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11n8n

DayByDay - RAG Chatbot Agent

Role: AI Developer - retrieval pipeline, agent orchestration, and injection defence. Front-end built by other team members. The system. Customer support questions are answered from a Pinecone vector database holding the company's domain knowledge. The orchestration runs entirely in n8n, with four GPT-5.6 Luna agents dividing the work. Luna was chosen for its speed and cost profile, a multi-agent pipeline means several model calls per user message, so a fast, inexpensive model keeps both latency and per-conversation cost viable at support volume.

Live

Gallery

DayByDay - RAG Chatbot Agent — image 1

Features

  • RAG Implementation
  • Prompt Injection Blocks
  • IP Black List
  • Multi-agent orchestration

Technologies

N8NCloudflareAzure FoundryJiraSMTP

Challenges

During this project one of the main challenges that I had to face was the IP blocking system. This is system that took the most time to build and test. After the system was built it was able to prevent the agent from being attacked in an actual prompt injection attack.

Outcome

Project shipped successfully.