Cracking the Code: How AI Transformed Fintech User Retention

Hey there, Indie Hackers!

Today, I'm excited to share our journey and how we successfully kept users engaged in the fintech industry using AI.

Ever since OpenAI introduced custom GPT features, our team has been full of creative ideas. And guess what? We figured it out! 🚀

It all started with an urgent email from Nancy, the CEO of a promising fintech startup.

The subject line said, "Our LLM recommendations seem confusing!"

But the real problem was serious – High LLM cost and inaccurate results were causing a drop in user retention.

The Challenge🔴:

Doing Everything Manually and Losing Money

Their manual LLM review process took a lot of time and money, leaving little time to focus on important features that keep users interested.

They couldn't see how well their model was doing, so they missed important information and couldn't fix user problems quickly.

The Need for a Solution👀:

We knew there had to be a better way. Wrong LLM results were hurting their user experience and money.

Enter LLUMO📍:

We introduced LLUMO, which looks at LLMs from every angle.

Our tools promised to make development easier and ensure that things were accurate and efficient by cutting LLM cost without compromising output quality.

Here's what we did that made a big difference:

Sometimes, you need bigger models for some tasks. This could be because it's easier to tell a bigger model what to do or because you can write a more general request and still get good results without giving examples. Making sure your LLM is the right size means picking the most cost-effective LLM based on what your chatbot needs to do.
We broke things down into smaller steps using different models, trying to be accurate but not too expensive. This saved about 20% in costs.
We made our models better at specific tasks and saved up to 40% by fine-tuning pre-trained models with our data!
Before using them in a Large Language Model (LLM), we made sure our prompts were short and clear. This cut costs by about 50%.
We saved up to 10% by only using relevant information from the user's chat history for each question! Managing memory well was a big win!

With these changes, we saw some great results:

Better results made users 20% happier, leading to a 20% increase in user retention.
Using LLUMO saved the development team time, cutting development time in half.
Our Annual Recurring Revenue (ARR) grew by 34%.
The costs of LLM optimization went down by 75%.

Conclusion🎯:

Nancy's startup is now a successful fintech company, all thanks to our AI solutions.

LLUMO helped them make their AI model better and give accurate, unbiased financial advice without compromising the output quality.

If Nancy's story sounds like yours, please like ❤️ and share 📎 this post with others who are also using AI to grow their businesses.

P.S. We're open to new GPT ideas. Send us your suggestions! 🙂