Why I Built an AI Assistant for My Blog

I wanted my blog to do more than list posts. I wanted visitors to be able to ask natural questions about me, my work, and anything I’ve written, and get answers that cite the right articles without me hand. In my previous post I laid the infrastructure groundwork by running n8n on Azure as an orchestration layer. This article goes deeper into how I assembled the chat assistant itself and wired it to semantic search so it actually “knows” my content rather than doing a brittle keyword lookup.

Last week I joined Geospatial FM, the podcast hosted by Wilfred Waters, to talk about AI agents and the Azure Maps MCP server I had created and bloged about.

We touched on how Bing Maps is the familiar public-facing mapping service, while Azure Maps is the developer platform for bringing mapping, routing, traffic, and spatial analytics into enterprise and IoT apps. The heart of our conversation was about Model Context Protocol (MCP) and why it matters. MCP lets AI agents use tools and pull fresh data from APIs, so instead of guessing about roads, traffic, or places, an agent can call Azure Maps in real time.

A lightweight Azure backend for my AI agent

Over the past few weeks, I’ve been exploring different ways to power a personal AI agent for my blog, one that can answer questions about me, my background, and my work using context I provide. I wanted a simple, secure, and cost-effective backend that I fully control and can iterate on fast.

n8n is a powerful open-source automation tool that’s perfect for wiring together APIs and logic without having to spin up tons of infrastructure.

Imagine this: you work at a company, and you have a clear idea for a web app, something like a custom expense tracking tool that fits exactly the way your team works. You’ve tried off-the-shelf products, but they always miss the mark. The alternative, building your own application, is usually time-consuming, expensive, and requires getting developers involved early on. But what if you could prototype your idea, adjust the UI, change the data model, and publish it, all without writing a single line of code?

AI is no longer an abstract promise of the future. It’s here, embedded into enterprise workflows, products, and decision-making processes. From Microsoft Copilot to ChatGPT and domain-specific assistants, businesses are adopting AI at an unprecedented pace. But too often, “AI” is used as a catch-all term for a wide range of technologies. To lead the next transformation wave, organizations must move beyond generic AI adoption and toward agentic AI, a more autonomous, goal-driven form of AI that’s ready to take on real work.

In today’s AI era, you’ve likely interacted with Microsoft Copilot, ChatGPT, or Claude.ai, tools powered by advanced Large Language Models (LLMs). These models excel at understanding and generating human-like text based on vast amounts of training data. However, while LLMs are impressive at reasoning and answering general questions, they fall short when it comes to performing real-world tasks or retrieving live, domain-specific information.

This is where the Model Context Protocol (MCP) comes in.