A research system called Evergreen targets one of the quieter failure modes of AI-powered databases: summaries that sound right but aren't.
When query engines use large language models to collapse rows of data into a natural-language summary — say, "most customers complained about wait times" — those summaries can include claims that the underlying data never actually supports. Verifying them is harder than it sounds: the claims often span comparisons and groupings across datasets too large to fit in any model's context window. Evergreen reframes the problem as a database query, compiling each claim into a verification query that runs on the same engine that produced the summary in the first place. It then cuts costs through a stack of optimizations — early stopping when a verdict is already clear, relevance sorting, prompt caching, and operator fusion — and attaches citations so every verdict points to the specific rows that justify it.
The results are worth paying attention to. Using a strong model, Evergreen hit an F1 score of 0.94 while costing 3.1 times less than a naive verification pass. With a weaker, cheaper model, it still beat the best external baseline on accuracy (0.87 versus 0.83) at 7.0 times lower cost. That cost-quality combination is the real headline: it suggests that verification does not have to be a luxury reserved for high-budget pipelines.
Semantic aggregation is still a niche capability, but it is spreading fast as vector databases and LLM-backed query layers mature. If AI summaries of structured data become a standard enterprise feature — and vendors are clearly betting they will — the question of whether those summaries are actually true moves from academic curiosity to production necessity.