chore: initial project scaffolding with AI rules and requirements

PLAN.md

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+# Inventory PWA System Implementation Plan
+
+This document outlines the technical architecture for the unified Inventory System (FastAPI + PWA).
+
+## Proposed Architecture
+
+### 1. Backend Server (Linux / Docker)
+- **Framework:** Python + FastAPI
+- **Database:** PostgreSQL
+- **Key Modules:**
+  - `Items / Interventions`: Standard CRUD logic.
+  - `Audit`: Every payload that mutates data writes an immutable log row.
+  - `AI-OCR`: Endpoint integrating Google Gemini Vision API (via Google AI Studio key) for complex label extraction onboarding.
+
+### 2. Unified Web Application (PWA)
+- **Framework:** React + Next.js (or equivalent).
+- **Offline Engine:** Service Workers, IndexedDB local storage map.
+- **Desktop Mode:** Full width dashboard, management, and settings.
+- **Mobile Mode (Browser / Add to Homescreen):** Dedicated full-screen scanner using `html5-qrcode`.
+
+### 3. AI Cost Strategy
+- **Routine Scans (Check-ins/Outs):** Client-side HTML5 barcode scanner. **Cost: $0.**
+- **Label Scanning (New Item):** Proxies a request to the Gemini API to parse the SFP label into JSON template fields.
+
+## Implementation Phases
+
+- **Phase 1: Database & Backend Foundation** - Docker setup, PostgreSQL schemas, User management, and Python API structures.
+- **Phase 2: Core Inventory API** - Endpoints to Add/Remove items, offline sync-merge logic, Audit triggers.
+- **Phase 3: The PWA Frontend** - PWA manifest, offline capabilities, UI scaffolding for Desktop/Mobile.
+- **Phase 4: Client-Side Scanning** - Integration of local JS barcode reading for routine stock scanning.
+- **Phase 5: Gemini AI Vision Integration** - Server-side Gemini API integration and the text-mapping frontend view.