Operations | Monitoring | ITSM | DevOps | Cloud

Ever been on the receiving end of a useless chatbot response? Imagine asking, “Why is my 5G down in this area?” and getting, “Try restarting your device.” No context. No understanding. No real help. The issue here, however, isn’t bad intent—it’s that the bot doesn’t understand telecom-specific language or service outage patterns. Instead of easing your support load, it’s driving frustrated customers to jam your support lines.

As the number of organizations moving their ML projects to production is growing, the need to build reliable, scalable architecture has become a more pressing concern. According to BCG (Boston Consulting Group), only 6% of organizations are investing in upskilling their workforce in AI skills. For any organization seeking to reach AI maturity, this skills gap is likely to cause disruption.

We’re excited to launch relaxAI, an AI assistant designed with one paramount focus: your privacy. In a world where AI tools are becoming indispensable but concerns about data usage are at an all-time high, relaxAI has been created as an assistant you can trust by combining cutting-edge AI capabilities with an unwavering commitment to security and transparency.

As an observability leader, at Logz.io, we pride ourselves on continuous innovation. That’s why, last year, we released our AI agents to revolutionize observability by helping businesses, and their engineering and DevOps teams, automate data analysis and root cause analysis. The primary way in which engineering and DevOps teams interact with the agents is by asking performance, troubleshooting, and optimization-related questions.

In bridging traditional enterprise environments with modern GPU resources, we're helping organizations build AI infrastructure that's truly ready for production workloads. Co-authored by Duncan Ng, Vice President Solutions Engineering, Vultr As enterprises move from AI experimentation to production deployment, most are realizing a fundamental truth: Successful AI adoption requires more than just access to GPU computing power.

2024 was the GenAI year. With new and more performant LLMs and a higher number of projects rolled out to production, adoption of GenAI doubled compared to the previous year (source: Gartner). In the same report, organizations answered that they are using AI in more than one part of their business, with 65% of respondents mentioning they use GenAI in one function.