EccoAI
Intelligent RAG Chatbot for Customer Support
A production RAG (Retrieval-Augmented Generation) chatbot system built with Flask and Pinecone vector database, providing instant, contextually-aware customer support by semantically searching company knowledge bases.
Platform Demo
The Challenge
Businesses struggled with providing 24/7 customer support while maintaining quality and accuracy. Traditional chatbots often gave generic responses that frustrated users, while human support was expensive and limited by business hours. Companies needed a solution that could understand context, access their specific knowledge base, and provide helpful, accurate responses at any time.
Key Problems to Solve:
- Limited Availability: Human support only available during business hours
- Generic Responses: Traditional chatbots lacked context and gave unhelpful answers
- High Costs: Scaling human support was expensive and unsustainable
- Knowledge Access: Difficulty accessing company-specific documentation and policies
- Response Times: Hours-long wait times frustrated customers and hurt satisfaction
Our Solution
We developed EccoAI, a production RAG (Retrieval-Augmented Generation) chatbot system built with Flask backend and Pinecone vector database for semantic search. The system ingests company documentation and converts it into vector embeddings stored in Pinecone, enabling contextually-aware responses by semantically searching the most relevant knowledge base content. The chatbot embeds directly into websites via customizable HTML/CSS widget.
Built on Heroku for scalable deployment, the system uses PostgreSQL for conversation history and user management, while Pinecone handles the vector similarity search that powers intelligent question answering.
Key Features Built:
Vector Database RAG Architecture
Pinecone vector database for semantic search enabling contextually accurate responses.
Knowledge Base Embedding
Ingests and vectorizes company documentation, FAQs, and policies for semantic retrieval.
Customizable Website Widget
HTML/CSS embeddable widget with customizable branding and positioning.
Conversation History
PostgreSQL database storing full conversation context for improved responses.
Flask REST API
RESTful API endpoints for chat interactions, embeddings, and analytics.
Scalable Heroku Deployment
Production-ready hosting with automatic scaling and reliability.
Technology Stack:
- Backend Framework: Flask (Python) with RESTful API architecture
- Vector Database: Pinecone for semantic similarity search and vector embeddings
- Relational Database: PostgreSQL for conversation history, user management, and analytics
- Frontend Widget: HTML, CSS, JavaScript for embeddable chatbot interface
- AI/ML: OpenAI GPT models, LangChain for RAG pipeline, vector embeddings
- Hosting & Deployment: Heroku for scalable cloud hosting with automatic scaling
- API Integration: RESTful endpoints for chat, document ingestion, and analytics
Results & Impact
EccoAI successfully answered over 8,000 customer questions with instant, accurate responses. The AI-powered system reduced response times from hours to seconds, dramatically decreasing support workload while maintaining high customer satisfaction. Businesses gained 24/7 intelligent support capabilities, allowing their teams to focus on more complex customer needs.
Key Metrics:
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