When you raise venture capital money, investors will shape everything from the strategy and product to the thought process. That may not be the best for what you are offering, especially in the AI space, so I recommend starting the AI startup without them: there are no more elements that distract from the objective.
This type of startup can be a competitive advantage in these times when it is difficult to obtain capital. Here are three aspects that you should focus on so that you can build the startup without being indebted to anyone.
Build to solve a specific problem
Bootstrapping requires that clients be involved when creating the Roadmap of the product. This is a great way to understand customers' businesses, problems, and blind spots, but it also serves a crucial purpose: it allows you to address a specific problem.
Once you know the problem that needs to be solved, you must find out what your customers' data capabilities are and if they have the data to solve that problem. A user feedback loop is then created so you can test, train your AI to be smarter, and provide the desired result.
The purpose of a startup is to understand, find and solve a specific problem and sell the solution to customers dealing with that problem.
In this case, an agile methodology will allow you to examine the quality of the result and understand what needs to be modified. It will also speed up the feedback loop, which in turn will help the algorithm learn and improve faster.
An organization must develop and mature from the perspective of the data to be able to manage an AI platform. So you have to understand the format of customer data before you start thinking about how to receive it. Does the data come from one or several sources? Are there redundancies?…
Es key determining data quality and the sources thereof. If the client has quality data, an API can be created to accept that data and leverage it by formatting it so the AI can use it.
You have to ask yourself if you are creating the technology for a real-world application that companies will need and if you are investing every penny in providing value to the product, the customer and the team.
The sooner you provide a solution for which customers pay full price, the sooner you will have a business model.
Use the status of autonomous startup to attract talent
Attracting and retaining talent is a skill in itself. There is a lot of competition for the best developers in the world, so articulating the company's vision and making them believe in it is very relevant, because a new startup will never have the resources of Google, Meta or OpenAI.
Although you will never be able to pay as much as Microsoft, as a startup, you have other strengths. Offer candidates the opportunity to make an impact, have influence and have a say in the Roadmap or allow you to innovate and stay at the forefront using the latest and best technology to generate results.
Big Tech may outperform on the compensation and branding fronts, but the startup offers a very different and far-reaching package to developers: a fusion of equity, technology, accessibility and freedom that can provide value to the kind of people who will prosper in the new initiative.
When you find engineers with a strong set of basic technical skills, it requires encouraging them to engage with all the technology and helping them evolve into full-stack developers. If someone originally started as a back-end developer, help them develop the knowledge to also understand the front-end and how it interacts with the UI/UX team.
It's not about proximity; It's about talent. It's naive to think that the best talent lives within an hour of the office. Distributed teams will be the way in 2023. The best developer for the company could live in Norway and the back-end developer could be in Japan. The effort and commitment is to find that talent and unite them.
Have strong leadership in each group and form these groups into a single team. Many people make the mistake of developing their product in silos, and mistakes are made and blind spots are created when people develop in a vacuum.
Think about security from the first line of code
When handling people's data, security is of utmost importance. Security starts with making sure the platform architecture maintains the privacy of each customer's data within the system.
The entire platform cannot simply be based on one database, as that will lead to mixing of data. The architecture should have divisions, permissions, structures, and firewalls in place. In fact, the developer's schedule should be built around these layers of security and data protection.
Customers can also perform their own security audits as part of the approval process. The standards used must be approved along with service level agreements and protocols focusing on security from the beginning.
Getting these basics right is the way to work with clients who write big checks. Build AI for a large company, not with heavy layers, but knowing that security will be the primary point to consider.
Eliminate any vulnerabilities by performing testing, regression testing, unit testing, systems testing, integration testing, and acceptance testing.
Start with the basics: the business problem
After all, the purpose of a startup is not to create a product or find customers. It's about understanding, finding and solving a specific problem and selling the solution to customers facing that problem.
Solving these problems and building relationships is the way to start an AI business and grow it organically. This will not help you get rich quick. Founders considering startups should be prepared for the long haul and reinvest every dollar into building the product, focusing on people, and innovating.
