boost

Based on your Laravel project focused on an e-commerce platform with a modular structure, here's how to implement a dynamic product recommendation system using Boost's AI integration:

1. **Data Preparation**: First, ensure your product data is well-structured in your database. Boost can help analyze your schema and suggest optimizations. For example, it might recommend adding a 'product_relationships' table to track which products are frequently viewed together.

2. **AI Model Integration**: Use Boost's AI capabilities to create a simple recommendation model. You can start with a basic collaborative filtering approach. Boost can generate the initial Laravel controller and service classes for this. Here's a sample controller method it might create:
```php
public function getRecommendations(Product $product, $limit = 5)
{
    $relatedProducts = Product::whereHas('viewedWith', function($query) use ($product) {
        $query->where('product_id', $product->id);
    })->limit($limit)->get();

    return $relatedProducts;
}
```

3. **Testing**: Boost can help generate test cases for your new recommendation system. It might create tests that verify the system works with different product categories and viewing patterns.

4. **Deployment**: Once tested, Boost can assist in creating deployment scripts specific to your Laravel environment, whether it's a shared hosting setup or a cloud platform.