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Why did you build railtown.ai?

As software engineers ourselves, we know how critical it is to know about a new error before it makes its way to production and is discovered by your customers. Not all errors should be treated equally. Some errors are critical and should be addressed immediately, and others can wait. What we have learned along the way is that first-time errors are business-critical. To quickly jump right in and have all the error context at your fingertips is something that can help provide excellent customer service and user experience. Quickly sifting through thousands of errors to identify patterns, our AI and machine learning algorithms are built to make the root cause analysis a breeze without the need to write complicated queries.

A significant chunk of developers’ time (up to 40%) is spent gathering context for an error, not resolving it. Having all errors being bucketed (grouped) saves developers and their superiors a lot of time and frustration.

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