
LogicMonitor announced the launch of Dexda, an AI solution for Hybrid Observability.
Using machine learning and Natural Language Processing (NLP) to automate insights and deliver a contextualized experience, LogicMonitor's Dexda empowers ITOps teams to effortlessly identify problems, determine the root cause of those problems faster than ever before, and prevent events from exploding into business-critical incidents.
"Being on the bleeding edge of technology requires shifting the organizational mindset from reactive responses to proactive insights, getting comfortable with humans leveraging machines for greater agility and innovation," said Christina Kosmowski, CEO, LogicMonitor. "Our users crave superior anomaly detection, predictive analytics, and intelligent alerting - where we are best in class. Dexda is the latest step in the evolution of our AIOps technologies. Now, we are advancing Generative AI for solving customer challenges, making LogicMonitor even more user-friendly as a co-pilot."
With Dexda, LogicMonitor offers:
- Robust AI capabilities through the application of sophisticated algorithms on historical and real-time data to provide purpose-built, layered intelligence for faster resolutions. LogicMonitor first introduced AI into its platform with its LM Intelligence feature within the LM Envision platform.
- Using AI machine learning, LM Intelligence acts as an "early warning system" for IT and Cloud operations by providing dynamic thresholding, anomaly detection, forecasting and more, empowering teams to reach a significantly lower mean time to resolution (MTTR) and reduce risks to the business.
Dexda ingests events from LM Envision to transform them into contextualized insights. The advanced machine learning techniques automatically correlate data to identify and alert based on time, resources, and pattern disruption. Dexda users can resolve critical issues faster than their competitors with these capabilities:
- Reduced Alert Noise - Advanced machine learning techniques, contextual enrichment capabilities, and deduplication efforts filter through thousands of daily events to produce succinct alerts for the most critical incidents, and drive down MTTR
- ServiceNow Ready – Includes a seamless bi-directional integration with ServiceNow Incident module to fit correlated insights into standard IT workflows. ServiceNow CMDB data automatically enriches Dexda alerts to drive additional context for alert correlations
- Adaptive correlation – Avoid delays in escalating insights to ServiceNow by automatically re-clustering alerts into new insights when a more optimal clustering option is found
- Extensible correlation – Customizable user-defined correlation models target both the alert and enriched CMDB data, based on what makes sense for the business
- MSP Ready – Now supports multi-tenancy with correlations scoped to each tenant
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...