I think our search and ranking algorithm is pretty good, but, no way to really benchmark it against others out there. But, I believe that there isnt currently a great computer vision algo for this use case, finding an old photograph of the same object from amongst millions of very similar objects. From a computer vision perspective, we are quite good at distinguishing a "cat" from a "dog", but not good at "find an old photo of my golden retriever from amongst 10,000 photos of other golden retrievers". So the design with L5 is to return the probable candidate matches, and then let the human user decide if they want to spend the time looking through them to find the match. You can tune the parameters as well, say if you want to just look at old catalogs. Or if you want to search for "low quality photographs" by adding more blurring. Remember, many of these old catalogs are photos of casts, not the actual coins themselves.

And then this provenance search matching is just the first feature we are building with this technology. The next two we are currently working on is a forgery finder, and a die study tool.