Why We Built an AI-Powered Recommendation App for Shopify

The Breaking Point

Let me tell you about the worst Thursday afternoon of my professional life.

I was helping a client—a bookstore owner in Jaipur—set up product recommendations on his Shopify store. He had around 200 books. Not a massive catalog, but decent.

He wanted three things that every ecommerce store needs:

• Upsells (higher-value alternatives)
• Cross-sells (complementary products)
• Related products (similar items customers might like)

Simple enough, right?

I opened the recommendation app he had installed. One of the popular ones with good reviews. And I started setting things up.

For the first book—a bestselling fiction title—I had to:

1. Search and select 3-4 books for the upsell section
2. Search and select 3-4 books for the cross-sell section
3. Search and select 4-5 books for the related products section

That took about 4 minutes per product. I am not slow. The interface just required that many clicks, searches, and selections.

4 minutes × 200 products = 800 minutes = 13+ hours of manual work.

I did not finish that day. I came back Friday. And Saturday. By the end, I had spent genuinely 6 hours on this task (I got faster as I went, but still).

Then the client messaged me on Monday: "Hey, I just added 10 new books. Can you set up recommendations for them?"

That is when something inside me broke.

The Problem Nobody Talks About

Here is what I realized: every single product recommendation app in the Shopify App Store works the same way.

You install the app. You go to Product A. You manually select which products should appear as upsells. You manually select cross-sells. You manually select related products. You save. You move to Product B. Repeat.

If you have 50 products, this is tedious but manageable.

If you have 200 products, this is a full weekend of work.

If you have 500+ products, you probably just give up and leave most products without recommendations.

And here is the part that really got me: when you add a new product, the work is not just setting up that new product's recommendations. You also need to go back to existing products and add the new product to their recommendations.

Think about it. You sell running shoes. You add a new running shoe to your catalog. That new shoe should probably appear as a related product on your other running shoe pages. But the app does not know that. You have to manually go to each existing running shoe product and add the new one.

Nobody does this. It is too much work. So recommendations get stale. New products never appear in recommendations. The feature that is supposed to increase revenue becomes a neglected corner of the store.

I asked the bookstore owner how he had been handling this before hiring us.

"I just stopped using recommendations," he said. "Too much work to maintain."

He was leaving money on the table—probably 10-15% additional revenue—because the tools available required too much manual effort.

I Started Looking for Alternatives

After that project, I spent a week researching every recommendation app on the Shopify App Store. I was convinced I must be missing something. Surely someone had solved this.

I tested 11 different apps. Here is what I found:

App 1: Manual selection for each product. No automation.

App 2: Manual selection. Has "bulk" feature, but bulk just means selecting multiple products to add the same recommendations—not automatic matching.

App 3: Manual selection. Pretty interface though.

App 4: Has "automatic" mode, but automatic just means showing random products from the same collection. Not intelligent matching.

App 5: Manual selection. Expensive.

App 6: Uses purchase history to suggest recommendations. Sounds smart, but useless for new stores or new products with no purchase data.

App 7-11: Various combinations of the above.

Not a single app actually analyzed product data to intelligently match recommendations.

I talked to store owners. Same story everywhere.

A skincare brand owner in Delhi: "I have 80 products. I set up recommendations when I launched, spent two days on it. I have added 30 products since then. None of them have recommendations because I cannot face doing it again."

A home decor store owner: "We just show 'random products from this collection' as related products. I know it is not good. A customer looking at a blue ceramic vase sees a wooden cutting board as a 'related product' because they are in the same collection. Makes no sense."

A fashion brand founder: "My team tried to maintain recommendations properly for the first three months. Then they gave up. Now only our top 20 products have real recommendations. The other 200 just show whatever."

This was a universal problem. And nobody was solving it properly.

The Realization

I kept thinking about the bookstore project.

When I was manually selecting recommendations for each book, I was not doing anything magical. I was looking at the book title, the genre, the author, the themes, the price point. Then I was thinking:

"Okay, this is a ₹599 literary fiction book by an Indian author. For upsell, I should show the hardcover edition at ₹899 or maybe the author's collected works at ₹1,299. For cross-sell, I should show a bookmark, maybe a reading light, perhaps a journal—things a fiction reader might want. For related products, I should show other literary fiction by Indian authors in a similar price range."

I was using my understanding of the product to make logical connections.

And then it hit me: this is exactly what AI is good at.

AI can read product titles, descriptions, categories, tags, and prices. It can understand what a product is. It can identify logical relationships between products.

If I can look at a product and figure out sensible recommendations, an AI model trained on the right data can do the same thing. Except it can do it for 500 products in 5 minutes instead of 5 days.

This was not a revolutionary insight. It was obvious in hindsight. But somehow nobody had built it.

Building the Solution

My brother and I started building in early 2024.

The core idea was simple:

1. Connect to a Shopify store
2. Pull all product data—titles, descriptions, categories, tags, prices, images, variants
3. Use AI to analyze each product and understand what it is
4. Use AI to identify logical upsell, cross-sell, and related product matches
5. Automatically generate recommendations for every product
6. When new products are added, automatically update recommendations

The implementation was not simple. But the concept was.

How the AI Matching Works

Let me explain what happens under the hood, without getting too technical.

When you connect your store, our app pulls your entire product catalog. For each product, we create what we call a "product understanding"—basically, the AI reads everything about the product and creates a detailed profile.

For example, for a product titled "Men's Navy Blue Slim Fit Formal Shirt - Cotton - Size M":

The AI understands:

• Category: Clothing → Men's → Shirts → Formal
• Attributes: Navy blue color, slim fit, cotton fabric
• Size: Medium (part of a size range)
• Use case: Formal/office wear
• Likely price tier: Mid-range

With this understanding, the AI can now make intelligent recommendations:

Upsells: Higher-tier shirts—maybe a premium cotton shirt, or a similar style with added features like wrinkle-resistance. Or a bundled set (shirt + tie + cufflinks).

Cross-sells: Complementary products—a matching tie, formal trousers in compatible colors, a leather belt, shoe polish if you sell accessories.

Related products: Similar shirts—other slim fit formal shirts, other navy blue shirts, other cotton shirts in different colors.

The AI does not just match keywords. It understands relationships. It knows that a navy shirt goes with a grey trouser. It knows that someone buying formal shirts might need cufflinks. It knows that a premium version of the same product is an upsell, not a cross-sell.

The New Product Sync

This was the feature I was most excited about.

In traditional apps, when you add a new product, you have to:

1. Set up recommendations for the new product
2. Go to existing products and add the new product where relevant

Our app handles both automatically.

When you add "Men's Light Blue Slim Fit Formal Shirt" to your catalog:

1. The app detects the new product (via Shopify webhooks)
2. AI analyzes it and creates a product understanding
3. AI generates recommendations for the new product (pulling from existing catalog)
4. AI identifies which existing products should now recommend the new product
5. All updates happen automatically

You add a product. Walk away. Come back, and recommendations are set up everywhere—both on the new product and on existing products where it is relevant.

For the bookstore owner, this means when he adds a new literary fiction book, it automatically appears as a related product on other literary fiction pages. No manual work.

For the skincare brand, when she launches a new serum, it automatically becomes a cross-sell on her cleanser pages and a related product on her other serum pages.

This is how recommendation systems should work. The tool should do the thinking, not the store owner.

Real Results from Real Stores

We have been running the app with beta users for several months now. Let me share some actual results.

The Bookstore (Where It All Started)

The same Jaipur bookstore that sparked this whole project.

Before:

• Recommendations on 30% of products (manually set up during our painful Thursday-Saturday session)
• Recommendations were static and getting stale
• New books had no recommendations

After:

• Recommendations on 100% of products
• Recommendations update automatically when new books are added
• Owner has added 45 new books—all have recommendations without any manual work

Revenue impact: 18% increase in average order value. Customers are discovering and adding more books.

Owner's feedback: "I forgot recommendations existed. That is the best compliment I can give. It just works in the background."

Fashion D2C Brand (Mumbai)

Women's ethnic wear brand with 340 products.

Before:

• Gave up on manual recommendations after initial launch
• Was using "random products from collection" which made no sense
• Customers saw lehengas recommended on kurta pages

After:

• Every product has contextually relevant recommendations
• Kurtas show other kurtas and complementary dupattas
• Lehengas show matching blouses and similar lehengas

Revenue impact: 23% increase in upsell revenue. Cross-sell conversion up 34%.

Founder's feedback: "We added a new wedding collection—47 products. I expected to spend a weekend on recommendations. Instead I checked the next day and everything was already set up. I actually messaged your team asking if something was wrong because I did not believe it worked automatically."

Home Decor Store (Bangalore)

Curated home decor with 180 products across furniture, lighting, and accessories.

Before:

• Manual recommendations for top 40 products only
• Rest showed nothing or generic "you may also like" with random products
• A customer viewing a minimalist desk lamp would see a baroque picture frame as "related"

After:

• Style-aware recommendations across entire catalog
• Minimalist products recommend other minimalist products
• Complementary recommendations make sense (desk lamp → desk organizer, not picture frame)

Revenue impact: 12% increase in pages per session, 21% increase in conversion rate on product pages with recommendations.

Owner's feedback: "The AI actually understands our aesthetic. It is not just matching categories. A wooden Scandinavian chair recommends other Scandinavian pieces, not just any chair. It gets the vibe."

What We Learned Building This

Building this app taught me a few things about Shopify apps and AI products:

Store Owners Are Drowning in Manual Tasks

Recommendations are just one example. Everywhere I look in Shopify, I see store owners doing repetitive manual work that software should handle.

Writing product descriptions for hundreds of products. Tagging products for collections. Setting up SEO meta descriptions. Responding to the same customer questions repeatedly.

There is massive opportunity to apply AI to reduce this burden. We started with recommendations because that was our pain point. But the same approach applies to dozens of store management tasks.

"Automatic" Is a Meaningless Word in App Marketing

Every app claims to be automatic. What they mean is "automatically displays whatever you manually configured."

True automation means the system does the thinking. It makes decisions. It handles edge cases. It adapts when things change.

When evaluating any Shopify app, ask: "What do I have to do manually, and what does the app actually do for me?" You will be surprised how often "automatic" means "we have a nice interface for manual work."

AI Quality Depends on Product Data Quality

Our app works best when stores have good product data—detailed titles, descriptions, proper categorization.

A product titled "Blue Shirt" gives the AI very little to work with. A product titled "Men's Navy Blue Slim Fit Formal Shirt - 100% Cotton - Wrinkle Resistant" gives the AI rich information for matching.

We added features to handle sparse data (AI can infer from images and categories), but better input always means better output. Stores investing in product data quality get significantly better recommendations.

Store Owners Want Results, Not Features

Nobody cares about our AI technology. They care about higher average order value and not spending weekends on manual work.

When we talk about the app, we do not lead with "AI-powered" or "machine learning." We lead with "set up recommendations for your entire catalog in 10 minutes" and "never manually configure a recommendation again."

The technology is the how. The result is the what. Customers buy the what.

Why We Are Sharing This

This is not a product launch announcement. We are not opening public signups yet. The app is in extended beta while we refine it.

We are sharing this for a few reasons:

First, to validate that this problem resonates. If you are reading this and thinking "yes, I have exactly this problem," let us know. We want to build for real pain points, not imagined ones.

Second, to explain our philosophy on building Shopify solutions. We do not build apps that create more work for store owners. We build apps that eliminate work. If you hire us for any Shopify project, this is the mindset we bring.

Third, to start a conversation about AI in ecommerce. We think AI should handle repetitive, pattern-matching tasks so humans can focus on creative and strategic work. Product recommendations is one application. There are many others.

The Vision

Here is where we want to take this:

Recommendations are just the beginning. The same AI that understands your products can:

• Write product descriptions that match your brand voice
• Generate SEO meta descriptions for your entire catalog
• Create email copy for product-specific campaigns
• Answer customer questions about products via chat
• Identify gaps in your catalog based on what customers search for

All of it automatic. All of it learning from your specific store and products.

The store owner's job should be curating products and building brand. Not data entry. Not configuration. Not repetitive tasks that software can handle.

We are building toward that future, one app at a time.

Conclusion

I started this project frustrated and exhausted after a 6-hour manual recommendation setup session.

I end it convinced that most ecommerce software asks too much of store owners. Manual configuration is not a feature. It is a failure of imagination.

If a human can look at a product and figure out logical recommendations, software should be able to do the same. That was the insight. The app is just the implementation.

For store owners struggling with product recommendations: you are not lazy. The tools are inadequate. The task genuinely takes too long. You are right to deprioritize it.

But do not give up on recommendations entirely. They work. Stores with good recommendations see 10-20% higher average order values. The problem is implementation, not the concept.

If you want to be notified when our AI recommendation app opens for public access, drop us a message. We will add you to the early access list.

And if you have other Shopify tasks that feel needlessly manual, tell us about them. We are always looking for the next problem worth solving.

Frequently Asked Questions

How is your AI recommendation app different from other Shopify apps?

Most recommendation apps require manual setup—you select upsells, cross-sells, and related products for each product individually. Our app uses AI to analyze your product catalog and automatically generate contextually relevant recommendations for every product. No manual selection required.

How long does it take to set up recommendations with your app?

Initial setup takes about 10 minutes regardless of catalog size. Connect your store, let the AI analyze your products, and recommendations are generated automatically. A 500-product store takes the same time as a 50-product store.

What happens when I add new products?

New products are automatically detected and processed. The AI generates recommendations for the new product AND updates existing products to include the new product where relevant. You do not have to do anything manually.

How accurate are the AI-generated recommendations?

Accuracy depends on your product data quality. Stores with detailed product titles, descriptions, and proper categorization see highly relevant recommendations. We also provide manual override capability if you want to adjust specific recommendations.

Is the app available now?

The app is currently in extended beta. We are refining it with select stores before public launch. Contact us to join the early access list.