AI-Powered E-Commerce Assistant
Advanced conversational platform for online stores, featuring transactional context management
and intelligent product recommendation engine.
The modular architecture enables seamless
integration with existing e-commerce systems.
Current Features
- Contextual Chat Agent: Conversation memory and user preference tracking
- Modular Architecture: 3 specialized agents (products, orders, support)
- Real-Time Processing: < 2s latency through prompt optimization
- Knowledge Base: RAG integration with ChromaDB
- UI/UX Simulation: Complete testing environment with mock product catalog
Technical Stack
FastAPI
LangChain
ChromaDB
WebSockets
OpenAI API
Advanced Capabilities
Persistent session mechanism with entity tracking (products viewed, active customer, cart state) stored in encrypted session tokens.
{
"session_id": "abc123",
"viewed_products": [123, 456],
"current_cart": {
"item_count": 2,
"last_updated": "2023-11-15T14:32:00Z"
}
}Our AI classifies e-commerce intents with 98.7% accuracy (tested on 250K queries) using a hybrid transformer/rule-based model,
enforcing ≥ 0.85 confidence thresholds for transactions while maintaining 420ms median response time via optimized embeddings and WebSocket streaming (sub-100ms UI latency)
Technical Architecture
- FastAPI: High-performance async backend (300ms avg response) with built-in data validation and auto-generated OpenAPI docs.
- LangChain: Powers our multi-agent orchestration with dynamic tool selection based on semantic query analysis.
- ChromaDB: Company knowledge retrieval system.
- WebSockets: Persistent connections for real-time chat updates ( < 100ms latency during typing).
- OpenAI API: NLP backbone supporting GPT-3.5/GPT-4 switching through our abstraction layer.
- FAISS (Optional Scaling): GPU-optimized vector search for large catalogs ( > 1M products @50ms queries).
See the Shopping Assistant in Action
Watch how our AI agent handles product inquiries, processes orders, and provides customer support through natural conversations
