A keywording tool that guides platform visitors towards the most meaningful content to each individual collectors.
Marketplaces need buyers as well as sellers. Currently, even on gated platforms, there's an overwhelming amount of content to wade through. Song stone uses a ranking system based on users search history. This in turn informs and creates algorithms specific to that individual. The result is more targeted and personalised search results which in turn means a more seamless collector experience and more success for all in terms of purchases, collectors branch out beyond their usual stable of go-to artists, and newer artists get the chance to be seen. Think of Amazon's 'you-might-also-like-feature'. Stone Song is similar, only it's catered for the more subtle art world where one collector loves the deep philosophical nature of a piece through to a collector simply wishing to flip and make money.
How it's made
I've been unable to find a coder - 'Search' is not sexy, but it's certainly necessary. I've therefore created a mock-up using PIC.IO as the platform into which I've 'minted' a number of sample 'NFTs'. These have then been manually tagged (I would use AI once coded). Tags include the obvious - literal tagging, but I have also added manual 'code' tags that would be determined by the machine learning algorithms.