This solution leverages enterprise-grade large language model (LLM) technology to build a comprehensive intelligent decision-making system for retail enterprises. Through natural language conversation, frontline store staff and management can instantly query sales data, inventory status, staff efficiency metrics, and other key business information — without requiring professional data analysis skills. The system transforms the paradigm from "people searching for data" to "data finding people." The platform comprises three core modules: intelligent data Q&A, business diagnostic analysis, and industry trend insights, covering key retail scenarios including sales, inventory, staff performance, and category management. Since launch, daily data query efficiency has improved by over 90%, with monthly interactions exceeding 100,000 queries and a 90.3% user satisfaction rate. The solution reduces slow-moving inventory by 15%, improves store staff efficiency by 25%, cuts decision meeting time by 70%, and shortens ROI payback period to 6 months.

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Highlights

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Service Packages and Offerings

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Details

This solution addresses core retail pain points — inefficient data retrieval and decision-making reliant on individual experience — by building a retail intelligent decision collaboration platform powered by enterprise-grade LLMs.

【Core Features】
- Intelligent Data Q&A: Supports natural language text and voice queries covering 400+ KPIs including sales, inventory SKUs, staff efficiency, and category analysis. Data accuracy 100%, intent recognition accuracy 99%. The system has served 17,626 users with 629,412 total queries and over 100,000 monthly interactions.

- Business Diagnostic Analysis: Based on 30+ business corpus documents and 300+ business questions, it builds a "YoY/MoM/Competitor" analysis framework supporting a complete analytical chain from situational interpretation, root cause analysis, and insight inference to decision recommendations.

- Industry Trend Insights: Collects external market data to provide multi-dimensional fashion industry trend analysis including category trends, brand trends, and price-band distribution, helping retailers stay ahead of market movements.

【Technology Architecture】
Built on the FastData Foil data fusion platform integrating structured, semi-structured, and unstructured multimodal data, combined with the Deepexi enterprise LLM platform (MQL Text2MQL2SQL technology), delivering high-accuracy, zero-hallucination proprietary AI capabilities with multi-level permission controls and private deployment support.

【Use Cases】
Applicable to chain retail and fashion brand enterprises. Enables frontline staff to query store data instantly, assists management in multi-brand and multi-region performance comparison and decision analysis, significantly shortening decision cycles and improving operational efficiency.

【Target Departments】
Innovation and Technology Bureau; Commerce and Economic Development Bureau; Financial Services and the Treasury Bureau

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Customer Cases

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