student
Russian Federation
UDC 336.76
UDC 65.012.4
This review quantifies how large language model (LLM) powered document intelligence, including retrieval augmented generation (RAG) and agentic AI, is reshaping buy side M&A due diligence in mid market deals. We conduct a rapid evidence assessment across regulatory and professional surveys, industry case studies, peer reviewed studies, and vendor whitepapers. We focus on measurable indicators: time to review, issue detection quality, and human in the loop controls. Triangulated findings show 70–75% reductions in document review time versus manual baselines. Optimized RAG configurations reach contract analysis accuracy up to 95%, matching or surpassing earlier machine learning tools. Banks, private equity firms, and law firms report faster deal cycles, stronger risk flagging, and maintained or improved quality with appropriate oversight. Humans in the loop remain essential to manage hallucinations and privacy constraints without erasing efficiency gains. The study consolidates metrics from real deployments and highlights governance practices required for adoption in the mid market.
M&A due diligence, large language models, generative AI, document review, retrieval-augmented generation, efficiency, legaltech, mid-market transactions
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