How to Start an AI Company — AI for Startups from Idea to Launch

The complete guide to building a tech startup that uses AI to create incredible, fast-growing products.

·10 minute read
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The AI wave kicked off by OpenAI with its ChatGPT product is changing the way tech startups design, build, and market their products. In this guide, we'll walk you through the steps to launch your own tech startup that leverages AI.

AI is not a magic bullet that will guarantee your startup's success. But it can provide a unique edge that can help you build a product that's better than the competition in its design, featureset, and user experience.

In this guide, we'll cover the following topics:

  1. Idea Generation: How to come up with an AI product idea that's valuable and, ideally, difficult for competitors to replicate.
  2. Product Design: How to think about your software's design with AI features in mind.
  3. Engineering: How to build your SaaS's AI features and integrate them into your product.
  4. Marketing: How to market your product to your target audience.

We won't be covering fundraising, company formation, or other topics that are important for starting a tech startup but are not specific to AI.

How to Come Up with an AI Product Idea

The first step in starting an AI startup is coming up with an idea for a product that leverages AI in a new way, ideally with a "moat" that makes it difficult for competitors to replicate.

Luckily, it's extremely early in the AI wave, and so most verticals are still ready to be disrupted. But remember that regardless of the market you pick, you'll need to create a competitive product that fully addresses the needs of your potential customers, and AI generally won't do that for you—not even half of it. It can be the extra bit of magic that makes your product stand out above the rest, but in most cases it won't be the whole product.

Here's the secret to finding a good AI software idea. Look for a type of software that has the following characteristics:

  1. Monotonous tasks: If users regularly perform repetitive, monotonous tasks that require little creativity or difficult decision-making, AI can likely automate much of it. Think bookkeeping (classifying transactions), customer support (answering common questions), or data entry (extracting information from documents). Or, more creatively—animation software (with artists spending hours creating models for tiny objects in a scene) or digital art software (with artists spending hours on minor details, backgrounds, etc.).
  2. High error tolerance: AI makes mistakes, so you'll need to target a market where minor errors are acceptable. A customer support tool that occasionally gives the wrong answer is fine; a medical diagnosis tool that occasionally gives the wrong diagnosis is not.
  3. High data availability: The more data you have demonstrating the correct response to a given input, the better your AI will perform. So look for markets where data of this kind is plentiful (like transaction categorization data in bookkeeping software).
  4. High value: The more valuable the task you're automating, the more you can charge for your product. So look for markets where the task you're automating is valuable to the user.

Those are the key characteristics of a good AI software idea. If your idea doesn't meet these criteria, be careful—you may be building a product that's much harder to sell than you expect!

How to Design Your AI SaaS Product

Once you've got the idea in hand, design is the next step. Given all the ways that AI is changing product design, you'll need to think about your product's design from first principles.

Start with the fundamental question: what problem is the user trying to solve? This is the most important question in product design, and it should guide every decision you make about your software.

If you've designed software before, you'll likely instinctively lean on the same design patterns you've used in the past. Toss aside as much of that as you can! It's critical to truly approach the problem from first principles, since AI provides a new set of tools that can solve problems in ways that were previously impossible.

Here are some examples.

  1. Onboarding: The "traditional" approach to onboarding is to create a guided tour of your software's features, showing the user where to click, how all the different tools and options are organized, and so on. This may be completely unnecessary for your software! Why not use an AI copilot to let the user simply tell the software what they want to do? Personalization and user-driven onboarding are likely to become the new norm.
  2. Predictive features: AI can ingest the user's usage data and predict what they're likely to do next. This can be used to suggest actions, pre-fill forms, or even automate tasks entirely. Think about how you can use this to make your software magical for the user, and incorporate it into your design. What's an unobtrusive way to suggest time-saving actions to your users?
  3. Error handling: AI makes mistakes, since it's not completely deterministic. How will you handle those mistakes? If you're truly embracing the power of AI features, you'll need to design a way to gracefully handle errors and allow the user to correct them.

These are just a few examples of how AI can change your product's design. The key is to think about the problem you're solving from first principles, tossing aside previous assumptions about how to tackle it, and considering how AI can help you solve it in a new way.

How to Build Your AI Software

Once you've got your design in hand, it's time to build your AI software. This is the most technically challenging part of the process (of course), but it's also the most rewarding. You'll see your product come to life, and you'll be able to start testing it with real users.

This is a broad topic, so we'll break it up into a few key areas. If you have no technical background whatsoever, find a technical co-founder and send this section of the guide to them! If you're a technical founder, read on.

Tech Stack

The non-AI parts of your software will likely be built using a traditional web stack. This means you'll have a frontend (the part of the software the user interacts with), a backend (the part that handles data storage, business logic, and so on), and a database (where you store your data). We'll assume you're familiar with these concepts.

The AI features in your software will run on your backend, likely using a REST API to communicate with OpenAI or similar services. You'll need to design your backend to handle these requests efficiently, so we recommend avoiding serverless architectures for this part of your software. Have your frontend communicate with your backend using a REST API, and have your backend handle the AI API requests.

Database Design

It is critical to keep your data structures simple. At the time of writing (June 2024), LLMs are still inconsistent in their ability to reason and write code accurately when given complex inputs. This means that the simpler you keep your data model, the better results you'll get out of the LLMs and therefore your AI features.

Avoid low-utility fields, complex relationships between data, and other unnecessary complexity like the plague.

Large Language Model Selection

You'll likely be using a large language model (LLM) to power your AI features. At the time of writing, OpenAI is the most popular choice by far. You'll need to decide which model to use, which will depend on your specific use case.

Here are a few things to consider:

  1. Cost: Consider how often users will use your AI features, how many tokens they'll generate, and how much it will cost you.
  2. Use Case: Different models are better at different tasks. Some are particularly good at writing code, for example—so if your features involve code generation (even under the hood) you'll want to choose a model that's good at that. Other models are better at summarization or text generation, so consider your use case carefully.
  3. Speed: Some models are faster than others. If your AI features need to be real-time, you'll need to choose a model that can generate responses quickly.
  4. Fine-tuning: If you expect to have large amounts of proprietary data, you may want to consider fine-tuning a model on that data. This can give you a competitive edge, but it's expensive. If it fits your use case, make sure you pick a model that can be easily fine-tuned.

How to Market & Sell Your AI Product

The next step in launching your AI startup is, well, launching! You'll need to market your product to your target audience, get them to sign up, and start using it.

We'll try not to cover the basics of marketing here, since there are plenty of resources on that topic already. Instead, we'll focus on how to market an AI product specifically, and how to use AI in your marketing and sales processes.


When marketing your AI product, you'll want to focus on the unique value proposition that AI brings to your product. This could be anything from time savings for the user, to better recommendations, to more accurate predictions. Whatever it is, make sure you're clear about it in your marketing materials.

Don't fall into the trap of marketing your product as AI. Users don't care about the technology under the hood—they care about the value it provides to them. So focus on that value, and use AI as a tool to provide it. We know it's hot these days to just launch "AI for X", but trust us when we say that's not what customers want or care about.

Focus on the problem and how your product solves it.

When it comes to using AI in your marketing functions, here are a few ideas:

  1. Chatbots: Use AI chatbots to answer common questions from potential customers right from your homepage. This can save you time and provide a better user experience.
  2. Content generation: Use AI to generate content for your marketing materials. This could be anything from blog posts to social media updates to ad copy. Be careful on quality, though. A bad reputation for quality can be hard to shake—so can getting flagged as spam by Google.
  3. Competitive analysis: Use AI to analyze your competitors' marketing strategies and find ways to differentiate yourself.
  4. Sentiment analysis: Use AI to analyze social media mentions of your brand and products. This can help you understand how customers feel about your brand and identify areas for improvement.
  5. Customer feedback analysis: Use AI to analyze customer feedback and identify trends and patterns. This can help you improve your products and services based on customer input.
  6. Social media monitoring: Use AI to monitor social media for mentions of your brand and products. This can help you identify trends, track sentiment, and engage with customers. Again, be careful with quality here.


Sales teams should follow the same philosophy as marketing teams: focus on the value that your product provides, not the technology that powers it. Rather than "AI Bookkeeping Software", market yourself with the headline, "Classify Your Transactions in Seconds, Not Hours".

When it comes to using AI tools in your sales process, here are a few ideas:

  1. Lead scoring: Use AI to score leads based on their likelihood to convert. This can help your sales team prioritize their efforts and focus on the most promising leads.
  2. Customer segmentation: Use AI to segment your customers based on their behavior, preferences, and other factors. This can help you tailor your marketing and sales efforts to specific customer segments and improve conversion rates.
  3. Personalization: Use AI to personalize your sales pitches and marketing materials based on customer data. This can help you engage with customers more effectively and increase conversion rates.


Starting a tech startup has never been more accessible, thanks to the many tools, frameworks, and resources out there, especially the new wave of AI tools that help even non-technical folks build products.

If you're considering starting an AI startup, we hope this guide has given you a good starting point. Remember that AI is not a magic bullet that will guarantee your startup's success, but it can provide a unique edge that can help you build a product that's better than the competition in its design, featureset, and user experience.

If you're looking for an easy way to add an AI copilot into your product, check out our AI copilot builder.