# I Want to Build a Proof of Concept: First AI Application

**From validated problem to working FastroAI application.**

You've identified a problem worth solving and validated that AI can actually help. Now you need to turn that understanding into a working application that people will actually use.

This section covers going from "I know what to build" to "I built something that works."

!!! tip "Real Implementation, Not Tutorials"
    We're not building toy examples or demos. You'll create an application using FastroAI that can handle real users, real data, and real business requirements.

## What You'll Actually Build

By the end of this section, you'll have a working AI application that demonstrates all the core patterns for building AI applications:

<div class="grid cards" markdown>

-   :material-brain:{ .lg .middle } **FastroAgent**

    ---

    Build conversational AI agents using FastroAgent, FastroAI's wrapper around PydanticAI with memory management, usage tracking, and tool integration built-in.

-   :material-database:{ .lg .middle } **Data Architecture**

    ---

    Design database schemas and data flows that work with AI agents while maintaining data integrity and system performance.

-   :material-api:{ .lg .middle } **APIs**

    ---

    Create FastAPI endpoints that handle validation, error handling, and integration with AI processing.

-   :material-monitor:{ .lg .middle } **Testing**

    ---

    Develop testing strategies for AI applications where outputs aren't deterministic, and learn how to iterate based on user feedback.

</div>

We'll build the Personal Finance Assistant from Section 1 using the FastroAI template. This gives you hands-on experience with the patterns while creating something that actually solves the problem we validated.

The approach is practical: instead of explaining concepts in isolation, you'll see how FastroAI's features work together to solve real challenges. FastroAgent's memory management, usage tracking, entitlement system, and API design all come together to create a working application.

## How We'll Build This

We're going to build the Personal Finance Assistant from Section 1. Section 1 gave us validated requirements and a clear implementation plan. Now we turn that plan into working code.

First, we'll translate our requirements into specific technical architecture. We know what to build - now we need to figure out exactly how to build it.

Then we'll build a FastroAgent that can handle questions like "Where does my money go?" This is where you'll see FastroAI's value - conversation memory that actually works, usage tracking that happens automatically, and prompts that give consistent results.

Next comes the boring but essential stuff: database design and APIs. We need to store financial data and conversations, then build endpoints that let users upload transactions and ask questions. You'll see how FastroAI's entitlement system handles different user tiers without you writing complex access control logic.

Finally, we'll tackle the testing problem. How do you test something that gives different answers each time? We'll cover strategies that actually work for AI applications without writing brittle tests that break every time the AI decides to phrase something differently.

## The Real Work

AI tutorials show you how to call an API or train a model. When you try to build something real, you discover that calling the AI is maybe 10% of the work.

!!! warning "The Infrastructure Problem"
    Real AI applications need user authentication, conversation storage, usage tracking, billing, rate limiting, error handling, monitoring, and deployment infrastructure. Tutorials skip all of this.

When you build an actual AI application, you spend your time on:

- User authentication and session management
- Storing conversation history efficiently
- Tracking how much users are spending on API calls
- Managing different subscription tiers and permissions
- Handling errors when the AI returns garbage
- Deploying and monitoring the whole system

FastroAI handles this infrastructure so you can focus on making your AI actually useful. Instead of spending months building authentication and billing systems, you get to work on the problem you're trying to solve.

## About FastroAI

We're using FastroAI because it handles all the boring infrastructure stuff that would otherwise take months to build. You get authentication, billing, AI integration, and deployment - all the pieces that aren't your core product but are necessary to ship something real.

Here's what saves you the most time when building the Personal Finance Assistant:

<div class="grid cards" markdown>

-   :material-brain:{ .lg .middle } **FastroAgent**

    ---

    AI conversation handling with memory, usage tracking, and tool integration. You don't write conversation management code - it's already done.

-   :material-account-key:{ .lg .middle } **Authentication System**

    ---

    Login with email, Google, or GitHub. Session management, password resets, OAuth flows - all working out of the box.

-   :material-credit-card:{ .lg .middle } **Payment Processing**

    ---

    Stripe integration with subscriptions, credits, one-time purchases. Webhooks handle subscription changes automatically.

-   :material-shield-check:{ .lg .middle } **Entitlement System**

    ---

    Tier-based access control that lets you offer different features to different subscription levels without complex logic.

</div>

FastroAI is a commercial template you buy once and use for unlimited projects. The alternative is spending months building authentication systems and billing infrastructure instead of working on your AI application.

[Get FastroAI](https://fastro.ai){ .md-button .md-button--primary }

## What You Should Know Before Starting

We're going to build a real application, not a tutorial demo. That means working with databases, APIs, and deployment concerns that come up in actual projects.

!!! info "Prerequisites Check"
    Don't worry if you're missing some of these - you can still follow along and learn the concepts. You'll just need to look up specific syntax or patterns as we go.

Here's what will help you get the most out of this section:

- **Section 1 context** - You understand why we're building a Personal Finance Assistant and what problems it solves. This context drives every technical decision we make.

- **FastAPI experience** - You can build REST APIs and understand async/await patterns in Python. We'll be creating endpoints, handling requests, and managing database connections.

- **Database basics** - You're comfortable with SQL and relational database design. We need to store financial transactions, user conversations, and system data in ways that perform well and maintain data integrity.

The implementation details will make more sense if you have this background, but the architectural patterns and AI integration concepts apply regardless of your specific tech stack.

[Start with Requirements to Architecture →](requirements-to-architecture.md){ .md-button .md-button--primary }
