Building an AI Application in 2025: How to go from Zero to MVP in record time

I’ll walk you through the tools I’d use, my thought process, and how to stay focused every step of the way. Ready?

It’s 2025. You’ve got an exciting idea for an AI app, but there’s one big question: how do you get started? With so many tools and frameworks to choose from, it can feel like an overwhelming maze. All you really want to do is build something that works—a Minimum Viable Product (MVP)—and do it fast. So, how do you make that happen?

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Step 1: Defining the Problem

The first thing you need to do is figure out exactly what problem your app is solving. Picture this: you’re sitting down with a blank page, jotting down the core idea. Maybe your app will help small businesses write professional emails quickly. Or maybe it’ll take massive PDFs and turn them into bite-sized summaries. Whatever it is, it needs to be crystal clear.

Remember, when building an MVP, simplicity is your best friend. The key is to solve one specific problem really well. If you try to build a feature-rich app right from the start, it’ll never work as intended. Focus. Start small.

Step 2: Choosing Your Stack

With your problem clearly defined, it’s time to pick your tools. You don’t need to spend hours or days comparing libraries and frameworks. Just pick the ones that help you get to your MVP quickly. Here’s what I’d use:

• Python: A versatile and powerful programming language. It’s the backbone of AI apps and has a library for almost anything you’ll need.

• FastAPI: My backend framework of choice. It’s modern, fast, and built for asynchronous operations—perfect for building efficient APIs.

• Milvus: Milvus is the ideal vector database for unstructured data.

• LangChain: To chain AI models, LangChain makes the process seamless.

• Ruff: A fast Python linter (written in Rust) and formatter that keeps your code clean and efficient without slowing you down.

• Uv: A modern package manager that handles dependencies and accelerates project setup (also written in Rust).

• Streamlit: If your MVP requires a user interface, you don’t need complicated frontend frameworks. Instead, use Streamlit. It’s perfect for quickly building data-driven apps with minimal code.

• Docker: Docker allows for consistent, portable, and efficient deployment of applications across different environments by containerizing them with all their dependencies.

This stack is modular, simple, and designed for speed. It eliminates unnecessary complexity so you can focus on building your MVP, not worrying about infrastructure or endless configuration.

Step 3: Building the Backend

With the problem defined and tools chosen, it’s time to roll up your sleeves and start building. First, set up FastAPI to create endpoints for your app. This is where the core functionality of your app will reside. Whether it’s generating email drafts or summarizing PDFs, FastAPI makes it easy to expose these functionalities through APIs.

Next, you’ll need a place to store your data. Enter Milvus—a vector database designed specifically for AI embeddings. If you’re building an MVP, you’ll want to use a managed version of Milvus, like Zilliz Cloud. Why? Because it lets you skip the setup and infrastructure headaches, so you can focus on the app itself.

Once Milvus is set up, LangChain comes in to handle your AI logic. It makes it easy to manage the interactions between various AI models or APIs. If your app uses a large language model (LLM) to generate responses or perform tasks, LangChain ensures everything runs smoothly.

Remember, this is an MVP, so test everything locally before adding too many features. Build the core functionality first, and keep it as simple as possible. Don’t worry about perfection at this stage—just ensure the MVP solves the problem you defined earlier.

Step 4: Building the Frontend with Streamlit

Now, let’s talk about the frontend. If your MVP requires a user interface, you don’t need complicated frontend frameworks. Instead, use Streamlit. It’s perfect for quickly building data-driven apps with minimal code.

Streamlit lets you focus on functionality rather than spending days working on layouts or complex UI elements. You can create interactive UIs in no time, making it easy for users or stakeholders to engage with your app. It’s simple, fast, and works perfectly for showcasing an MVP.

Think about it: you could build a small, functional web app where users can input text, click a button, and get an AI-generated email in return—all without needing to write HTML, CSS, or JavaScript.

Step 5: Data and Testing

Your MVP is coming together, but now it’s time to make sure everything works as expected. Start by populating Milvus with mock data—use sample inputs to generate embeddings. This allows you to test how the system will perform without having to deal with real data just yet.

Once your data is set up, you’ll want to test your AI logic. Does it produce meaningful results? Is the app solving the problem it was built for? Don’t skip this step—get real feedback on whether the app works before moving forward.

Add unit tests to verify that each part of your system is functioning correctly. This will help catch bugs early. And while you’re at it, use Ruff to make sure your code is clean and consistent. It’ll keep everything neat, helping you avoid wasting time on small formatting errors.

Step 6: Deployment

Okay, your MVP is ready. Now, it’s time to get it into the hands of real users. The easiest way to deploy your app is with Docker. Docker helps you containerize your app, making it portable and easy to deploy to different platforms.

Once your app is containerized, you can deploy it to any platform that supports Docker, whether it’s a cloud service or a local server. With Docker, you don’t have to worry about compatibility issues. Plus, you can easily scale your app as needed.

Step 7: Feedback Loop

Here’s where the real magic happens: feedback. Once your MVP is live, start gathering feedback from real users or stakeholders. Don’t wait for everything to be perfect—ship your MVP as soon as possible and see how it performs in the real world.

Ask yourself and your users these critical questions: Does the app solve the problem? Is it intuitive? What features are missing? Use the feedback you get to improve the app in the next iteration.

Final Thoughts

Building an AI MVP in 2025 is all about using the right tools to solve one problem quickly and efficiently. With the stack I’ve shared—FastAPI, Milvus, LangChain, and others—you can go from an idea to a working MVP in no time.

The goal is to stay focused, avoid unnecessary complexity, and iterate based on real-world feedback. Your MVP doesn’t need to be perfect; it just needs to work. Get it out there, gather feedback, and keep improving. The tools are there to help you, so all that’s left is the drive to get it done.

Until next time,
Adlet

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