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What are the cost and performance advantages or disadvantages of using a no-code tool like Levity versus developing an in-house solution?
Co-founder & CTO at Levity
There are cases for both.
First, many companies are actually not in a privileged position to make that decision because they don't have the resources at all. They might have developers, but they are usually already overworked and don't have machine learning expertise. Even if they had machine learning expertise, the in-house ML teams don’t work with us because they're busy with two or three high leverage things like for eCommerce it would be pricing optimization. They don’t work much on internal process optimization to make some teams 10 to 20% more efficient. Since their time is limited, there's just a long tail of things that have not been automated yet and would stay that way indefinitely.
Some customers told us they’ve tried doing things internally but oftentimes it’s a very slow back and forth between the person that has the problem and the person that can solve the problem. Then, you may have something deployed and running, and if you want to make a change, you have to reach out to the machine learning person again, and then, they have to retrain. It becomes very cumbersome to maintain. It's like Webflow where you're not reaching out to the developer anymore to make changes to your website, but you're just doing it yourself and then, hit “publish.” It's the same with us, you just hit “train”.
We're placing the tools to solve the problem in the hands of the same person that has the problem. There are cases where this is the best solution, and there are cases where you need to tweak for those last few percentage points of performance because it has such a big impact on your bottom line. Then it does make sense to have your own people to dive deep into it but if it's just a very basic thing where you need to classify some texts and you have the data at hand, then we’re often the better option.