LinkedIn · Jul 2026
Raffi Isanians says Mage ran Harvey's open-source legal benchmark scenario for M&A diligence and reports 90.7% vs 11.0% on the benchmark, plus full-document coverage and cited outputs.
Public company, workplace, funding, and market signals
Updated Jul 30, 2026
Mage Legal (Entori, Inc.) is a San Francisco-based YC S24 legal AI startup building AI-powered M&A legal diligence software that ingests data rooms, flags issues, drafts diligence memos and disclosure schedules, and compares redlines for attorneys.
Primary product
AI M&A legal diligence platform
Founded
2024
Headquarters
San Francisco, California, United States
Team size
1-10 employees
Work style
Onsite
Industry
Technology, Information and Internet
Sub-industry
AI legal diligence / legal tech
Offices
0 jobs at Mage Legal
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Business model
Investors
Precision-first, infrastructure-heavy legal AI culture: the team writes about extraction, evals, legal-native workflows, citations, and security rather than generic chat-based prompting.
Work style
Onsite
Visa sponsorship
Limited
Compensation
Public job posts show founding roles at roughly $80K-$150K base plus equity, with in-person San Francisco roles and postings that say 'US citizen/visa only'.
Pricing
Contact sales / request demo
Differentiators
Technology
Customers
Competitors
LinkedIn · Jul 2026
Raffi Isanians says Mage ran Harvey's open-source legal benchmark scenario for M&A diligence and reports 90.7% vs 11.0% on the benchmark, plus full-document coverage and cited outputs.
LinkedIn · Jul 2026
Mage announced a data-room product that organizes documents automatically, surfaces missing files, and shares them through an NDA-gated link with engagement analytics.
LinkedIn · Apr 2026
Raffi described multi-step transactional workflows including finding deal-breaking issues, generating diligence memos, building disclosure schedules, and flagging consent/assignment issues.
Mage blog · Feb 2026
Mage argues that purpose-built M&A diligence infrastructure beats generic legal AI for structured extraction, variance detection, disclosure schedule mapping, and multi-model consensus.
Mage blog · Aug 2025
A case study on capital-markets diligence showing automated review of risk factors across comparable offerings and linking findings to the underlying documents.