Very cool! I am curious about a few aspects:

How do you handle the scenario where errors may exhibit evolving characteristics over time, which might potentially impact the effectiveness of the current embedding model? Is there a mechanism for ongoing model adaptation to ensure sustained accuracy and relevance?

Lastly, could you envision a scenario where this technology is extended to not only classify, but also to predict potential errors based on historical and real-time data?