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IntroductionThe European Union Artificial Intelligence Act (AI Act), Regulation (EU) 2024/1689, establishes a legal framework for the development, deployment, and use of artificial intelligence systems within the European Union. Its objective is to ensure that AI systems are developed and used in a manner that respects fundamental rights, safety, transparency, and accountability while supporting innovation. ClicShopping AI has been designed as an AI-assisted e-commerce platform that integrates external or locally hosted Large Language Models (LLMs) to assist merchants with content generation and administrative tasks. More as ClicShopping AI, this content is for all tools use the AI, below some recommandations to follow. More information : AI Eurepean Act AI-Assisted FeaturesClicShopping AI provides AI-assisted tools that support, but do not replace, human decision-making. Typical use cases include: Product description generation SEO content generation FAQ generation Product content enhancement Translation assistance Product creation assistance Administrative report generation Marketing content generation These features are initiated voluntarily by the user and operate as productivity tools. Human OversightOne of the principles promoted by the AI Act is appropriate human oversight. ClicShopping AI follows this principle by generating content intended for human review before publication or operational use. Users remain responsible for validating generated content, correcting inaccuracies, and deciding whether the generated output should be published or modified. ClicShopping AI does not make autonomous commercial or legal decisions on behalf of the user. TransparencyAI-assisted functionality is clearly identified within the application. Users intentionally invoke AI features through dedicated actions or interfaces. Generated content is presented as AI-generated assistance and should not be considered authoritative without verification. AI ProvidersClicShopping AI does not develop or train artificial intelligence models. Instead, it connects to AI providers selected by the system administrator. Supported providers include, without limitation: OpenAI Anthropic Google Gemini Mistral AI Ollama (local deployment) The administrator remains responsible for selecting the provider that best satisfies organizational, legal, privacy, and security requirements. Data ProcessingTo perform a requested AI operation, ClicShopping AI transmits only the information required by that operation. Depending on the selected feature, transmitted information may include: Product titles Product descriptions Product specifications Keywords Category names User prompts Context required for content generation The exact information transmitted depends on the selected feature and configured AI provider. Sensitive information should only be processed where appropriate safeguards have been implemented. High-Risk AI SystemsThe AI Act defines a category of High-Risk AI Systems for applications such as: Critical infrastructure Employment and recruitment Education Creditworthiness assessment Law enforcement Migration management Judicial decision support Access to essential public and private services The intended purpose of ClicShopping AI does not fall within these categories. ClicShopping AI is designed as an AI-assisted content generation platform intended to improve productivity within an e-commerce environment. If the software is integrated into workflows that qualify as high-risk under applicable legislation, additional legal obligations may apply to the deployer or integrator. Accuracy and LimitationsLike all generative AI systems, responses may contain: factual inaccuracies; incomplete information; outdated content; unsupported statements; hallucinations. Generated content should always be reviewed before publication or business use. ClicShopping AI does not guarantee the accuracy, completeness, originality, or legal compliance of AI-generated content. Privacy and Data ProtectionWhen external AI providers are used, submitted information is processed according to the policies of the selected provider. Administrators should ensure that the chosen provider complies with applicable data protection requirements, including the General Data Protection Regulation (GDPR), where applicable. Local AI deployments, such as Ollama, may be used when organizations require that data remain within their own infrastructure. Open Source ArchitectureClicShopping AI is an open source software project. The platform provides a modular architecture allowing administrators to choose their preferred AI provider without modifying the application's core architecture. The software acts as an integration layer between business workflows and AI services and does not itself constitute a foundation model or a general-purpose AI model. DocumentationThe following documents provide additional information inside the github repository: AI_USAGE.md — AI features and supported providers. AI_PROVIDERS.md — AI provider configuration. AI_LIMITATIONS.md — Known limitations of AI-assisted features. AI_ACT_COMPLIANCE.md — Compliance statement regarding the AI Act. DisclaimerThis article provides a general overview of how ClicShopping AI has been designed with regard to the principles established by the European AI Act. It is intended for informational purposes only and does not constitute legal advice. Organizations deploying ClicShopping AI remain responsible for ensuring that their implementation complies with the laws and regulations applicable in their jurisdiction.
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The European AI Act (Regulation (EU) 2024/1689) introduces a legal framework for the development and deployment of Artificial Intelligence systems within the European Union. Although ClicShopping AI integrates AI capabilities, its primary role is to assist merchants with content creation and administrative tasks. It does not make autonomous decisions affecting individuals or perform activities classified as High-Risk AI Systems under the AI Act. To support the principles of the regulation, ClicShopping AI has been designed around several key concepts: Human oversight – AI generates suggestions that remain under the user's control and responsibility. Transparency – AI features are explicitly invoked by the user through dedicated interfaces. Provider independence – Administrators can choose their preferred AI provider, including OpenAI, Anthropic, Google Gemini, Mistral AI, Ollama, and other compatible services. Privacy awareness – Only the information required for a requested AI operation is transmitted to the configured provider. Open architecture – ClicShopping AI does not develop or train AI models; it provides a flexible integration layer between the application and AI providers. To improve transparency and documentation, the project now includes dedicated documentation covering: AI usage and supported providers AI limitations AI Act compliance These documents describe how AI features operate, what information may be exchanged with AI providers, known limitations of generative AI, and the measures implemented to align with the principles of the European AI Act. As with any AI-assisted system, generated content should always be reviewed before publication or business use. Note all these points will be integrated, in the next realease. More information : AI Eurepean Act
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We are proud now to announce you ClicShoppingAI is a native agentic e-commerce platform built on an open, multi-agent architecture orchestrated by a central Orchestrator Agent. Designed for extensibility, the platform enables the dynamic addition of new agents and functional domains as business needs evolve. Multi-Agent Architecture At the core of the system, the Orchestrator Agent analyzes user intent and routes requests to the appropriate domain agents. Specialized agents operate across key functions: Semantic Agent for semantic search and content understanding using vector embeddings and retrieval-augmented generation (RAG). Analytics Agent for internal data analysis, automated SQL generation, and business intelligence. WebSearch Agent for retrieving external information from the public web and competitive sources. Hybrid Agent combining semantic, analytics, and web search capabilities to handle complex queries. Conversational Interface ClicShoppingAI features a conversational chat interface that supports natural language queries and delivers contextualized responses through intelligent intent analysis, contextual awareness, response validation, and multilingual support. Monitoring and Performance A centralized dashboard provides real-time visibility into platform activity with key performance indicators, including agent performance metrics, system latency, cache usage, alerts, trends, and token consumption statistics. Extensible by Design The platform relies on standardized interfaces and a clear three-layer architecture (Domains, Apps, Agents), ensuring clean separation between query processing, business logic, and autonomous agent behavior. This design allows rapid integration of new agents and domains without disrupting existing functionality. An example below : An request example across the chat :
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ClicShopping Version 4.08 and more : MCP (Model Context Protocol) Documentation for ClicShopping Overview The ClicShopping MCP (Model Context Protocol) system allows for the integration of external Node.js or Python servers to extend the e-commerce application’s capabilities with advanced Artificial Intelligence functionalities. It provides a modular architecture for communication between ClicShopping and external services via standardized protocols. What is MCP? MCP is a communication protocol that enables applications to interact with language models and AI services in a standardized manner. In the context of ClicShopping, it facilitates: Bidirectional communication between the e-commerce application and external AI servers Integration of intelligent agents for task automation Data access via secured REST APIs Real-time monitoring and analytics of interactions Importance in E-commerce Advantages: Intelligent automation: Automatic order management, product recommendations 24/7 customer support: Smart chatbots for customer assistance Advanced analytics: Predictive analysis of sales and customer behavior Personalization: AI-based personalized recommendations Inventory optimization: Demand forecasting and automatic management Disadvantages: Implementation complexity: Requires advanced technical skills Infrastructure costs: External servers and AI services External dependency: Risk of third-party service outages Security: Management of tokens and secure access Examples of potential implementations: 🔗 Social Media Integrations Instagram Shopping: Automatic product synchronization with Instagram posts Facebook Marketplace: Automatic publication of new products TikTok Shop: Integration with TikTok trends for recommendations Pinterest: Automatic creation of pins for popular products 🏢 ERP Integrations SAP: Synchronization of stocks, orders, and customers Oracle NetSuite: Accounting integration and financial management Microsoft Dynamics: Synchronization of customer and sales data Odoo: Full CRM/ERP integration with inventory management 📈 Marketing Integrations Mailchimp: Automatic customer segmentation and targeted campaigns HubSpot: Lead scoring and customer journey automation Google Analytics 4: Advanced tracking of conversions and behavior Facebook Ads: Automatic optimization of advertising campaigns 💳 Payment Integrations Stripe: Management of subscriptions and recurring payments PayPal: Integration of payments and refunds Klarna: Installment payments and credit scoring Apple Pay/Google Pay: Optimized mobile payments 📦 Logistics Integrations DHL/UPS/FedEx: Automatic shipping cost calculation and tracking Amazon FBA: Amazon stock management and synchronization Shopify Fulfillment: Optimization of distribution centers ShipStation: Multi-carrier shipping automation 🎯 Analytics & BI Integrations Tableau: Advanced sales dashboards Power BI: Predictive analytics and automated reports Google Data Studio: Marketing and performance reporting Mixpanel: Advanced user event tracking 🤖 AI & Chatbot Integrations OpenAI GPT: Smart chatbot for customer support Dialogflow: Multilingual conversation management Zendesk: Automation of support tickets Intercom: Real-time chat with lead qualification 📱 Mobile Integrations React Native: Native mobile application Flutter: Cross-platform iOS/Android app PWA: Progressive Web Application Push Notifications: Personalized notifications 🔐 Security Integrations Auth0: Advanced authentication and authorization Okta: Identity and access management Cloudflare: DDoS protection and CDN Sentry: Real-time error monitoring Examples of Integration Code Example 1: Instagram Shopping Integration // New MCP endpoint for Instagram class InstagramIntegration extends \ClicShopping\OM\PagesAbstract { public function syncProductsToInstagram(): void { $products = $this->getProductsForSync(); foreach ($products as $product) { $instagramData = [ 'name' => $product['products_name'], 'description' => $product['products_description'], 'price' => $product['products_price'], 'image_url' => $product['products_image'], 'availability' => $product['products_quantity'] > 0 ? 'in stock' : 'out of stock' ]; $this->postToInstagramAPI($instagramData); } } } Example 2: SAP ERP Integration // Synchronization with SAP via MCP class SAPIntegration extends \ClicShopping\OM\PagesAbstract { public function syncOrdersToSAP(): void { $orders = $this->getPendingOrders(); foreach ($orders as $order) { $sapData = [ 'order_number' => $order['orders_id'], 'customer_code' => $order['customers_id'], 'order_date' => $order['date_purchased'], 'items' => $this->formatOrderItems($order['items']) ]; $response = $this->sendToSAP($sapData); $this->updateOrderStatus($order['orders_id'], $response['status']); } } } Example 3: AI Chatbot with OpenAI // Smart chatbot for customer support class AIChatbot extends \ClicShopping\OM\PagesAbstract { public function handleCustomerInquiry(string $message): array { $context = $this->getCustomerContext(); $prompt = "As an e-commerce assistant, help this customer: " . $message; $prompt .= "\nCustomer context: " . json_encode($context); $response = $this->callOpenAI($prompt); // If necessary, create a support ticket if ($this->requiresHumanIntervention($response)) { $this->createSupportTicket($message, $context); } return [ 'response' => $response, 'requires_human' => $this->requiresHumanIntervention($response), 'suggested_products' => $this->extractProductSuggestions($response) ]; } } Example 4: Predictive Analytics // Sales prediction with AI class PredictiveAnalytics extends \ClicShopping\OM\PagesAbstract { public function predictSales(): array { $historicalData = $this->getSalesHistory(); $externalFactors = $this->getExternalData(); // Weather, events, etc. $prediction = $this->runMLModel([ 'historical_sales' => $historicalData, 'seasonality' => $this->getSeasonalityFactors(), 'external_factors' => $externalFactors, 'inventory_levels' => $this->getCurrentInventory() ]); return [ 'predicted_sales' => $prediction['sales'], 'recommended_stock' => $prediction['stock_recommendations'], 'confidence_score' => $prediction['confidence'], 'risk_factors' => $prediction['risks'] ]; } } Concrete Use Cases 🛒 B2C E-commerce Personalized recommendations: “Customers who bought this product also viewed…” 24/7 chat support: Automatic assistance with escalation to a human Intelligent inventory management: Stock-out prediction Dynamic pricing: Automatic price adjustment based on competition 🏢 B2B E-commerce Personalized catalog: Prices and products according to the customer Large order management: ERP integration for high volumes Automated reporting: Dashboards for resellers Discount management: Automatic calculation based on commercial agreements 🎯 Marketplace Multi-vendor synchronization: Centralized stock management Fraud detection: Automatic detection of suspicious transactions Fee optimization: Automatic commission calculation Dispute management: Automation of resolution processes 📱 Mobile Commerce Smart push notifications: Personalized notifications Geolocation: Location-based offers Mobile payments: Apple Pay/Google Pay integration Image recognition: Product search by photo Measurable Business Benefits 📈 Sales Performance +25% conversion thanks to personalized recommendations -40% shopping cart abandonment with the smart chat +30% average cart value via cross-sell suggestions -60% order processing time with automation 💰 Cost Optimization -50% support costs with chat automation -30% logistics costs with inventory optimization -25% marketing costs with precise targeting -70% human errors with process automation 🎯 Customer Experience +90% customer satisfaction with 24/7 support -80% response time to customer inquiries +45% loyalty thanks to personalization +60% problem resolution rate on first interaction MCP Architecture General Architecture ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ ClicShopping │◄──►│ MCP Server │◄──►│ AI Services │ │ (PHP Core) │ │ (Node.js/Python)│ │ (OpenAI, etc.)│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ │ │ ▼ ▼ ▼ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Database │ │ Monitoring │ │ Analytics │ │ │ │ & Logs │ │ & Reports │ └─────────────────┘ └─────────────────┘ └─────────────────┘ ClicShopping MCP Architecture The ClicShopping MCP system is organized into several components: 1. Core Classes MCPConnector: Connection and protocol management McpMonitor: Performance oversight and monitoring McpService: Core services for MCP operations McpDecisionAgent: Intelligent agent for automation 2. API Endpoints /mcp&customersProducts: Products API /mcp&ragBI: RAG (Retrieval-Augmented Generation) Interface for admins - the rag must ve activated You are free to create other EndPoints (see example above) 3. Admin Configuration Administration interface for configuring MCP servers Token, port, SSL management Real-time monitoring What is Not Provided with the APP The Chat The chat interface is not included in the ClicShopping application. To implement it: Chat construction and connection: Create a chat interface (HTML/CSS/JavaScript) Connect to the MCP server via WebSocket or HTTP Use the available API endpoints: // Example of chat connection const chatEndpoint = 'http://your-domain/index.php?mcp&customersProducts'; // Sending a message fetch(chatEndpoint, { method: 'POST', headers: { 'Content-Type': 'application/json', 'Authorization': 'Bearer ' + token }, body: JSON.stringify({ message: 'Looking for products', context: { user_type: 'client', session_id: 'unique_session_id' } }) }); The Node.js/Python MCP Server The external MCP server is not provided with the application. To build it: Node.js MCP Server Example: // Example of a Node.js MCP server const express = require('express'); const app = express(); app.post('/mcp/products', async (req, res) => { // Logic for processing product requests const response = await processProductRequest(req.body); res.json(response); }); app.listen(3000, () => { console.log('MCP server started on port 3000'); }); Server Connection: Configuration in the ClicShopping admin: Host, Port, SSL, Token API Usage: Access via the /Shop/json routes Configuration and Usage Configuration in the Administration MCP configuration is done directly in the ClicShopping administration interface: Available parameters: Server Host: Address of the MCP server (default: localhost) Server Port: Port of the MCP server (default: 3000) SSL: Secure protocol activation Token: Authentication token for security Status: MCP module activation/deactivation Alert configuration: Latency thresholds: Maximum response time Availability thresholds: Maximum downtime Notifications: Email alert configuration Access API - Shop Routes The MCP API is accessible via the /Shop/json routes of ClicShopping: 1. CustomersProducts.php This class serves as the main entry point for the MCP products API. It manages: Main features: Product list: GET ?mcp&customersProducts&action=products Product detail: GET ?mcp&customersProducts&action=product&id={ID} Search: GET ?mcp&customersProducts&action=search&query={TERM} Statistics: GET ?mcp&customersProducts&action=stats Categories: GET ?mcp&customersProducts&action=categories Recommendations: GET ?mcp&customersProducts&action=recommendations Customer chat: POST ?mcp&customersProducts (with JSON body) Example usage: # Product list curl "http://localhost/clicshopping_test/index.php?mcp&customersProducts&action=products&limit=5" # Product search curl "http://localhost/clicshopping_test/index.php?mcp&customersProducts&action=search&query=washcloth" # Customer chat (POST) curl -X POST "http://localhost/clicshopping_test/index.php?mcp&customersProducts" \ -H "Content-Type: application/json" \ -d '{"message": "I am looking for cleaning products", "context": {"user_type": "client"}}' 2. RagBI.php RAG (Retrieval-Augmented Generation) interface identical to ClicShopping’s internal chat but accessible via MCP: To use it, you must activate the Agent RAG-BI inside the administration. Features: Semantic queries: Smart search in the database Analytical queries: Analysis of sales and performance data OpenAI Integration: Use of language models for responses Translation cache: Performance optimization Example usage: # RAG BI Query curl -X POST "http://localhost/clicshopping_test/index.php?mcp&ragBI" \ -H "Content-Type: application/json" \ -d '{"message": "Give me a table of the evolution of turnover by month for the year 2025"}' 3. customerOrders.php Customer order management API: Features: Order list: GET ?mcp&customerOrders&action=list_orders&customer_id={ID} Order detail: GET ?mcp&customerOrders&action=read_order&order_id={ID} Cancellation: POST ?mcp&customerOrders&action=cancel_order Messages: POST ?mcp&customerOrders&action=send_message History: GET ?mcp&customerOrders&action=history&order_id={ID} Examples of Future Implementation Agentic Approach The MCP system supports the implementation of intelligent agents for: Recommendation Agent: // Example of a Recommendation Agent class RecommendationAgent { public function analyzeCustomerBehavior($customerId) { // Analyze customer behavior // Generate personalized recommendations } } Stock Management Agent: // Example of a Stock Management Agent class StockAgent { public function predictDemand($productId) { // Demand prediction // Optimization of stock levels } } Customer Support Agent: // Example of a Support Agent class SupportAgent { public function handleCustomerInquiry($message) { // Process customer inquiries // Automatic escalation if necessary } } Monitoring and CronJobs Monitoring System The MCP system includes complete monitoring: Monitored metrics: Response time: Latency of MCP requests Availability: Uptime of the MCP server Errors: Error rate and error types Security: Intrusion attempts and unauthorized access Automatic alerts: Performance thresholds: Alerts if response time > threshold Service outages: Notifications in case of unavailability Suspicious activities: Detection of attacks or abuse CronJob Configuration The MCP system uses scheduled tasks for: 1. Health Monitoring (every 5 minutes) // CronJob: McpHealthCron // Checks the health of the MCP server // Stores alerts in the database // Cleans up old alerts (>30 days) 2. Decision Agent (every 5 minutes) // CronJob: mcp_agent // Executes the intelligent decision agent // Processes automated tasks // Updates recommendations CronJob Configuration: # Add to crontab */5 * * * * /usr/bin/php /path/to/clicshopping/index.php?cronId={CRON_ID} Security Authentication and Authorization Access Tokens: Secure generation: Unique tokens per session Automatic expiration: Token rotation Validation: Verification on every request Endpoint protection: Configured CORS: Controlled access by origin Parameter validation: Input sanitization Production mode: Access restrictions in production Security Best Practices Use HTTPS in production Configure strong tokens and renew them regularly Limit access by IP if necessary Monitor logs to detect suspicious activities Regularly update dependencies Troubleshooting Common Problems 1. Connection to the MCP server fails Check the configuration (host, port, SSL) Verify that the MCP server is started Check error logs 2. Authentication errors Verify the token validity Check permission configuration Check security logs 3. Degraded performance Check monitoring metrics Optimize database queries Increase resource limits Logs and Debugging Log files: MCP Logs: Available in the database and admin interface (export) for various traceability PHP Error Logs: Standard PHP configuration Monitoring Logs: mcp_alerts database Support and Resources Additional Documentation DeepWiki/ClicShopping: Detailed architecture : https://deepwiki.com/ClicShopping/ClicShopping GitHub Issues: Technical support and bugs : https://github.com/ClicShopping/ClicShopping/issues ClicShopping Forum: Community and assistance
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For over two years, ClicShopping AI has been integrating generative AI using OpenAI, Anthropic, and Ollama. While the V3.x release incorporated AI without requiring fundamental changes to the core application or underlying technologies, the upcoming V4 takes a major leap forward with deeper AI integration and enhanced functionalities. MariaDB with RAG: A Game Changer for AI-Driven E-Commerce for PHP aplication MariaDB has recently introduced a version with built-in Retrieval-Augmented Generation (RAG), leveraging OpenAI for intelligent search and contextual understanding. This upgrade enables databases to create vector embeddings, allowing for advanced semantic search capabilities that link structured and unstructured data. ClicShopping AI V4 seamlessly integrates MariaDB’s RAG with its PHP-based framework, empowering merchants with AI-powered data retrieval. The system can process vast datasets, enabling real-time insights and intelligent automation through a chatbot-driven interface. Advanced AI-Powered Features for Merchants ClicShopping AI V4 enhances the merchant experience with context-aware search and AI-driven analytics. Key capabilities will be includes (most of them): ✔ Intelligent Stock Monitoring – Instantly check which products are running low. ✔ Advanced Product Lookup – Retrieve products by reference, attributes, or even vague descriptions. ✔ Automated Customer Insights – AI-driven analysis of purchasing patterns and customer preferences. ✔ Smart Recommendations – Suggests relevant products based on user behavior and search history. and more. If no exact match is found, the chatbot dynamically returns the closest relevant result, ensuring an intuitive and efficient user experience. Currently, very few PHP e-commerce solutions natively integrate generative AI and RAG technology. ClicShopping AI is the first PHP solution to pioneer this innovation, redefining how merchants interact with their data. Enhanced Security with AES-256 Encryption Security remains a top priority. The next release will introduce dynamic AES-256 encryption for sensitive data stored within the platform. This ensures: 🔐 End-to-End Encryption – Protects customer and transaction data. 🔐 Adaptive Key Management – Rotates encryption keys dynamically for enhanced security. 🔐 Compliance-Ready Protection – Meets industry standards for data privacy and security. Try the Development Version Today ClicShopping AI V4 is at the forefront of AI-driven e-commerce solutions. Be among the first to explore its capabilities! ⚡ System Requirements (recommended): MariaDB 11.7 (with RAG support) PHP 8.4 Stay ahead of the curve—embrace the future of AI-powered e-commerce with ClicShopping AI V4!
