Build an AI Image Search Engine in Under 5 Minutes

Imagine building a cutting-edge visual search app in less time than it takes to brew your morning coffee. Sounds impossible, right? Not if you're using Graft. 

In today's fast-paced digital landscape, the race is on to leverage AI and machine learning for superior customer experiences. But let's face it—the technical complexities can be a real roadblock. 

That's where Graft comes in, a Modern AI platform that simplifies the entire process, allowing you to go from concept to a fully functional visual search app in under 5 minutes. Intrigued? 

Let's dive into how you can break free from technical constraints and ML expertise to unlock endless possibilities with Graft.

The Old Way: A Maze of Complexities

Ever tried navigating a maze without a map? That's what building a visual search app feels like using traditional methods. You're faced with a labyrinth of technical puzzling and glue code that can easily derail your project.

“Slapping together a foundation model and a vector database and calling it semantic search is like putting wheels on a cardboard box and calling it a car. It does almost none of the things a modern AI production system should be expected to do—from second-stage reranking to managing dependencies when embeddings change—and so isn’t even state-of-the-art for semantic search. I wonder if the folks dropping 6-7 figures on that AI stack are aware of this.” 
~ Adam Oliner, CEO & Co-founder of Graft

Typically, you'd need to:

1. Create a Data Model: Design a model to manage catalogs of raw, unstructured data.
2. Provision Compute Infrastructure: Set up a distributed, scalable, and heterogeneous computing infrastructure for image embeddings. This should support both large-scale batch processing and ad hoc API requests.
3. Configure a Vector Database: Establish a database to store vectors and create the appropriate indexes for efficient search.
4. Secure Inter-Service Communications: Build a secure and reliable system for inter-service communications to facilitate data flow between different parts of the application.
5. Implement End-to-End Application Logging and Monitoring: Set up logging and monitoring systems, including considerations for machine learning-specific metrics like embedding drift.
6. Build End-to-End Application Error Handling: Implement a robust error-handling mechanism to catch and manage exceptions throughout the application.
7. Manage ML Artifacts: Create a system for managing machine learning artifacts, such as trained models.
8. Set Up a Web Server: Establish a web server capable of handling API requests, complete with load balancing and security features.
9. Provision GPUs with a Cloud Vendor: If your compute infrastructure requires graphical processing units (GPUs), you'll need to provision these with a cloud service provider.

And believe it or not, that's just scratching the surface. Companies quickly find themselves stuck in this maze for months, racking up costs and delaying time-to-market.

A recent survey showed that data management is the top technical inhibitor to AI/ML (32%), outweighing security challenges (26%) and compute performance (20%). Source: 2023 Global Trends in AI Report

Looks daunting, doesn't it? But what if there was a way to bypass this maze altogether?

The Graft Way: AI-Power + Simplicity

What if you could bypass the maze infrastructure complexities with a single AI platform? That's the magic of Graft. Our CEO and founder of Graft, Adam Oliner recently demonstrated how to build a production-ready image search engine using advanced foundation models within a few minutes.

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Here's your roadmap to turn the daunting into the doable.

Step 1: Link Your Product Catalog (data source)

What You Need: Just your product catalog. That's it!

Pro Tip: Make sure your catalog is well-organized with clear metadata and high-quality images for best results.

Step 2: Choose the Search App

What Happens: Once your catalog is linked, Graft's platform presents you with the search app option.

Note: You'll see your product catalog displayed, complete with metadata and images. This is your chance to review and make sure everything looks good.

Step 3: Preview and Confirm

What to Do: Preview the catalog to ensure it has the information you expect. Confirm your selection.

Best Practice: Double-check the metadata and image quality to ensure optimal search results.

Step 4: Interact and Integrate

What's Next: Your visual search app is now fully functional and production-ready. You can interact with it directly through the UI or integrate it with your front-end website using APIs.

Flexibility: You have the option to enable or disable these APIs as your business needs change.

The Bottom Line: Speed, Savings, and Scalability with Graft

In a world where time is often equated with money, Graft offers you the luxury of both. By streamlining the complexities of building a visual search app, Graft frees you to focus on what truly matters—your business.

Businesses using Graft's platform can achieve 50% lower total cost of ownership and a 100% faster time-to-value.

The Benefits Are Clear:

1. Speed: Go from zero to a production-ready app in minutes, not months.
2. Efficiency: Eliminate the need for specialized skills or a dedicated team to manage the technicalities.
3. Flexibility: Adapt and scale your app effortlessly as your business grows.
4. Reliability: With Graft's robust architecture, you can trust that your app is performant, secure, and reliable.

So, what will you do with all the time and resources you save? As Adam suggests, perhaps you could take a leisurely walk and enjoy the beautiful weather. Or better yet, reinvest that time into scaling your business to new heights.

Explore Graft today and unlock the possibilities that Modern AI has to offer.