Using categorical prompts, our LLM stack identifies top global experts via multidimensional signals, e.g. citation metrics, funding, institutional standing, and recency - synthesizing intelligence beyond any single authority.
Longevus loads ~30 high-signal research papers per expert into vectorized memory, powering a multi-stage RAG (i.e., database) pipeline that prioritizes signal density over volume.
Content generation is not left to zero-shot prompts. Outputs are scaffolded via structured prompts and segmented into predefined modules. Safety, efficacy, consensus, and recency act as topic-level control levers.
Last-mile QA revalidates outputs against internal instructions and expert-informed structures - ensuring factual accuracy, semantic integrity, and structural clarity before surfacing.
Final content is refined and authorized by EverMe's Steering Committee of clinicians and researchers - bringing field consensus, methodological rigor, and expert judgment on topic-specific nuances surfaced during content development.
Outputs trained on everything the internet says - fact, fiction, and clickbait all mixed together.
Research-grounded answers, structured by expert-reviewed processes. No filler. Just precision.
Endless pages of links ranked by popularity, ads, and algorithms. You scroll, skim, and still leave unsure.
A curated synthesis of what matters most, scored and structured so you can act with confidence.
Narrow perspectives, fragmented views, and advice that may already be outdated.
An evolving intelligence system that brings together expert consensus, tracks scientific shifts, and puts the power back in your hands.