Darrow raises $35M for an AI that parses public documents for class action lawsuit potential
Darrow, a legal tech startup founded in 2020, built an AI-based data engine that crawls thousands of sources of publicly available information (newsfeeds, social media, consumer complaints, regulatory filings, SEC filings, environmental reports, court dockets) to identify class-action litigation potential in areas such as data privacy violations, environmental contamination, and discrimination, and to predict likely legal outcomes and case value. A team of legal data specialists, including former lawyers, reviews the AI-surfaced insights before lawyers use the platform for case review and discovery. Around 50 law firms, covering hundreds of lawyers, were using the product, and active cases stemming from Darrow's data insights totaled around $10 billion in claims at the time of the funding round.
Overview
Darrow, a legal tech startup founded in 2020, built an AI-based data engine that crawls thousands of sources of publicly available information (newsfeeds, social media, consumer complaints, regulatory filings, SEC filings, environmental reports, court dockets) to identify class-action litigation potential in areas such as data privacy violations, environmental contamination, and discrimination, and to predict likely legal outcomes and case value. A team of legal data specialists, including former lawyers, reviews the AI-surfaced insights before lawyers use the platform for case review and discovery. Around 50 law firms, covering hundreds of lawyers, were using the product, and active cases stemming from Darrow's data insights totaled around $10 billion in claims at the time of the funding round.
This entry has 10 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Class action suits are often perceived as too pricey and risky for law firms to take on, and it's often only the biggest firms that have teams to proactively seek out interesting cases and the resources to fight them, leaving smaller players and less prominent violations on the sidelines.
The solution
Darrow built an AI-based "justice intelligence" data engine that crawls thousands of sources of publicly available information -- newsfeeds, social media, consumer complaints filed with regulators, administrative reports, SEC filings, environmental reports and court dockets -- to identify class-action litigation potential in areas such as data privacy violations, environmental contamination and discrimination, and to predict likely legal outcomes and case value. A team of legal data specialists, including former lawyers, reviews the AI-surfaced insights before lawyers use the platform for case review and discovery.
Reported business value
Around 50 law firms, covering hundreds of lawyers, were using the product, and active cases stemming from Darrow's data insights totaled around $10 billion in claims at the time of the $35 million Series B funding round.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other legal entries in the register.
Daxte Builds LegalMap, a Generative AI Search Tool for French Legal Documents, on AWS
French startup Daxte built LegalMap, one of the first French generative AI solutions for legal documents, on AWS. The company fine-tuned open-source LLMs such as Llama2 7b on Amazon SageMaker, used Amazon Textract to digitize 30 years' worth of legal documents, and changed names, addresses and other sensitive information to remain GDPR compliant. As of December 2023 LegalMap had a dataset of nearly 125 million documents, and in 2023 Daxte worked with Data Reply France to optimize training costs by migrating to AWS Trainium and AWS Inferentia.
Delivering agentic AI to law firms globally using AWS with stp.one
stp.one, a Karlsruhe, Germany-based legal tech provider founded in 1993, built Legal Twin, an AI-driven platform integrated with its document management system STP Documents, using Amazon Bedrock (including Anthropic's Claude and the Titan embedding model) and Amazon Aurora PostgreSQL. Legal Twin offers six AI-powered capabilities including intelligent contract risk analysis and automated receivables processing, with responses grounded in case documents and court decisions and citations shown so lawyers can verify sources. It supports Model Context Protocol integration and lets clients with data sovereignty requirements store and process data locally, connecting on-premises storage with cloud-based processing. Certain claims that traditionally took 6-10 minutes to file can now be processed in under 30 seconds using Legal Twin, and productivity gains for users reach up to 3x at the upper end. The platform must comply with GDPR and Germany's federal code for lawyers.
stp.one· GermanyAmazon Bedrock · Anthropic Claude · Amazon Titan embedding model +2
IDEXX Laboratories cuts contract sanctions review from weeks to 20 minutes with Luminance AI
IDEXX Laboratories used the AI-powered contract automation platform Luminance to review its entire contract database for connections to sanctioned countries and entities following Russia's invasion of Ukraine, completing in 20 minutes a review that would typically have taken weeks manually. Separately, Koch Industries' in-house legal team uses Luminance Corporate to automate generation of NDAs, master service agreements and IT-related contracts, including self-service contract generation for non-legal business units, and Deloitte used Luminance on a project standardizing 4,500 contracts across 14 European entities for client BT in two weeks, estimating a 50% time savings versus manual review.
IDEXX LaboratoriesLuminance
Spellbook runs 530,000 contract reviews a month with Claude
Spellbook, a contract drafting and review platform serving 5,000 customers across 80 countries including in-house legal teams at companies like LG, Dropbox and eBay, built Claude-powered agents that review and redline contracts inside Microsoft Word, apply each legal team's own review standards automatically, and orchestrate about 15 different Claude agent configurations matched to task complexity. Spellbook's agents now run about 530,000 contract reviews a month and field more than 700,000 lawyer chat messages a month, cutting the time to complete an agreement roughly 10x, from about 10 hours of lawyer work to about one hour.
SpellbookClaude Platform · Claude CodeWas this helpful?
Your feedback helps us improve our use case database