Software Engineering Insights

Imagine having a replica of your car - a digital twin that can carry out all the functions of your actual car. This computerized model enables you to virtually drive your vehicle, explore its workings and even test out repairs or enhancements, all conveniently from your computer or mobile device. By experimenting with this twin, you can discover the methods to maintain your car, enhance its performance and identify potential issues before they escalate into real concerns. This digital companion acts as a tool that empowers you to oversee and upkeep your vehicle. Essentially a digital twin resembles owning a virtual version of your car that presents opportunities to enhance both your driving experience as well as vehicle maintenance.

Imagine trying to solve a jigsaw puzzle without all the pieces. Won’t be possible, right? While you might be able to make some progress, you won’t be able to finish. Synthetic data is like having all the pieces of the AI puzzle with you -  right at your fingertips. It lets you build and fine-tune your models minus the real-world data limitations. Think of it as a flexible way to train your algorithms with vast amounts of artificial data that closely resembles the real one. Plus, it also helps you sidestep privacy challenges, security risks, and high costs that come with getting access to real-world data. 

Businesses today often come across the challenge of picking the most appropriate architectural approach for their applications. Although microservices are known for their scalability and flexibility, modular monolith is stepping up as a great option for organizations seeking to balance between manageability and complexity.

Synthetic data is a term that's been buzzing around the tech world, especially in discussions about AI. So, what exactly is it? 

With smart manufacturing & Industry 4.0 reinventing the landscape, the need for advanced and interconnected manufacturing systems is now even more essential than ever. At the centre of this evolution lies Manufacturing Execution Systems (MES), weaving emerging technologies such as Industrial Internet of Things (IIoT), artificial intelligence and cloud computing, into the intricate process of manufacturing.

Over time, there has been an astronomical rise in the number of ways Artificial Intelligence can be utilized for the betterment of everyday lives. The technology is almost everywhere- in traffic predicting Google Maps, in video surveillance cameras, in face recognition apps, in speech recognition & language translation solutions, etc. In fact, AI is one of the trending technologies during the COVID-19 pandemic.

With the changing consumer behaviour and desires, the lines between the industries such as retail, fashion, fintech and healthcare are slowly and steadily fading with technology. What we are seeing today is a universe where smartwatches are used for payments, smartphones are enabling everything from virtual shopping to virtual tours, credit cards are branded with in-store offers, and online pharmacies are delivering smart health devices right at the doorstep. 

One such technology that is changing the way we look at the future is - Wearables.

Globally, 1 in every 2 outsourced projects either fail outright or fail to meet the client’s requirements.

Most software development projects fail or lose momentum because organizations choose a software development company based on cost comparisons or skill sets in any specific technology. However, this approach can slowly lead to a growth gridlock and push you way behind your business goals.

Agile methodologies are an integral part of modern software development. Their focus on adaptability and customer feedback makes them important in software development. On the other side, AI is changing industries with automation, data analysis, and smart decision-making.

Integrating AI with established legacy systems presents both, a challenge and a business opportunity. Legacy systems are crucial to many businesses. However, they often struggle to keep pace with modern requirements due to their limited flexibility and outdated technology stack.