Of all of the emerging tech of the last two decades, artificial intelligence (AI) is tipping the hype scale, causing organizations from all industries to rethink their digital transformation initiatives asking where it fits in. In Financial Services, the projected numbers are staggering. According to a recent McKinsey & Co.

It is estimated that between 80% and 90% of the world’s data is unstructured1, with text files and documents making up a significant portion. Every day, countless text-based documents, like contracts and insurance claims, are stored for safekeeping. Despite containing a wealth of insights, this vast trove of information often remains untapped, as the process of extracting relevant data from these documents is challenging, tedious and time-consuming.

More than two-thirds of companies are currently using Generative AI (GenAI) models, such as large language models (LLMs), which can understand and generate human-like text, images, video, music, and even code. However, the true power of these models lies in their ability to adapt to an enterprise’s unique context. By leveraging an organization’s proprietary data, GenAI models can produce highly relevant and customized outputs that align with the business’s specific needs and objectives.

We’ve integrated generative AI features deeply into our no-code test automation platform, Rainforest QA. Each of these features is designed to help you avoid the time-consuming and otherwise annoying work of keeping automated test suites up to date — so your software development team can stay focused on shipping, fast. In this video, our CEO, Fred Stevens-Smith, walks through what some of these genAI features look like in action.

In the mobile-first era, high-quality apps are essential to keep users happy and engaged. At our software testing company, we specialize in the intricacies of mobile app testing. This blog outlines best practices to guarantee your apps provide a smooth and reliable user experience.

As a software testing professional, you know the importance of efficient and reliable testing processes. With Katalon's StudioAssist, you can streamline your testing workflow and ensure high-quality results. In this comprehensive guide, you'll learn how to utilize StudioAssist for mobile, API, and web testing through practical examples and step-by-step instructions. Whether you're a seasoned tester or just starting out, this guide will help you harness the full potential of StudioAssist.

In part one of this series, we walked through how to use Streamlit, Apache Kafka, and Apache Flink to create a live data-driven user interface for a market data application to select a stock (e.g., SPY) and discussed the structure of the app at a high level. First, data with information on stock bid prices is moved via an Alpaca websocket, then, it’s produced to a Kafka topic in Confluent Cloud where it is also processed with Flink SQL.

How to use Gemini generative AI models through BigQuery to boost productivity in Google productivity tools like Sheets.

In today's rapidly evolving digital landscape, superior software is essential, whether it be the apps on our phones or the intricate systems supporting various business requirements. The process known as Software Testing (ST) has undergone an immense transformation in order to meet this necessity where Artificial intelligence (AI) and machine learning (ML) play a great role in QA solutions.