Blogs

By 2027, Gartner predicts that 80% of enterprises will integrate AI testing tools into their software engineering toolchains—up from just 15% in 2023. This shift reflects the complexity of modern applications—rapid release cycles, dynamic UIs, cross-platform demands, and architectures built on microservices, APIs, and the cloud.” AI-driven tools promise enhanced

Amid soaring AI deployment costs and data privacy concerns, CIOs are finding a powerful ally in Small Language Models (SLMs). These compact yet potent models offer targeted AI performance without the resource drain of their larger counterparts—delivering on cost, security, and precision like never before. SLMs bring several key competitive

Introduction Copilots and AI Agents are ushering in a new wave of intelligent process automation, ready for prime time in the enterprise. They are poised to fundamentally redefine how enterprises approach and apply software. Real-World Examples: These examples demonstrate the practical applications of Copilots and AI agents, setting the stage

Introduction: Setting the Stage for AI Compliance The European Union (EU) has taken a pioneering step in regulating artificial intelligence (AI) with the introduction of the EU AI Act. As AI continues to permeate every aspect of our lives — from healthcare and finance to legal services — ensuring these

Introduction Advancements in Large Language Models (LLMs) have ushered in a new era of AI capabilities, closely mimicking human-like reasoning. Yet, despite their prowess, LLMs face significant challenges in logical reasoning, primarily due to their inherent design limitations. They lack causal models of the world, structured internal knowledge representation, and

Introduction: AI Models like ChatGPT, Bard  & Claude are constantly evolving by incorporating more powerful feature functionality. If you are already leveraging this tool as part of your workday, how do you keep up with the steep learning curve to get the best responses from the AI models? Outlined below