How to bootstrap an AI startup

When you take venture capital money, investors will shape everything from your strategy and product to your thought process. That may not be best for what you’re offering, especially in the AI space, which is why I recommend bootstrapping your AI startup: You don’t have any other hands in the cookie jar.

Bootstrapping can serve as a competitive advantage in these times when capital is difficult to come by. Here are three aspects you should focus your attention on so you can build your startup without being beholden to anyone.

Build to solve a specific problem

Bootstrapping requires that you involve your clients when building your product roadmap. This is a great way to understand customers’ businesses, problems and blindspots, but it also serves a crucial purpose: It lets you target a specific issue.

Once you know the problem you need to solve, find out what your customers’ data capabilities are and whether they have the data to solve that issue. Then build in a user feedback loop so that you can test, train your AI to get smarter, and provide the desired output.

Here, an agile methodology will let you examine the quality of the output and understand what you need to tweak. You’ll also accelerate the feedback loop, which will in turn help the algorithm learn and improve faster.

An organization must be developed and mature from a data perspective to be able to handle an AI platform. So understand your client’s data formatting before you start thinking about how to receive it. Is the data coming from one or multiple sources? Are there redundancies?

Determine the quality of their data and data sources. If your client has clean data, you can build APIs to accept that data and leverage it by formatting it so your AI can use it.

Ask yourself if you’re building the technology for a real-world application that companies will need, and if you’re putting every dollar toward providing value for the product, the customer, and the team.