CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases
Testing whether AI can actually handle the mountains of real corporate documents
Researchers created CorporateBench, a large-scale test for AI systems that answer questions about corporate documents, using over 230,000 synthetic but internally consistent papers. When tested on five major language models, performance dropped significantly as document volume grew closer to what companies actually deal with — revealing a gap between how well these systems work in labs and how they'd perform on real corporate communication networks.
Companies increasingly deploy AI to search internal emails, reports, and knowledge bases, but there's been no realistic way to test whether these systems will actually work at scale before rolling them out. CorporateBench gives developers a standardized measure to catch failures before they happen in production, potentially preventing costly mistakes like AI systems giving executives wrong answers about contracts, policy, or business decisions.