Imagine you are the chief engineer launching a groundbreaking electric vehicle. Riding the thrill of early promising customer feedback, you begin to hear concerning rumors of complaints about a steering system malfunction. Worried about a product recall, you mobilize your team to dig into the issue. Immediately, they get stuck. The labyrinth of messy data sources scattered across the company stalls progress. The investigation drags on for months, as your team combs manually through vast spreadsheets of customer calls, returned parts, error codes, telematics, and more, racing against the clock. As customer grievances escalate, a safety event hits the media spotlight. The cost in warranty, recall expenses, damaged reputation & revenue loss spirals into the hundreds of millions.
Now, picture a different scenario: One where your team has the extraordinary ability to listen to & absorb every stream of information about your product’s performance. A world where every customer call and blog post, every service event and returned parts analysis, manufacturing line hiccup, supplier quality notice, telematics feedback, log file, software patch, and more - is not just noise, but a symphony of clear, actionable insights. This transformation from data noise to valuable information enables your team to daily pinpoint and address the most pressing issues jeopardizing the integrity of your products.
Enter Axion Ray, the AI-powered command center your engineers use to swiftly identify, investigate, root cause, solve, and monitor issues that could lead to downtime, safety, warranty cost, and recalls. Axion Ray empowers your team with the tools to navigate the complexities of responding to emerging issues, ensuring problems are resolved before they escalate.
Major quality issues impacting customers are debilitating for engineering leaders overseeing product releases, across industries. Medical device manufacturers recall malfunctioning dialysis machines impacting patient safety, airlines ground flights when critical components are not working, electronics manufacturers struggle with blank screens, electric vehicle OEMs recall millions of vehicles when software-rich products do not work as intended, and even new parents sometimes find their strollers have wheels that just don’t work the way they thought they would. These problems bring together customer support, hardware & software engineering, manufacturing, supply chain, and more, to understand the root cause & introduce a fix.
Enterprises are having much success with AI tools to automate responses & decision making, turning first-call in a contact center or service organization into a chatbot & 101 FAQ. But when it comes to solving new, unforeseen escalations, such as why hundreds of customers around the world are complaining about a never-seen-before error code on a medical device, the current AI products available to enterprises fall short. To solve these emerging issues, engineers use more careful, deliberate root cause investigation, taking in data from different streams to solve this - a “
System 2” mode of thinking. The issues are never-seen-before and complex; investigating them holistically requires contextualizing them in a broad range of other information from across the enterprise & outside of it. Without Axion, hundreds of engineers try to make sense of these failures manually, wading through millions of messy, disconnected rows of data in excel files. The result is that emerging issues are masked resulting in delayed detection for many months, downtime, safety events, and more.
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Daniel First