We believe it is possible to use root cause data collected from our bottom-up checks, coupled with NAV error logs, to deploy systems that can guide analysts through the most critical transactions, fund classes, and areas during the day. This would potentially save time after the market closes and could reduce the actual NAV error rate in the long term. We can show an AI-generated heat map by transaction type and fund class on the more likely errors. We can also show progress and a T minus clock on activities leading to publishing the NAV.