Results of analyzing 100s of AI Products, what makes them profitable
Over the past three months, as a creator of TreeScale.com, a low-code/no-code tool for making robust APIs from AI Text Prompts with powerful data integration, I've seen 100s of AI apps built within a few days with just a few prompts for Large Language Models like OpenAI GPT4.
Last week, I started to look at profitable products to understand the pattern that makes successful AI Micro-SaaS products profitable, especially if there is a shallow barrier to entry. You can hook up TreeScale.com with Webflow or Wix and have your own AI Product within a day or less without writing a single line of code.
The AI Product Idea
It turned out it was not about the product idea as much as the target market. Especially if there is already an established product on the market, the new way of doing it with just a simple LLM Prompt makes it more efficient and cheaper to operate, making the competition stand out.
The critical part is that building a Language translation Application classically involves having huge dictionaries and an NLP infrastructure to handle the word mapping. But with this new approach, building a translation App with your API is just writing a single Prompt for a TreeScale and forgetting about infrastructure costs or maintenance.
Here are examples of niche product ideas
- Job Application cover letter maker from uploaded CV PDF file
- Linkedin Post generator using existing user’s data
- Shopify product recommendation
- SEO Article generator from the given Trending Keyword dataset
Faster Time to Market
Traditionally, MVP creation took a lot of engineering talent and time for AI-based products. Nowadays, It is usually required to code to connect the dataset properly and write your prompt for your data types. This means you can make product release iterations quickly, within just a few days, and make a target market experiment almost every week.
That kind of rapid product release feature iteration gives an unfair advantage for Micro SaaS products against the companies that are already on the market but built in an old way.
The fact that you can have DuoLingo’s backend engine replaced by just 2-3 API Prompt endpoints from TreeScale.com makes that massive company vulnerable to this kind of small niche product experimentations by a solopreneur for a small team.
How to find your next product Idea?
After investigating our users and the rest of the market, it becomes clear that the fastest way is to look at the target market first and then find a competitor to ensure that there is already a business opportunity. It is even better if the competitor company/product has been selling for a long time in that market. It means that having quick product iterations in a better way might be cheaper because, in most cases, older IT companies have legacy codebases that cost more money to turn around and support.
The tricky part is choosing a target market so that you have at least an interest in solving a problem there. Otherwise, advertising might become a hustle, and you will burn out pretty quickly. I’ve been in that situation, and even though the product was cash-positive, I had no interest in maintaining it.
Conclusion
Building a software product becomes easier and more accessible. It is more like staying ahead of the trends and ensuring the product evolves. Otherwise, someone will create the same thing and target your user group within a week.
Establishing a brand is essential, of course, but let's be honest, branded Software Products went a long journey staying up to date with the trends and innovating constantly.
