The advent of digital technologies requires industries, including shipping services, to meet the demands of global e-commerce. Accurate tracking, efficient inventory management, and seamless deliveries are now essential components that define successful shipping providers. Shipping providers turn to advanced tools to navigate the complexities of contemporary logistics. These solutions help automate workflows, minimize delays, and optimize customer service.

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.

Bringing machine learning (ML) models into production is often hindered by fragmented MLOps processes that are difficult to scale with the underlying data. Many enterprises stitch together a complex mix of various MLOps tools to build an end-to-end ML pipeline. The friction of having to set up and manage separate environments for features and models creates operational complexity that can be costly to maintain and difficult to use.

Buy 10 sandwiches, get 1 free. Classic punch cards (and fishing for them in your wallet or occasionally misplacing one) have become a thing of the past, as today's digital landscape demands more innovative solutions. Today’s customer loyalty programs are increasingly sophisticated—evolving, proliferating, and diversifying across every industry from retail, travel, and hospitality to healthcare (e.g., a discount for paying within 30 days of a hospital visit).

The value proposition of embedded analytics in 2024 has never been clearer: real-time insights that help your user base make smarter decisions, while enhancing their engagement with both data analytics as a tool, and your product or business systems. But what about the costs?

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.

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.

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.