Darrow AI is a legal intelligence platform that uses AI to identify potential class action lawsuits, mass torts, and large-scale corporate harms by analyzing public data sources. The platform connects plaintiffs’ attorneys with high-value, impactful cases and provides case assessment, risk analysis, and outcome prediction. Also offers ‘Torch’ — an agentic browser extension for legal analysis on any web page (recommended by Reddit user on r/legaltech, Jun 2025). Legal Exposure Management product helps organizations proactively assess their litigation risk. Practice areas span: privacy/data breach, ERISA, securities, antitrust, medical liability, consumer protection, employment, and more. Founded 2020, dual HQ in New York City (276 5th Ave) and Tel Aviv, Israel. Raised $35M Series B (Oct 2023) from Georgian Partners and others — TechCrunch coverage. Forbes feature (Aug 2025): ‘This AI-Fueled Startup Is Helping Attorneys Find New Class Action Lawsuits.’ 10,022 LinkedIn followers, 196 employees. Key leadership: Mathew Keshav Lewis (COO), Barak Rabinowitz, Roy Rubin, Gaby Prechner. Actively hiring (legal intelligence analysts, data science team lead, VP marketing). This is a major, well-funded legal AI company that should be in the rlegaltech1000.
Company Info
- Founded: 2020
- Team size: ~196 employees
- HQ: New York, NY + Tel Aviv, Israel
- Funding: $35M Series B (Oct 2023)
What We Haven’t Verified
This page was assembled from publicly available information. Feature claims and workflow mappings are based on what the vendor and third-party listings publish — not hands-on testing or practitioner feedback.
Workflows
Based on practitioner evidence, Darrow AI is used in these workflows:
What practitioners struggle with
Real frustrations from legal professionals — the problems Darrow AI addresses (or should address). Sourced from practitioner reviews, Reddit threads, and case studies.
Legal research costs $400-600/hour in associate time and takes hours of manual digging — searching Westlaw/Lexis, reading irrelevant results, synthesizing case law. Clients increasingly refuse to pay for research hours on invoices. AI can compress a 4-hour research memo into 20 minutes, but most firms have no approved tool
Plaintiff firm specializing in privacy class actions knows there are thousands of CCPA and BIPA violations happening every day but has no systematic way to find them — the partner reads news articles, monitors regulatory enforcement, and relies on referrals, while a competitor with better intelligence files the case first and gets appointed lead counsel
Claims litigation manager at a P&C insurer has 500 open litigated claims across 30 defense firms but no way to compare which attorneys actually get better outcomes — case cycle times, settlement-to-reserve ratios, and cost per claim vary wildly by firm, and when it's time to assign a new bodily injury case the manager picks a firm based on relationship and gut feel, not data, while nuclear verdicts are rising and C-suite wants accountability for defense spend ROI
Where it fits in your workflow
Before Darrow AI
Plaintiffs' firm seeks new cases → traditionally relies on news monitoring, referral networks, and partner intuition → Darrow AI scans public data to identify patterns of corporate harm (data breaches, regulatory violations, consumer fraud) before they become widely known → connects cases with appropriate attorneys
After Darrow AI
After Darrow identifies potential case → attorney evaluates Darrow's case assessment and predicted value → decides to pursue → Darrow provides ongoing intelligence and analysis through litigation → settlement or trial outcome
Integrations & hand-offs
Darrow AI (case identification + intelligence) → plaintiffs' attorney (case evaluation, client recruitment, filing); → legal research tools (case law, precedent); → eDiscovery (once litigation begins). Torch browser extension provides real-time analysis during research.
Also used by similar teams
Community Data
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