Smart Site Selection Engine

Problem

Store location decisions often stall on vague questions like “which commercial area looks better.” Without an explicit customer segment and operating strategy, AI can pull broad POI, competitor, and transit data and produce a report that looks confident but is hard to act on.

How It Works

• Forced parameter validation: The workflow first confirms core customer positioning and store operating strategy; if either is missing, it asks a follow-up and ends the turn.
• Lightweight coordinate fetching: It uses AMap POI to retrieve only the top 3 candidate locations, including names and center coordinates, without expanding into transit, competitor, or facility details.
• Business insight generation: It generates a short macro outlook and concise strengths/weaknesses notes for each of the three districts.
• Report handoff: The coordinates, location names, and text insights are handed to a Python engine for concurrent data retrieval, scoring, a micro-business radar, and a single-file HTML report.

Boundaries

This flow depends on AMAP_WEBSERVICE_KEY, AMap POI access, and a downstream Python environment. It does not verify utility conditions such as water or power, and it does not guarantee real-time accuracy of competitor or transit details. It fits structured commercial insight reports for store opening decisions.