AI M&A Legal Diligence | Mage

What

Mage is an AI-powered legal diligence platform focused on M&A workflows. It is designed for law firms, private equity teams, and corporate counsel that need to review large data rooms, identify legal risk, and produce deal work product such as diligence memos and disclosure schedules.

The product appears positioned as a legal-native first-pass diligence system rather than a replacement for attorneys. Its workflow centers on classifying and analyzing transaction documents, extracting key provisions, surfacing red and yellow flags, comparing revisions, and helping legal teams move from document review to memo drafting and issue tracking more quickly.

Features

• Auto-generated diligence memos: Produces memorandum drafts with executive summaries, flagged issues, and recommendations, helping teams convert review findings into client-ready output faster.
• Disclosure schedule support: Flags potentially missing or incomplete disclosure items, cross-references against the data room, and supports export for closing preparation.
• Tabular first-pass review: Presents extracted provisions such as liability caps, change-of-control clauses, and termination rights in a grid view so reviewers can scan many documents efficiently.
• Variance detection across agreement sets: Identifies where a contract departs from a standard form, helping teams prioritize non-standard or higher-risk outliers.
• Redline comparison for transaction documents: Highlights material changes across SPAs, merger agreements, and asset purchase documents so edits are easier to spot and assess.
• Due diligence questionnaire generation: Tracks open diligence requests and generates follow-up questions or draft outreach based on gaps identified during review.

Helpful Tips

• Treat AI findings as first-pass output: Mage explicitly positions attorneys as the final reviewers, so legal teams should build review and signoff steps into the diligence process.
• Use structured outputs to triage risk early: Grid views, flagged provisions, and variance analysis are most useful when paired with a clear internal escalation framework for material issues.
• Standard-form comparison is especially valuable: Teams with repeat deal playbooks or template-heavy commercial agreements are likely to benefit more from variance detection than teams with highly bespoke documents.
• Check source-linked extractions during evaluation: Since the site states extractions link back to source text, buyers should validate how easily reviewers can verify AI conclusions in live matters.
• Assess fit by transaction type and volume: The strongest evidence on the page supports M&A diligence and related contract review, while capital markets functionality is described as coming soon.