About the Project

A leading member network struggled to find a reliable system to integrate their social network and knowledge base for AI-powered answers. Previous attempts were unreliable and failed to meet the critical need for high data accuracy.

N3XTCODER was engaged to develop a production-ready RAG (Retrieval-Augmented Generation) chatbot that could deliver accurate, real-time answers to over 1,000 network members.

Our Services

• System Architecture Consultation
• RAG System Development
• Vector Database Configuration
• Semantic Search Setup
• HumHub Integration
• Training & Documentation

The Challenge

The network faced several obstacles:

• Unreliable previous solutions: Earlier AI assistant attempts failed to meet accuracy requirements
• Critical need for data accuracy: Members needed to trust the answers they received
• HumHub integration: The solution had to work within their existing social network
• Team independence: Non-technical staff needed to maintain and expand the system

The Solution

We developed a prototype RAG (Retrieval-Augmented Generation) Chatbot using a low-code architecture designed for easy operation and expansion without requiring in-depth programming skills.

The entire project was delivered in 4 short sprints, covering system architecture, functional RAG implementation with semantic search, and comprehensive documentation for independent maintenance.

Tech Stack

• Workflow Automation: n8n (Berlin-based, SaaS/PaaS)
• Vector Database: Qdrant (Berlin-based, EU-hosting)
• LLM: OpenAI GPT-4 (via Microsoft EU hosting for data sovereignty)

The Outcome

Version 1 of the RAG Chatbot is now running in production:

• 1,000+ members receive time-aware, highly accurate, real-time answers
• HumHub integration: Chatbot works directly within their existing social network
• Data reliability: Low-code architecture ensures consistent, trustworthy responses
• Independent maintenance: Team can operate and expand the system without developer support