Building Referral Search Engine: My Journey from TeslaReferralHub to ReferralSearchEngine
Building Referral Search Engine: My Journey from TeslaReferralHub to ReferralSearchEngine
I recently launched a SaaS side project called Referral Search Engine, and this post will walk you through everything about it—why I built it, the tools I used, the decisions I made, and the new technologies I tried. If you're interested in building SaaS products using AI tools, this might be the perfect read for you.
Some of you might remember one of my earlier projects, Tesla Referral Hub. Tesla Referral Hub allowed users to share their Tesla referral links in a fair, rotating system. Each time someone viewed a link, it would refresh and display a different user's link. I built this system because I lacked the social media influence to promote my referral links, and I realized others like me could benefit from a more equitable way to share referral opportunities.
Expanding from a Niche Product to Something Bigger
Like many successful products, I started by focusing on a specific niche. Tesla Referral Hub allowed me to experiment with the referral link model, and while the results were modest—two referrals earning me about $2,000 worth of Tesla credits—it sparked the idea of a more general solution. That’s when I decided to build Referral Search Engine.
What is Referral Search Engine and What Problem Does It Solve?
Referral Search Engine helps users find referral links for products they want to buy without needing a friend who has purchased the product. For example, if you’re interested in buying a Peloton but don’t know anyone who owns one, you can use Referral Search Engine to find a referral link, benefiting both you and the person who shared the link.
The concept is simple: users who have referral codes share them on the platform, and others can search for and use them. It’s a win-win for everyone—the user sharing the referral benefits, the buyer benefits from the discount, and the company gets more customers. I haven’t come across many similar products, probably because it isn’t easy to monetize. But I wanted to build the solution and see where it goes.
How Referral Search Engine Works
Using Referral Search Engine is straightforward. Users can search for a product, and the platform will show available referral links. For example, you can search for a Tesla referral link, copy it, and apply it directly on Tesla’s site to get benefits like $500 off your purchase.
Adding referral links is easy as well. After logging in with Google or email, users can submit a URL, product name, company name, and description. The referral link goes through an admin approval process to ensure its legitimacy before it appears in search results.
Tools and Technologies I Used
Building Referral Search Engine gave me a chance to experiment with several exciting tools. Here's a breakdown of the tech stack I used:
1. Vercel
Vercel is my go-to tool for handling front-end deployment, particularly with Next.js. It simplifies the process by automatically deploying new changes each time you push a commit to Git. Vercel’s rollback feature ensures that if a deployment fails, the last successful version stays live. I use Vercel for all my side projects because it makes front-end development and deployment incredibly easy.
2. Supabase
Supabase offers an all-in-one backend solution with authentication and managed PostgreSQL databases. I use it to manage user authentication and handle database operations for all my projects. Their generous free tier and easy-to-use API make it a great choice for indie developers.
3. Cloudflare D1
I chose Cloudflare D1 for the main database. One key reason was that I wanted to try out Cloudflare’s stack, and their free tier was very attractive, especially for read-heavy applications like Referral Search Engine. With D1, the SQLite databases are placed on the edge, allowing faster data access for users.
4. Vercel V0
Vercel V0 is an AI-powered tool that helps generate user interfaces quickly. As someone more comfortable with backend development, Vercel V0 has been a game-changer for me in building polished front-end designs without spending too much time on it. It allows you to describe what you want, and it generates a UI based on those requirements.
Database Schema
The database schema for Referral Search Engine is designed to handle various key functions. There are tables for users, searches, referral links, and companies. For example:
Users table tracks user data, including tiers (free or paid), and how many referral links they are allowed.
Referral links table stores the link, user ID, and product ID, as well as metrics like views and clicks to ensure fair rotation of links.
Searches table tracks what users are searching for, which could potentially be useful for businesses interested in advertising on the platform.
Monetization and Marketing
The primary challenge with Referral Search Engine isn't the technical implementation—it’s the marketing. For a platform like this to succeed, it needs strong SEO and distribution efforts to drive traffic. Some monetization strategies I’m considering include partnering with companies to provide exclusive referral links or selling advertising spots to businesses looking to reach potential customers.
Conclusion
In the end, Referral Search Engine took about three or four days to develop. But the hard part lies ahead—gaining traction, marketing the platform, and exploring monetization opportunities. If you’re interested in SaaS projects, building with AI, or have questions about any part of this journey, feel free to ask. And if you enjoyed this post and want to learn more about building side projects, drop a comment!