Systems | Development | Analytics | API | Testing

Automation has helped countless businesses enjoy improved scalability, accuracy, and efficiency. However, traditional automation is known for its complexity. Setting up automation workflows often requires extensive coding knowledge, advanced technical skills, and a deep understanding of underlying systems. This makes it challenging for non-technical users to independently implement and maintain automation solutions.

In government agencies around the world, large enterprise legacy systems are what stand in the way of desperately needed modernization programs. Such legacy systems can be large enterprise resource planning (ERP) implementations or custom-built applications using complex codebases. Simply put, they are not supportable, upgradable, and do not provide the rich user experience customers have become accustomed to.

One of the main challenges while building a software application is data storage. An easier way would be to store data in Excel and its likes, but they don’t really help with large amounts of data. When dealing with high volumes of data, a database is a better choice. You can either opt for a relational database such as Microsoft’s SQL Server, or a non-relational database such as MongoDB.

The market for artificial intelligence (AI) stood at $184 billion in 2024 and is expected to more than quadruple in the next six years. While these expectations are astonishing, AI experts think they’re conservative, to say the least, and the actual market value would be considerably bigger. Large language models (LLMs) like GPT 3 have ushered in the age of AI. They’re finding applications as varied as complex scientific research and writing lyrics for rap battles.

Traditional test automation usually needs a lot of coding skills, which makes it take heavy resources time-consuming, and unscalable. In an increasing rate of software development speed, QA teams have to adhere to the fast release cycle while still maintaining the quality. This is happening now, and by 2025, low code and no code test automation will be the center of QA. But why? What drives these companies to jump into such new, relatively unstable platforms?

The term “artificial intelligence” was first coined by James McCarthy in 1955. In less than 70 years since then, AI has gone from being a scientific concept to a fact of life.

Invoice scanning is a crucial step in managing the accounts payable (AP) process for any business. You may think that invoice scanning is no longer required if invoices are received electronically. However, according to Bottomline’s The State of ePayables report, more than a staggering 48% of invoices are received in paper format. In other words, paper invoices are still very much a part of modern accounts payable, and so is invoice scanning.

Invoice management is an important task regardless of the size of your business, but it can quickly become overwhelming due to the sheer volume of invoices, varying formats, and the need for accurate tracking and timely payments. Manual processes with paper invoices are time-consuming, often leading to errors, delays, and difficulty in maintaining organized records. However, with the right invoice processing software, you can streamline invoice management, saving time and reducing errors.