How to Create an Effectively Engineered Product

What is a Great AI product?

A good product is more than just functional; it is also usable, understandable, aesthetically pleasing, and useful in the long run. Building such a product is a complex process that requires a deep understanding of the underlying technology, user needs, and the unique challenges of the particular platform.

In the realm of AI-based products, the process is even more complex. The technology is constantly evolving, and staying up-to-date with the latest developments is critical. Additionally, AI-based products typically require large amounts of data to train the model, which can be challenging to obtain and process. Finally, designing a user-friendly interface for an AI-based product can be difficult, as users may not understand the underlying technology and may be hesitant to trust the product's recommendations.

But what does it take to build a good product?

How to build great products?

Experience is a critical factor in building a successful product. Someone who has spent 10,000+ hours building products is a strong indication that they can build a usable product. Our founder has built several AI products, including a multi-lingual Summarizer for Apple/Android and multiple apps. He has also worked with some of the brightest minds in Artificial Intelligence, such as Qi Lu, Paul Viola, and Joaquin Candela. Qi Lu, Paul Viola, and Joaquin Candela are among the brightest minds in Artificial Intelligence, and they have made significant contributions to the field. Qi Lu has extensive experience in developing and deploying AI-powered products, including Bing and Microsoft Office, while Paul Viola is a pioneer in computer vision and is the co-inventor of the Viola-Jones face detection algorithm and a ex-VP of Amazon Air(the Drone Arm of Amazon). Joaquin Candela, the Director of Applied Machine Learning at Facebook, has made significant contributions to the field of machine learning, particularly in the areas of recommender systems and online advertisements.

What's more, our founder has been creating AI products before it became trendy. His first product was a Stock Predictor based on Pattern Recognition Theory before he even graduated with a Bachelor's degree! He went on to work at Amazon and Microsoft, where he had the opportunity to work on the world's largest datasets in Artificial Intelligence and Machine Learning. This experience has been critical in understanding scale and learning from two of the only workplaces in the world with the largest data available.

Along with experience, it's important to have the ability to finish a product on multiple platforms, system and AI engineering, and the ability to avoid feature creep and bring the product to launch.

Building a product for multiple platforms, such as Apple and Android, is desirable because it allows the product to reach a broader audience. However, this approach can also be difficult to execute. Each platform has its own set of design guidelines, technical requirements, and user expectations, and adapting the product to each platform while maintaining a consistent user experience across all platforms requires careful attention to detail, flexibility, and a willingness to adapt to the unique requirements of each platform.

Overall, building a good product requires a deep understanding of the technology, the user's needs, and the unique challenges of the particular platform. It also requires the expertise of some of the brightest minds in Applied Artificial Intelligence and a willingness to adapt to new developments in the field.

A Growth Mindset

Finally, a growth mindset is crucial in building a successful product. A willingness to learn, adapt, and grow will help overcome challenges and keep the product moving forward. A good product isn't just built in a vacuum; it's a result of a growth mindset, experience, expertise, and a deep understanding of the user's needs and customer focus.