What would happen if an organization could parse its operational logs in real-time to identify issues and errors that might otherwise go undetected, but which could have a significant impact on the customer experience? In many cases, these kinds of issues run rampant in enterprise organizations, but those organizations simply do not have the resources or means to track them down and resolve them effectively. But in the experience economy, this is becoming a costly problem.
Using technology developed for defense applications, Loom Systems is applying AI technology to help IT teams identify and connect the dots between operational issues and their impact on the customer experience. Using unsupervised learning approaches and their proprietary set of heuristics, they can ingest log data from nearly any source and rapidly identify potential service anomalies.
While there are a number of organizations applying various AI techniques in the IT operations space, Loom Systems recognized that most of them were focused on identifying significant trends via intelligent event correlation. While the company’s technology will identify major impacts as well, what sets it apart is its ability to automatically identify micro trends that may only be affecting a small number of customers, but which may have a big customer experience impact.
The company’s focus on the business impact of operational issues is also manifest in its ability to automatically identify issues that may span the customer journey as the customer traverses multiple systems. Moreover, using its AI-powered interface, the company connects logline errors with insights and recommended actions from a dynamically-built knowledge base, enabling operations staff to more rapidly resolve the issues most critical to protecting the customer experience.
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