How to start an AI company in 6 simple steps + Common mistakes founders make

Since ChatGPT was launched in November of 2022, we've seen plenty of founders starting their own AI companies but with no success. These were the two biggest mistakes we saw founders make:

1. Founders don’t niche down enough. They create AI products that compete directly with big players like ChatGPT and Google Bard. Their expensive language models fail because they don’t have the funding that OpenAI and Google have.

2. Founders spend hundreds of thousands of dollars building AI products from scratch instead of training pre-existing language models like ChatGPT 3.5. This causes them to run out of money before they can bring in product signups.

So, if you’re wondering how to start an AI company successfully and you don’t have a massive budget, avoid the two mistakes above. Niche down as much as possible and use pre-existing language models to train your AI. This keeps costs low and ensures you aren’t directly competing against the likes of OpenAI and Google.

In this article, we wanted to talk about more in-depth tips for avoiding these common pitfalls so you can start a successful AI company.

‍Note: It’s impossible to cover everything you need to know about starting an AI company inside a single article or even multiple articles. Entire websites and YouTube channels are dedicated to helping people start AI companies, so we aren’t trying to cover everything. Instead, we’re discussing a more high-level process you can use to avoid common mistakes.

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Step 1: Find gaps in your industry that can be filled by AI

The first step is to do some market research and trying to find an industry in which AI hasn't already automated huge chunks of workflow.

For example, if you learn that receptionists at your local doctor's offices are struggling to keep up with calls coming in, you could develop a bot that books appointments for customers by getting them to click certain buttons on their keypads. Or, if you find that all the companies you talk to struggle to attribute revenue to ads, you could build an AI that tracks ads better than the existing products on the market.

By finding gaps or “micro niches” that has little to no competition, you wouldn't have to compete with existing AI giants, allowing you to start an AI company with limited capital.

However, the problem with being the first to explore a niche is that you don't really know if there's demand for your product. You can't look at existing competitors and say there's demand because they are charging $50 per month and have 10,000 customers, for example.

Think through a solution and what it will take to build your MVP.

Step 2: Build an MVP and get your first few customers

Now that you’ve found a gap in an industry that AI can solve, it's time to build an MVP to get into market and get your first few customers.

As we said in previous articles, when you're building an MVP, you want to get a product on the market as soon as possible and as cheaply as possible. It doesn't matter if there are a few flaws and UX issues; you can always flush these out in the product development stage.

People haven't actually paid you for your AI product just yet, so you don't want to sink too much money into it. Get your large language model trained and your product running as quickly as possible and you've got a product ready to go-to-market wtihout a huge investment.

Compare this to failed AI startups who spend over $200,000 building an MVP from scratch only to find there isn't a market for their product.

To get your first few customers, we've found that companies who give their products away for free usually have the most success. This is because, in this initial stage, your goal isn't to make money. It's to get people using your product and actually tracking the results you're generating so you can use it to close paying customers in the future.

A good way to get these first couple of "free customers" is to send your MVP to the people you interviewed in step one and ask for their thoughts. If your product truly helps them, they'll continue using it.

Step 3: Optional: Secure funding

This step is optional, and if you're niched down enough, you won't even need venture capital because you won't be competing directly with the big players in the AI space.  With solutions like Dome, you can get your product launched with a low investment quickly.

A point to note is that the funding stage can come before or after the MVP stage. If you have an excellent idea and you have the team to convince investors that you can make this work, it's possible to raise money before you build an MVP.

That said, most investors will like to see a product and a good base of paying customers before investing in your AI company, which is why we put this after the MVP stage.

A benefit to launching your MVP and building your user base on your own is that you will retain control of your company and not lose any ownership to VCs.

Step 4: Ask users for feedback

Once you've built a usable MVP and convinced a couple of people to try your product for free, hop on a video call with them and ask them what your software does well, where you can improve, and what features they'd like you to introduce or remove.

In order to interview the largest possible pool of prospects, not just those you interviewed in the market research stage, consider cold emailing people who fit the criteria of your target audience. You can let them know that you just launched a new AI product and would like to give them access to it in exchange for constructive feedback.

This feedback will fuel your product development efforts up until the first launch of your product, which is why it's important to interview as many people as possible so you aren’t receiving biased feedback.

For example, if several customers say they’d like your product to have more third-party integrations, you can work to add those integrations and  focus on connecting to more applications when launching your full-scale AI product.

Step 5: Continue improving your product

With a list of improvements and features you need to add, you can have your development team build out your full-scale product and launch it on your website. However, product improvement doesn't stop after launch, so it's important to continuously book calls with customers and try to figure out where you can improve.

Step 6: Market your product

We can’t cover everything you need to know about product marketing in one article, let alone one subheading.

However, the best advice we can give you is to track the results your AI product generated for your "free customers," turn them into case studies, and promote these case studies on your website, YouTube channel, and social media pages. Social proof goes a long in way in validating your product to potential users.

Now you can show potential customers how you generated results for similar businesses. This works especially well if your software directly impacts how much money a business makes or saves. 

For example, if you're selling AI marketing attribution software, you can show prospects how you helped businesses reduce ad spend by 20 percent while maintaining the same results, for example. If they'd like you to do something similar for their business, they can sign up for a free trial.

Deploy your AI product with Dome within minutes

Starting a successful AI company is all about niching down to a very specific need that your target audience has and training pre-existing language models like ChatGPT 3.5 to do what you want. This ensures you’re targeting specific verticals and aren’t competing with the big players in the AI space. And since you aren’t building an AI from scratch, you keep costs down.

If you want to deploy your AI product quickly and without going through a tedious setup process, consider signing up for an account with Dome. Dome is a PaaS that deploys software products in minutes rather than weeks. All you have to do is copy and paste your Git URL or choose one of our existing templates.

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