It was 2am on a Wednesday. I'd been trying to name my app for three days straight.
The app itself was nearly finished - an AI-powered revision tutor designed to help parents support their children's learning. The code worked. The interface was polished. But I couldn't launch without a name, and every domain I checked came back the same way: taken, taken, premium ($4,500), taken.
The Naming Time Sink
I started the way most founders do - a blank document and some enthusiasm. First, the obvious names. LearnAI. Taken. TutorBot. Taken. StudyGenie. Taken. Okay, time to get creative.
Two hours later, my list had grown to 47 names, each requiring a manual check across multiple domain registrars. The pattern was always the same: type the name, wait for the search, see the red "unavailable" message, try adding a prefix, try a different TLD, eventually give up and move to the next one.
According to First Round Capital's research on startup naming, most founders should expect the naming process to take at least a month. But I didn't have a month. I had a working product that was gathering dust because I couldn't figure out what to call it.
The Manual Research Spiral
By day three, my process had evolved into something that looked organized but felt increasingly desperate.
I had a spreadsheet tracking every name I'd considered. Columns for the domain, TLD options, availability status, and my subjective rating of how "good" the name felt. Rows and rows of red cells marking unavailable domains, interrupted occasionally by a green cell that would get my hopes up - until I realized the available name was terrible.
The process went like this:
- Brainstorm 10-15 names
- Open multiple tabs - GoDaddy, Namecheap, Porkbun, Google Domains
- Check each name across .com, .ai, .io, .app
- Record results in spreadsheet
- Google each available name to check for trademark conflicts
- Search for similar businesses that might cause confusion
- Repeat until exhausted
What I didn't realize was how much time I was losing to context-switching. Every name check required opening new tabs, waiting for searches, copying results back to my spreadsheet. The SBA recommends checking trademark databases, state registrations, and domain availability - but doing this manually for dozens of candidates was mind-numbing.
The worst part? After three days, I still couldn't confidently say which of my "available" names was actually good. I had data but no real analysis. Nothing told me whether KnowledgeNest.io was actually better than SmartStudy.app beyond my gut feeling at 2am.
The Breaking Point
The moment that changed everything came when I found a name I loved - let's call it BrightMind - only to discover after an hour of research that there were already three companies using variations of the name, including one in the same education space. All that time, wasted.
That's when the irony hit me: I was building an AI tool that could help students learn more efficiently, but I was stuck in the most inefficient process imaginable. I was doing manual research that a computer could do in seconds.
As a computer engineer turned educator, I'd spent years teaching kids how to use technology to solve problems. I ran Tech Camp UK, showing children how to build 3D printers and create games - all about using tools to amplify human capability. Yet here I was, manually checking domains like it was 1999.
The question wasn't whether I could build something better. The question was why I hadn't already.
From Prototype to Product
The first version was ugly and fast. A basic prototype that could:
- Generate creative name ideas based on a description
- Check availability across multiple TLDs automatically
- Display everything in one place
I built it in a few days, using the same tools I use for my other projects - Cursor IDE, Next.js, and Convex for the backend. The prototype was rough, but it worked. Instead of spending an hour checking 10 names, I could generate and check 50 names in minutes.
That's when I made a decision that would change the direction of the project: I used my own tool to name itself.
The tool suggested URLGenie. It scored high on memorability, passed the verbal clarity test (no spelling confusion), and most importantly - the .ai domain was available. I registered it the same day.
But the prototype wasn't the product. Over the following weeks, I iterated on what actually mattered:
Scoring that meant something. A name being "available" wasn't enough. I needed to know why one name was better than another. This led to developing five specific metrics - Brand Fit, Verbal Clarity, SEO Potential, Resale Value, and Authority - that could objectively compare any two names.
Risk analysis that caught problems early. After my BrightMind disaster, I added background checks that scan for similar businesses, potential trademark conflicts, and negative brand associations. Not legal advice, but early warning signals.
Real availability data. Not just "is the .com taken" but actual pricing across TLDs, showing exactly what each option would cost to register.
What I Learned Along the Way
Building URLGenie taught me things I couldn't have learned from just reading about naming.
Creative brainstorming and availability checking are different problems. Most tools try to solve one or the other. Domain registrars check availability but don't help with creativity. Name generators are creative but don't verify anything. The real bottleneck is doing both simultaneously.
Objectivity requires structure. My 2am gut feelings about names were worthless. But when I could see that NameA scored 92/100 while NameB scored 78/100, with specific reasons for each score, the decision became obvious. Data doesn't replace judgment, but it makes judgment reliable.
Speed changes behavior. When checking a name takes 5 minutes of manual work, you become conservative and only check names you're already attached to. When checking 50 names takes 5 minutes total, you explore more freely and often find options you never would have considered.
The complete process is documented in how we named URLGenie, including the actual scores and runner-ups from our naming session.
The Difference Now
That AI revision tutor I was trying to name? It eventually got named - using URLGenie, of course. What took three frustrating days of manual research now happens in a single session.
More importantly, the experience changed how I think about the naming process. It's not about finding the perfect name through exhaustive manual research. It's about systematically generating and evaluating options until a clear winner emerges.
The myth that all good domains are taken persists because manual research is so painful that founders give up before finding the good ones. When you can check 200 names in the time it used to take to check 10, the landscape looks different.
Try It Yourself
I built URLGenie because I needed it. The frustration of those three days - the spreadsheets, the tab-switching, the 2am desperation - drove every feature decision. If you're stuck in that same loop, you don't have to stay there.
The naming process doesn't have to feel like gambling. It can feel like analysis. And analysis is something a good tool can accelerate dramatically.
Your next product name is probably 5 minutes away. Give it a try.