The Universal Rating Index is a multi-asset rating and scoring technology platform that uses machine learning, big data and human expertise to deliver contextualized index suites that can be formally benchmarked. Each index constructed as a composite of several assets with the option to integrate custom datasets to construct entirely new indices.
For a rating index to be valuable and useful, it must accurately and impartially capture the relationship between objective factors and subjective expectations in a realistic way.
Through AI-driven sentiment and semantic analysis and information arbitrage we synthesize data, reviews, research, and expert knowledge from disparate perspectives and weave it all together to produce contextualized rating indices.
Craig Silverstein, who was the first person employed by Larry Page and Sergey Brin at Google as its Director of Technology, famously said:
“… My guess is that it will be 200 to 300 years until computers and algorithm are as good as, say, a human librarian. The big difference is that the human librarian will understand emotions and other nonfactual information that even a fully intelligent computer may have trouble with…But we can make slow and steady progress, and maybe one day we’ll get there…”