Using RAG is definitely a relief factor after reading that you're using AI and NLP for aggregate analysis, but I'm curious how much manual review this actually saves?

Since the model summaries would still need to be validated against the source results manually, your business' actual viability as a product hinges on whether customers perceive a significant time savings in the data provided via these channels over historical aggregation methods (like keyword analysis that you mentioned) and level of false positives.

What do you measure as the largest impact here? Is there a large time savings, is it additional discovery from blindspots that other methods don't cover? Both? Are there additional benefits you see to this model beyond automation and expanded discovery?