Building an AI-Powered Laravel + MongoDB App: A Walkthrough Using Laravel Boost

Imagine you’re building a new feature for your product.

Nothing huge—just a smarter way to explore property data, something like a lightweight Airbnb browser.

But instead of writing complex queries, you want developers to ask questions in natural language, like:

“Show me apartments in Barcelona under $100.”
“Find places in Porto with WiFi and at least 2 bedrooms.”

No syntax to remember.

No guesswork.

Just intent → result.

In this tutorial, we’ll build a small but powerful project that does exactly that using:

• Laravel 12.
• MongoDB Atlas.
• Laravel Boost for natural-language queries.
• AI-discoverable helper methods that become part of your domain.

Instead of presenting a list of disconnected steps, we’ll build this project as a narrative, where each step emerges naturally from the needs of the previous one.

Let’s begin.

#What we’re building

Our goal is to create Airbnb Explorer, a small Laravel module capable of:

• Querying real property data from MongoDB’s sample_airbnb dataset.
• Answering natural-language questions using Laravel Boost.
• Generating insights using MongoDB aggregations.
• Providing reusable, AI-discoverable domain helpers.
• Supporting a modern, intention-driven development workflow.

This won’t be “just another CRUD.”

It’s a progressive journey from idea → usage → abstraction → insight.

#Why MongoDB for this project?

Before diving into code, it’s worth explaining why MongoDB is an ideal fit for this kind of exploratory, AI-assisted application.

#1. Flexible document schema

Each listing in the sample_airbnb dataset contains many fields:

• Nested objects (like address)
• Arrays (like amenities)
• Optional fields
• Varying structures across documents

MongoDB’s document model lets you store and query these structures naturally, without migrations or rigid schemas.

For exploratory development and AI-generated queries, this flexibility is extremely valuable.

#2. Rich query capabilities

MongoDB offers:

• Powerful filtering.
• Geospatial queries.
• Full aggregation pipelines.
• Faceted analytics.
• Array queries.
• Flexible indexing.

These map naturally to common “property search” tasks, especially when the results are driven by dynamic user intent or AI-generated constraints.

#3. Built-in analytics engine (aggregation framework)

The MongoDB aggregation pipeline lets us generate insights like:

• Average price per property type.
• Distribution of bedroom counts.
• Top trending amenities.

All inside the database—no external analytics layer required.

#What is Laravel Boost?

Laravel Boost is a new Laravel feature designed to bring natural-language development into your workflow.

It consists of three parts:

#1. The Boost assistant (IDE integration)

This is a sidebar panel available in popular editors like VS Code, PHPStorm, Sublime Text, and Cursor.

It provides:

• Natural-language queries.
• Code generation.
• Project understanding.
• Execution of PHP code in an isolated environment.
• Explanations and refactoring assistance.

This is the main interface developers use to “talk to” their project.

#2. The Boost engine (inside your Laravel project)

When you enable Boost in a project, Laravel includes:

• Metadata files.
• Structural hints.
• Context definitions.
• Mechanisms for safe code execution.

Boost uses these to understand your project’s models, methods, and structure.

#3. AI-discoverable methods

Boost is capable of discovering your model methods through:

• Naming conventions.
• PHPDoc comments.
• Examples.
• Parameter types.

This makes your domain logic feel “alive”—the AI naturally reaches for the helpers you define.

#Installing Laravel Boost

To use Boost in your project, you enable it during project creation.

Laravel provides a flag:

laravel new airbnb-explorer --boost

This:

• Scaffolds Boost metadata files.
• Configures your project for AI assistance.
• Allows your IDE Boost extension to understand the project immediately.

If you’ve already created a project, you can enable Boost manually:

php artisan boost:install

But the recommended method—and the only one covered in this tutorial—is using the --boost flag during project creation.

#Spinning up the world (setting up Laravel + MongoDB)

You start by creating a new Laravel project with Boost enabled:

laravel new airbnb-explorer

The --boost flag installs the necessary metadata and configuration files that allow Laravel Boost to understand your project structure and execute natural-language instructions.

On the MongoDB side, you spin up a free M0 cluster in Atlas and load the sample_airbnb dataset, which includes more than 5,000 real-world listings.

This dataset becomes the “world” your AI assistant will later explore.

#Connecting the dots: Laravel ↔ MongoDB

Install the PHP MongoDB extension and the Laravel MongoDB package:

pecl install mongodb
composer require mongodb/laravel-mongodb

Add your connection to .env:

MONGODB_URI="mongodb+srv://<user>:<pass>@cluster.mongodb.net"
MONGODB_DATABASE="sample_airbnb"
DB_CONNECTION=mongodb

Then, test the connection:

DB::connection('mongodb')->getCollection('listingsAndReviews')->countDocuments();

If you see something like 5555, your world is officially connected.

#Giving shape to the data (creating the model)

Now, Laravel needs a way to “think” about your MongoDB collection.

Create the model:

php artisan make:model ListingAndReviews

Then, configure it:

use MongoDB\Laravel\Eloquent\Model;
 
class ListingAndReviews extends Model
{
    protected $connection = 'mongodb';
    protected $collection = 'listingsAndReviews';
 
    protected $fillable = [
        'name', 'summary', 'property_type', 'bedrooms', 'beds',
        'bathrooms', 'price', 'address', 'amenities'
    ];
 
    protected $casts = [
        'bedrooms' => 'integer',
        'bathrooms' => 'decimal:1',
        'price' => 'decimal:2',
    ];
}

Try it in Tinker:

ListingAndReviews::first()->name;

If you see something like "Ribeira Charming Duplex," your model works.

#Talking to your data for the first time (using Boost)

Here comes the magic moment.

You open your editor with Boost enabled and say:

“Using the ListingAndReviews model, show me 5 listings in Barcelona.”

Boost:

1. Reads your project structure.
2. Detects your model.
3. Generates the correct code.
4. Executes it.
5. Returns real data.

For example:

ListingAndReviews::where('address.market', 'Barcelona')
    ->limit(5)
    ->get();

You’ve just queried MongoDB using natural language.

But after a few similar queries, you notice patterns emerging…

#Extracting patterns into AI-friendly helper methods

Most queries follow the same shape:

• Pick a city.
• Apply filters (price, bedrooms, property type).
• Limit results.

Rather than repeating yourself—and to help Boost understand your domain—you extract the logic into a method:

/**
 * Get listings from a specific city with optional filters.
 *
 * @param string $city
 * @param array  $filters Optional:
 *   - propertyType
 *   - minBedrooms
 *   - maxPrice
 *   - limit
 *
 * @example ListingAndReviews::byCity("Porto")
 * @example ListingAndReviews::byCity("Barcelona", ["limit" => 5])
 */
public static function byCity(string $city, array $filters = [])
{
    $query = static::where('address.market', $city);
 
    if (isset($filters['propertyType']))
        $query->where('property_type', $filters['propertyType']);
 
    if (isset($filters['minBedrooms']))
        $query->where('bedrooms', '>=', $filters['minBedrooms']);
 
    if (isset($filters['maxPrice']))
        $query->where('price', '<=', $filters['maxPrice']);
 
    return $query
        ->limit($filters['limit'] ?? 10)
        ->get(['_id', 'name', 'price', 'bedrooms', 'property_type', 'address']);
}

Now, type:

“Show me apartments in Porto under 80 dollars.”

Boost uses your method automatically:

ListingAndReviews::byCity("Porto", [
    "propertyType" => "Apartment",
    "maxPrice" => 80,
]);

You’re no longer just querying.

You’re teaching the AI your domain language.

#Turning queries into insights (analytics with aggregation)

Once you explore listings, the next natural question is:

“What insights can I extract?”

MongoDB shines with analytics through its aggregation framework.

Create your first analytics helper:

/**
 * Get price stats grouped by property type.
 *
 * @example ListingAndReviews::priceStatsByType()
 * @example ListingAndReviews::priceStatsByType("Porto")
 */
public static function priceStatsByType(?string $city = null)
{
    $pipeline = [];
 
    if ($city) {
        $pipeline[] = ['$match' => ['address.market' => $city]];
    }
 
    $pipeline[] = [
        '$group' => [
            '_id' => '$property_type',
            'averagePrice' => ['$avg' => '$price'],
            'count' => ['$sum' => 1],
            'minPrice' => ['$min' => '$price'],
            'maxPrice' => ['$max' => '$price'],
        ]
    ];
 
    return DB::connection('mongodb')
        ->collection('listingsAndReviews')
        ->aggregate($pipeline)
        ->toArray();
}

Now, Boost can answer questions like:

“What’s the average price by property type in Barcelona?”

And it will choose your helper first.

#Teaching the AI what your domain means

Boost learns from your:

• Method names.
• PHPDoc comments.
• Parameter structures.
• Examples.

When you create methods like…

public static function affordable(string $city, float $maxPrice = 75)

…Boost begins to understand:

• “Affordable” = price below a threshold.
• This method is the preferred path for budget queries.

So, prompts like…

“Find budget-friendly places in Porto.”

…will map correctly to your helper.

Boost isn’t just guessing—it’s learning.

#Supercharging performance with indexes

As your queries grow, so does the need for performance.

Create indexes:

$collection->createIndex(['address.market' => 1]);
$collection->createIndex(['price' => 1]);
$collection->createIndex(['address.market' => 1, 'price' => 1]);

Re-run:

"Show me affordable apartments in Porto"

Now, it executes dramatically faster, often by an order of magnitude.

AI + optimized data structures = modern development.

#Giving your work a safety net (tests)

Your methods now form a small internal API.

Tests lock in expected behavior:

public function test_by_city_filters_correctly()
{
    $listings = ListingAndReviews::byCity("Barcelona", [
        "propertyType" => "Apartment",
        "maxPrice" => 100,
    ]);
 
    $this->assertNotEmpty($listings);
 
    $this->assertTrue($listings->every(fn($l) =>
        $l->property_type === "Apartment" &&
        $l->price <= 100
    ));
}

Tests help Boost too. They:

• Clarify semantics.
• Reduce ambiguity.
• Reinforce your domain rules.

You’ve created correctness signals for both humans and AI.

#A developer workflow powered by AI

By now, you have:

• Domain-specific helper methods.
• Analytics powered by aggregations.
• Natural-language development through Boost.
• Fast queries via indexes.
• Safety via tests.

Your workflow looks like:

Developer Intent → AI Understanding → Helper Method → Query → Insight

You’re not writing database queries.

You’re expressing goals—and Boost translates them into code.

This is software development driven by intention, not syntax.

#Common pitfalls and how to avoid them

As you build your Laravel + MongoDB + Boost workflow, there are a few common issues developers tend to run into. Here are the most frequent ones—and how to fix them quickly.

#1. “Connection timeout” or “Could not connect to MongoDB Atlas”

This usually happens because the cluster is blocking the request.

#✔ Fix: Add your IP to the Atlas network access list

In MongoDB Atlas:

Security → Network Access → Add IP Address

For development, most developers use:

0.0.0.0/0 (allow all)

But you can also add your specific IP if preferred.

If you’re using a VPN or corporate Wi-Fi, your IP may change—update the whitelist accordingly.

#2. Forgetting to install the MongoDB PHP extension

Laravel will fail to connect to MongoDB without the PHP extension installed.

Common symptoms:

• “Class MongoDB\Client not found”
• “Call to undefined function MongoDB\Driver\Manager()”
• pecl is not installed

#✔ Fix: Install the extension

macOS:pecl install mongodb
 
Ubuntu:sudo apt install php-mongodb
 
Windows:Download the extension matching your PHP version and add to php.ini:extension=mongodb

Remember to restart your web server or PHP-FPM.

#3. Not using the correct Laravel MongoDB driver

Laravel does not include MongoDB support out of the box.

If you forget to install the package…

composer require mongodb/laravel-mongodb

…you will see errors like:

• “Database type mongodb not supported.”
• “Call to undefined method connection() on driver ‘mongodb.’”

#4. Missing or misconfigured ENV variables

Common mistakes:

• Wrong connection string
• Missing username/password
• Using the wrong database name
• Special characters in the password not URL-encoded

#✔ Fix: Verify

#.env

Example working configuration:

MONGODB_URI="mongodb+srv://username:password@cluster123.mongodb.net"
MONGODB_DATABASE="sample_airbnb"
DB_CONNECTION=mongodb

#5. AI not discovering your helper methods

Symptoms:

• Boost keeps generating raw queries instead of calling your helper.
• Boost ignores your domain methods entirely.
• Boost misinterprets parameters.

#✔ Fix: Add precise PHPDoc + examples

Boost relies heavily on:

• Clear parameter names.
• Clean method signatures.
• PHPDoc explanations.
• Usage examples.
• Consistent naming.

Example:

/**
 * Get affordable listings in a city.
 *
 * @example ListingAndReviews::affordable("Porto", 75)
 */

When documented well, Boost will reliably choose your helper first.

#6. Tinker execution errors when using Boost

Sometimes, Boost executes code that fails in Tinker because:

• The model wasn’t imported.
• The MongoDB driver didn’t load.
• The query references a missing field.

#✔ Fix: Preface your commands with model imports

use App\Models\ListingAndReviews;

And always verify the actual field names from your dataset.

#Conclusion: Building smarter with Laravel + MongoDB + AI

Through this journey, you built more than a tutorial. You:

• Connected Laravel to MongoDB Atlas.
• Explored data through natural language.
• Created AI-discoverable helper methods.
• Built insights with aggregations.
• Optimized performance with indexes.
• Provided stability with tests.
• Taught the AI how your domain works.

Laravel brings elegance.

MongoDB brings flexibility.

Boost brings intelligence.

Together, they enable a new kind of workflow.