How I Built My AI Chatbot Using n8n and Elementor

Over the last few weeks, I’ve been building a custom AI chatbot from the ground up, not just a basic assistant, but one designed to provide high-quality, structured, and ethical responses in a very specific domain. It is developed for a non-profit as part of my voluntary effort, to give back.

This post is a behind-the-scenes look at how I built it: from automated data ingestion to AI reasoning and frontend integration. It’s powered entirely by n8n for the backend workflow logic and Elementor(WordPress website builder) for the frontend interface. While I’ll reveal more about the chatbot's identity and its unique purpose in a future article, this one focuses on the technical process.

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Why Build a Custom AI Chatbot?

With tools like ChatGPT and Gemini already out there, why bother building your own? The answer lies in control and customisation. I needed a solution that:

• Responds based only on approved content

• Avoids hallucination and speculation

• Includes multilingual disclaimers and reliability tags

• Can integrate directly into my website with a tailored UI

That meant building from the ground up and n8n was the perfect tool for the job.

Architecture Overview

The system is composed of two main n8n workflows:

1. Data ingestion pipeline: to process and embed source documents.

2. Chatbot interaction engine: to manage real-time user queries.

Let’s break them down.

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1) Workflow: Automated Data Ingestion

This workflow ensures the chatbot always has access to the latest, most reliable source material.

Step 1: Trigger on New Uploads

A Google Drive Trigger monitors a folder. When a new file (like a guideline or scientific document) is uploaded, the workflow begins.

Step 2: Download & Process

The file is downloaded and processed through OpenAI’s text-embedding-3-large model to generate vector embeddings.

Step 3: Store in Pinecone

The embeddings are stored in a Pinecone vector database, organised under a specific namespace for later retrieval.

Text is chunked and metadata-tagged to improve semantic search.

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2) Workflow: AI-Powered Chatbot Interaction

This is what powers the chatbot users interact with.

Step 1: Message Trigger

A ChatTrigger node in n8n captures messages from the frontend interface.

Step 2: LangChain Agent

A LangChain Agent is set up with:

• A clear scope of what it can and cannot answer

• A non-technical, empathetic tone

• Built-in multilingual disclaimers

• Transparency levels (A, B, C) for content sources

Step 3: Memory, Retrieval, and Response

The agent uses short-term memory to maintain context.

If needed, it queries Pinecone using a vector store tool, retrieves the most relevant content, and passes it to OpenAI’s gpt-4o-mini to generate the final reply.

Frontend Integration with Elementor

To make the chatbot feel native to my website, I used HTML element. Here’s how I connected everything:

• Created a custom floating chat window with HTML + JS

• Connected it to the n8n webhook

• Made it responsive and cleanly styled

• Ensured every response includes a disclaimer

No third-party UI tools, no extra hosting, just pure control.

Names are hidden as the chatbot is not publicly live yet.

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Built-In Safety and Guardrails

Accuracy and ethical design were top priorities. Here's how the chatbot stays safe:

• No advice, no diagnosis, no speculation

• Politely redirects questions that are out of scope

• Every response includes a source reliability tag and a multilingual disclaimer

• If a reliable answer isn’t possible, it clearly says so

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What’s Next?

In my next article, I’ll reveal the identity and mission of this chatbot. who it’s designed to help, and the meaning behind its name.

Hint: it’s not just another assistant. It’s designed to help people make sense of something complex and important, but you’ll find out more soon.

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Final Thoughts

This project has taught me how powerful it is to combine AI with low-code tools like n8n and Pinecone.With thoughtful design and the right architecture, you can build something:

• Scalable

• Safe

• Purpose-driven

Thanks for reading. I hope this inspired you to experiment or at least left you curious about what’s coming next. 😉