BenchSci

EMET is built on a neuro-symbolic orchestration architecture that decomposes complex research questions, routes them to domain-specific AI skills, and chains results across 38M+ publications (including 16M closed-access papers) and 1,000+ databases. Its proprietary knowledge graph contains 858M nodes, 2.2B relationship edges, and 100M ontological nodes across 241 edge types. The platform orchestrates frontier LLMs alongside specialized scientific models — ESM-2 (protein language), AbLang2 (antibody language), and RDKit (cheminformatics) — achieving 95%+ accuracy validated across 600+ tests and 8+ benchmarks. It supports integration of proprietary and dark data, customizable agentic workflows, and connectivity to self-driving labs, all within a regulated, enterprise-grade environment.