Systems | Development | Analytics | API | Testing

Gen AI call center co-pilots can provide enterprises with operational visibility and insights while automating repetitive tasks, to improve the customer experience. In this session, we’ll show how a large health insurance provider implemented an agentic co-pilot designed scale across multiple call centers and environments. To dive deep into the architecture and see a demo of the co-pilot, you can watch the webinar this blog is based on.

At ThoughtSpot, we’re on a mission to empower every business user to become a data champion. Over the past year, I’ve witnessed firsthand how organizations across Australia and New Zealand are embracing this vision, transforming the way they work, make decisions, and serve their customers. Today, I’m excited to share some of the incredible momentum we’re seeing in the region and to celebrate the forward-thinking organizations leading the charge.

AI is everywhere. Depending on who you ask, it’s either making developers obsolete, or it’s just hype. But for those of us who’ve actually used AI tools in real-world engineering workflows, especially in complex distributed systems, the truth lies somewhere in between. At Multiplayer, we’ve spent the past few years exploring how AI can—and can’t—help solve two of the most persistent challenges in distributed systems: documentation and debugging.

In the fast-paced world of modern software development, performance is no longer just a backend concern—it's a critical driver of user satisfaction, infrastructure cost, and business growth. At NodeSource, we're excited to unveil a new feature in the N|Solid platform that takes Node.js application performance to a new level: N|Sentinel.

As cloud native applications grow, managing resource usage and controlling costs becomes increasingly complex. Choreo's Cost Optimizer addresses this challenge by providing AI-driven insights and actionable recommendations, enabling developers and organizations to streamline resource utilization and reduce unnecessary expenditures.

AI has been booming and has become a transformative force in several industries, software testing and QA are no different. With the evolution of technology and complexity of applications, automation and big-volume data analysis require extra assistance, and that’s where AI might come in handy. In QA, AI could help improve the efficiency of test management tasks, reducing manual effort.