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# Slide 1: Title Slide
**Title:** BMS AI Assistant
**Subtitle:** Enterprise Supply Chain Optimization System
**Footer:** Technical Overview | Version 1.0
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# Slide 2: Executive Summary
**Key Points:**
* **Objective:** Streamline access to supply chain data via natural language.
* **Solution:** Local AI Chatbot with integrated forecasting capabilities.
* **Value Proposition:** Zero external API costs, 100% data privacy, instant response time.
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# Slide 3: System Architecture
**Layers:**
1. **Presentation:** Web-based UI (HTML5/JS).
2. **API:** FastAPI Backend (Python).
3. **Logic:** Intent Parser & Business Rules.
4. **Data:** In-memory high-speed data store.
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# Slide 4: Core Capabilities
**Features:**
* **Demand Forecasting:** 30-day predictive analytics using ARIMA.
* **Inventory Tracking:** Real-time multi-warehouse stock visibility.
* **Supplier Intelligence:** Vendor lead times and contact details.
* **Automated Reporting:** On-demand PDF generation.
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# Slide 5: Technical Stack
**Technologies:**
* **Language:** Python 3.10
* **Framework:** FastAPI + Uvicorn
* **AI Model:** TinyLlama 1.1B (Local Inference)
* **Deployment:** Docker Container
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# Slide 6: Process Flow
**Steps:**
1. **User Input:** Natural language query.
2. **Intent Recognition:** Pattern matching algorithm.
3. **Data Retrieval:** SQL/CSV lookup or Model Inference.
4. **Response Generation:** JSON formatting and UI rendering.
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# Slide 7: Future Roadmap
**Next Steps:**
* Integration with live ERP database (SQL).
* Advanced multi-variate forecasting models.
* User authentication and role-based access control.
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