The speed at which we can develop deep learning solutions to new problems has tremendously accelerated over the years. We were able to develop a proof of concept (and justify the effort) very quickly, with the availability of cloud-based GPU servers, pre-trained models, and open sourced architectures and code. We quickly learned though that getting to a quality that is appropriate for a production level personal health application required a little more ingenuity and trial and error than just simply stringing together open sourced components. This wasn’t surprising to us, given the known complexity of both insurance cards and images taken without forcing formatting constraints. We’re very happy with where we are now, and excited that with more data, the system will literally have superhuman accuracy.