AI-powered review of large document sets in M&A, financing, or real estate transactions to identify risks, obligations, and anomalies; AI flags issues, lawyers assess materiality.
Last reviewed: 2026/05/19
Extracting key data points from contract text into structured fields — parties, term, governing law, renewal dates, payment obligations, liability caps; AI compresses this from minutes to seconds per contract.
CapabilityAI-generated numeric or categorical risk scores assigned to contracts based on clause-level analysis and deviation from standard positions, helping prioritize contracts needing lawyer review.
Enterprise AI for portfolio-level contract analysis and institutional memory.
Move from this definition to role-based legal AI shortlists and the selection criteria that matter for each type of legal team.
Am Law 200 and global firm workflows: accuracy at scale, security compliance, and matter-level auditability.
Legal department workflows: contract lifecycle, regulatory tracking, outside counsel management, and risk.
Last reviewed: 2026/05/19. Definitions are written by the LawyerAI Editorial team. Commercial relationships are disclosed and do not determine editorial scores or conclusions. See our Sponsorship & Affiliate Disclosure.
AI-assisted due diligence applies machine learning and large language models to the review of large document sets in the context of M&A transactions, financing deals, real estate acquisitions, and similar processes requiring comprehensive review of a target's contracts, intellectual property, litigation history, regulatory filings, and corporate records. AI tools extract key data points, flag unusual provisions, identify missing standard protections, and surface anomalies across hundreds or thousands of documents. The AI reduces the time required for initial document triage and extraction; lawyers assess the materiality of identified issues and make the substantive judgments about risk.
Due diligence document review is one of the most time- and cost-intensive components of M&A transactions. A mid-market acquisition involving a virtual data room with 5,000 documents can require hundreds of associate hours for comprehensive review. AI tools can compress initial extraction and triage to a fraction of that time.
The efficiency gain is not merely cost reduction. Faster due diligence enables tighter deal timelines, which can be a competitive advantage in auction processes. AI also applies consistent review criteria across the entire document set — something human review teams performing marathon sessions under time pressure do not always achieve.
The critical limitation is hallucination risk on complex factual characterizations. An AI tool that mischaracterizes a material contract provision — summarizing an unlimited liability provision as a standard cap, or missing a change-of-control trigger — creates a risk of relying on inaccurate due diligence findings. Lawyers must verify AI-flagged issues against source documents before including them in due diligence reports.
Luminance applies document-level and portfolio-level analysis in M&A due diligence, extracting defined data points across contract types and surfacing anomalies against market norms — identifying, for example, that a target's license agreements are disproportionately one-sided relative to typical market terms.
Harvey supports large-corpus document analysis, enabling legal teams to query across the full data room document set in natural language — asking which agreements contain change-of-control provisions, or which leases have rent escalation clauses above a defined threshold. Kira is a purpose-built contract analysis tool with strong performance on M&A due diligence document sets.