# How I Built a Local AI Orchestrator and City AI: My Journey as a Developer

> Source: <https://dev.to/lostxmusafir/how-i-built-a-local-ai-orchestrator-and-city-ai-my-journey-as-a-developer-46ea>
> Published: 2026-07-09 20:41:30+00:00

Hi, I'm **Raj Patil** (known online as **Dream** / **lostxmusafir**), an AI Engineer and Full-Stack Developer. In this article, I want to break down how I developed two of my most impactful projects: **Local AI Orchestrator** and **City AI**.

You can find my full interactive portfolio here: [Raj Patil AI Portfolio](https://rajpatil-port.vercel.app/).

With growing data privacy concerns, relying on cloud-based LLM APIs isn't always feasible for enterprise applications. I built the **Local AI Orchestrator** to solve this. It's a completely offline, privacy-first AI system that runs locally on consumer hardware like my NVIDIA RTX 3050.

Optimizing LLM quantization (GGUF formats) was critical to achieving sub-100ms token generation times on a local laptop GPU. It proved that robust, responsive AI applications don't always need expensive cloud servers.

**City AI** is a platform built to automate municipal feedback loops. When citizens submit complaints (like street light outages or road damage), the system categorizes, prioritizes, and routes them to the correct local government departments automatically.

I believe the future of software lies at the intersection of high-performance web applications and intelligent AI orchestrations. Whether it's building a full-stack food delivery system (like my **Swiggy Clone**) or engineering cross-platform apps using **Flutter** (like my mobile civic client **Place.ai**), my objective is to make software feel alive, smart, and exceptionally fast.

Feel free to check out my open-source code and reach out if you'd like to collaborate on building next-generation AI platforms!