AI for Product Development – How Monolith AI Uses No-code AI for Product Engineering
We live in a time where technology thrives more than anything else. A technology era, if you will. One of the fastest-growing branches of technology is none other than artificial intelligence. In the last few years, AI has found its way into medicine, education, agriculture, business, etc. In addition, more and more companies are recognizing the power and influence of AI and, therefore, implementing its applications in their business. Since many startups and enterprises are rushing to create new products and get them on the market, AI and ML (Machine Learning) have been making a significant contribution in the field of product development.
One of the world-leading companies that aid in bettering product development is Monolith AI. The main goal of this startup is to help engineers dramatically improve the product development process by creating innovative software that uses the newest machine learning technologies. Therefore, the question is not whether but how AI betters the world of business and product engineering.
Accelerate engineering research and development with the power of AI
AI powers and accelerate engineering research and development by using software and hardware components. With the help of ML, machines support both sophisticated production lines and demanding manufacturing activities. Additionally, AI programs may automate low-value tasks, allowing engineers to focus on the ones with a higher value. Machines will play a critical role in engineering decision-making by applying machine learning to detect patterns in data. Automobile manufacturers have been using robots on the manufacturing line for a long time, and they’ve progressed from simple engineering tasks to managing numerous precise motions necessary for some of the most complicated elements of the process.
As the leading AI software for engineers in the automotive industry, Monolith AI radically changed the way many automotive companies function. How? For example, they created software for Jota Sports that simplifies the validation of the automobile and simulation data.
Finding a good AI use case for your engineering company
When it comes to integrating an AI use case into your engineering company, there are a few extra precautions that have to be taken. In other words, it’s vital to think about the possible applications of AI before training neural networks every time you come across some data. According to Monolith AI, there are three key conditions for a successful AI adoption and application when it comes to your engineering company:
- Knowledge of AI capabilities
- Knowledge of company pain points
- Knowledge of available data
The bottom line is when your employees (engineers) understand the pain points and the data they’ve accumulated over time, the most effective initiatives will emerge. They will come up with practical, viable, and beneficial use cases if you educate them and expose them to AI properly.
Using No-Code for Products
What are No-Code Development Platforms? Simply put, no-code platforms are allowing both programmers and non-programmers to create application software by replacing complex program languages with simple drag and drop interfaces. Now, what does this mean for AI? Actually, No-code platforms are enabling the very implementation of artificial intelligence without the need to hire a slew of pricey programmers and data scientists. This is a huge step forward for small businesses with an abundance of data on their shoulders. In other words, these no-code tools can empower employees to develop innovative ways to utilize data to drive or maximize their work and, thus, the business.
Monolith AI is using no-code tools to power engineers in extracting relevant insight from complex engineering data. The main goal is to apply these insights to decrease the number of designs, tests, and simulations, allowing companies to get their product to market much faster.
How AI can exploit data sources of different quality and availability
To be beneficial, AI doesn’t have to replace your existing tools; it may also be employed to enhance them. However, with the current quantity of data, certain serious engineering issues may be too complex for AI to model accurately. Take cars, for example. Single datasets may not allow you to create exact predictions about your car’s drag. Nevertheless, AI could still be used to connect data from multiple sources. In other words, AI can learn and understand how various sources of fidelities are connected (e.g. simulations and experimental tests). In addition, without having to undertake trial runs, the model may be used to generate high-fidelity predictions from low-fidelity outcomes.
Monolith AI changing the history of product development
With its latest technology and hard-working software developers, Monolith AI is changing the course of the history of product development. Not only are they improving the process of product development by implementing no-code platforms and tools but they are also empowering engineers worldwide to accelerate product development workflows with their funding program. All things considered, they are revolutionizing both the world of AI and engineering.