Bootstrapping an AI Startup: A Step-by-Step Guide for Founders

When you take venture capital money, investors will shape everything from your strategy and product to your thought process. This 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.

Building 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 whether it is sufficient for your needs. If not, find ways to improve its quality. This may involve working with clients to collect more accurate data or finding alternative sources.

The Importance of Security

Security should be a top priority when building an AI startup. Getting these basics right is how you get to work with clients who write the big checks. You have to build your AI for a large company — not with heavy layers, but by knowing that security is going to be the paramount point of consideration.

Eliminating any vulnerabilities by doing smoke tests, regression tests, unit tests, system tests, integration tests, and acceptance tests. This will ensure that your AI platform is secure and reliable.

The Business Problem Should Come First

At the end of the day, a startup’s purpose is not to build a product or find customers. It is to understand, find and solve a specific problem, and sell the solution to customers grappling with that problem.

Solving such problems and building relationships is how you bootstrap an AI business and grow it organically. This isn’t going to help you get rich quickly. Founders who are considering bootstrapping must be prepared for the long haul and put every dollar back into building the product, focusing on people and innovating.

Conclusion

Bootstrapping your AI startup can be a challenging but rewarding experience. By focusing on solving specific problems, building relationships with clients, and prioritizing security, you can create a successful business that grows organically.

Don’t be tempted by venture capital money that may compromise your vision and values. With persistence and dedication, you can build a sustainable business that makes a real difference in the world.

Michael Koch is the co-founder and CEO of HubKonnect, an AI-enabled local store marketing platform. He has extensive experience in building and leading teams, with a focus on innovation and customer satisfaction.