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.

The sooner you provide a solution that your customers will pay full price for, the sooner you will have a business.

Use your status as a bootstrapped startup to attract talent

Attracting and retaining talent is a skill in and of itself. There is a lot of competition for the best developers in the world, so you must be able to articulate your vision and get them to believe in it, because a bootstrapped startup will never have the resources of Google, Meta or OpenAI.

While you’ll never be able to pay as much as Microsoft, as a bootstrapped startup, you have other strengths. Offer candidates the opportunity to make an impact, have influence and have a say in the roadmap. Let them innovate and stay at the forefront by using the latest and greatest tech to drive results.

Big Tech may have you beat on the compensation and brand fronts, but you, as a startup, can offer a much different and wide-ranging package to developers: A fusion of equity, technology, accessibility and freedom that can provide value to the kind of people who will thrive in a bootstrapped startup.

Once you find engineers with a strong, core technical skill set, push them to get involved with all of the tech and help them evolve into a full-stack developer. If someone originally started out as a back-end developer, help them build the knowledge to also understand the front end and how it interacts with your UI/UX team.

It’s not about proximity; it’s about talent. It’s naive to think that the best talent lives within a one-hour radius of your office. Distributed teams are the way in 2023. The best developer for your company could be living in Norway, and the back-end developer could be in Japan. Find that talent and unite them.

Have a strong lead in every group and mold these groups together into one team. Many people make the mistake of developing their product in siloes, and mistakes are made and blindspots are created when people develop in vacuums.

Think about security from the first line of code

You’re going to be handling people’s data, so security is of the utmost importance. Security starts with making sure your platform’s architecture maintains the privacy of each client’s data within your platform.

You can’t just base your entire platform off one database, as that will lead to data commingling. Your architecture must have divisions, permissions, structures and firewalls in place. In fact, the developer timeline needs to be built around these security and data security layers.

Your customers may also run their own security audits as part of the approval process. You need to pass their standards, service-level agreements, and protocols by focusing on security from the get-go.

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.

Start with the basics — the business problem

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.