# Case Study: Implementation Plan

**From technical requirements to buildable MVP scope.**

We've validated the problem and designed the technical approach. Now we need to figure out what we'll actually build first and how we'll deal with real-world constraints.

This is where most AI projects either succeed or fail - in the gap between what sounds good on paper and what you can actually ship.

## The Constraints

Building this thing means dealing with constraints that will affect what you can actually ship.

**Cost** is the obvious one. LLM API calls add up fast, especially when you're generating personalized insights for lots of users. You need to balance insight quality with what you can afford to run.

**Privacy regulations** are huge for financial data. Legal compliance shapes how you store, process, and share data. Some features that work technically might not be allowed legally.

**Integration headaches** come from banks that don't make data access easy. Some users will need to upload data manually, which affects both user experience and how current the data is. The system needs to work well even with incomplete information.

!!! tip "Data Collection Path"
    There's a clear progression: (1) Manual upload (CSV, Excel), (2) Bank statement reading (PDF parsing), (3) API integrations. Start with #1 to validate the insights and interaction before making data collection easier.

## What We're Actually Building

Here's what makes sense for a first version that can actually help Alex understand where their money goes:

You upload your bank transactions (CSV or Excel files), ask questions in plain English like "Where does my money go?" or "Why did I spend so much last month?", and get personalized insights you can understand and act on. That's it.

!!! note "MVP Focus"
    Validate that the insights and interaction actually help before investing time in user management, data collection automation, or scaling concerns.

Here's the minimal tech stack we need:

<div class="grid cards" markdown>

-   :material-file-upload:{ .lg .middle } **Data Upload**

    ---

    CSV/Excel file processing and validation

-   :material-tag:{ .lg .middle } **Transaction Analysis**

    ---

    Automatic categorization and spending pattern detection

-   :material-robot:{ .lg .middle } **AI Integration**

    ---

    LLM integration to understand questions and generate responses

-   :material-web:{ .lg .middle } **Simple Interface**

    ---

    Ask questions in plain English - no login, no accounts, no persistence

</div>

No login, no user accounts, no persistent storage - just prove the core concept works.

We'll know it's working when people can upload their transactions without getting confused, questions get helpful answers that make sense, and the insights actually help them understand their spending. User accounts and persistence come later - first we prove the AI interaction is valuable.

## What We're Definitely Not Building

Just as important is what we're leaving out:

No automatic bank connections (too much regulatory complexity), no investment tracking or advice (different skill set entirely), no bill pay or transaction execution (regulatory and security headaches), no social features or spending comparisons (privacy issues), and no advanced financial planning or tax help (need specialized expertise).

These features might be valuable later, but they're not needed to test the core idea. The goal is proving that people actually want personalized financial insights they can get by asking questions in plain English.

## Where We Stand

We have everything we need to start building: we know what success looks like, what to build first, how to measure progress, and what constraints we're dealing with.

The next step is turning this plan into working code. That means picking technologies, designing the detailed architecture, and building the features that actually help users like Alex.

!!! success "Key Insight"
    Start with real problems and use technology to solve them, rather than starting with cool technology and hunting for problems to apply it to.

We've worked through the complete process: identifying a real problem, checking whether AI actually helps, and figuring out what to build. This same thinking process works for any AI application - the specific technologies change, but the approach stays the same.

When you find a problem that actually benefits from AI capabilities - like our finance example with its need for natural language understanding and personalized insights - you can build something people will use.

[Continue to Section 2: Building Your First AI Application →](/learn/2-building-your-first-ai-application/){ .md-button .md-button--primary }