CartBuddyGPT

Converts free-form natural language questions into structured queries against e-commerce databases without requiring SQL knowledge. Uses NLP intent classification to map user questions (e.g., 'show me low-stock items across all stores') to parameterized database queries, with semantic understanding of domain-specific terminology like SKU, inventory levels, and order status. The system maintains a schema mapping layer that translates natural language field references to actual database columns across heterogeneous storefront systems.

Aggregates inventory data from multiple e-commerce platforms (Shopify, WooCommerce, custom APIs, etc.) into a unified data model through connector-based ETL pipelines. Each storefront connector handles platform-specific authentication, pagination, and data format translation, normalizing disparate inventory schemas into a canonical representation. Real-time or scheduled sync mechanisms maintain consistency across sources, with conflict resolution for duplicate SKUs across channels.

Generates interactive data visualization dashboards from natural language descriptions of desired metrics and layouts. The system interprets requests like 'show me sales by category over time with a pie chart' and automatically selects appropriate chart types, aggregation functions, and data bindings. Uses a template-based rendering engine that maps chart specifications to visualization libraries (likely D3.js, Chart.js, or similar), with real-time data binding so dashboards update as underlying inventory/sales data changes.

Maintains multi-turn conversation context to enable follow-up questions and drill-down analysis without re-specifying filters or context. The system uses a conversation state machine that tracks previously queried datasets, applied filters, and user intent history, allowing users to ask 'show me the top 5' after 'what products are low stock' without repeating the low-stock filter. Implements a sliding context window (likely 5-10 previous turns) to manage token usage and relevance.

Automatically identifies discrepancies between order records across multiple storefronts (e.g., order placed on Shopify but not synced to inventory system, duplicate orders from same customer across channels). Uses statistical anomaly detection algorithms (likely z-score or isolation forest) to flag unusual patterns like sudden order spikes, price mismatches, or inventory deductions without corresponding sales. Provides reconciliation recommendations and audit trails for compliance.

Automatically assigns product categories, tags, and attributes based on product names, descriptions, and images using multi-modal ML models. The system analyzes text descriptions and product images to infer category hierarchies, generate SEO-friendly tags, and populate structured attributes (size, color, material, etc.) without manual data entry. Supports bulk categorization of new product imports and can learn from user corrections to improve accuracy over time.

Forecasts future product demand using historical sales data, seasonality patterns, and external signals (holidays, promotions, trends) to recommend optimal inventory levels. The system applies time-series forecasting models (likely ARIMA, Prophet, or neural networks) to predict demand 7-90 days ahead, then calculates reorder points and safety stock recommendations based on lead times and service level targets. Integrates with inventory data to highlight products at risk of stockout or overstock.

Allows users to define automation rules through conversational natural language rather than visual workflow builders or code. Users describe desired automations (e.g., 'when a product goes below 10 units, create a purchase order and notify the manager') and the system translates these into executable workflow rules with conditional logic, actions, and notifications. Supports integration with connected storefronts and external services (email, Slack, webhooks) through a rule execution engine.