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The most obvious solution here was to rewrite each of these backend C# systems as Unreal C++ code. This would be an incredibly risky undertaking. There were hundreds of backend APIs that needed to be converted like this. Furthermore, each of these APIs relied on complex interlocking logic systems powered by the aforementioned custom conditional language. The C++ code would also need to be able to parse and understand this language to support all the existing content. Without our established C# test suite, it would be extremely tricky to pin down functionality and make sure every edge case was accounted for. Was this even possible in just 6 months?
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Once you've identified target queries, the automated system tests them periodically—daily, weekly, or on whatever schedule makes sense for your monitoring needs. Each test queries the AI model with your specified prompt, captures the response, parses which sources were cited, and records whether your content appeared. Over time, this builds a database showing your visibility trends, how often competitors appear for the same queries, and which topics you're gaining or losing ground on.
quality of the generated content may vary depending on the data source
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Why the FT?See why over a million readers pay to read the Financial Times.