You seem to be pretty clued up on the area, what do you see as the pros and cons of an end-to-end approach?
Normally we do this kind of benchmark internally by sending the same dataset to each service and running some stats on the results, but if a vendor comes in with a ready to use comparison report that would be easier sale.
As for end-to-end you would be competing with large internal ML teams and revenue bringing internal ML engines, i'm probably not the right audience for that type of product. Salesforce seems to be doing alright on that front, but from my discussions with them there is a lot of hand-holding and customizations for each client use case, it's a high-touch thing.
One of the main differences is that we've pretty exclusively focussed on language rather than vision which has quite a different tech stack.
We also view human-in-the-loop not just as a way to get better data but actually as a better deployment paradigm.
P.s You're right that David is awesome btw!