For decades, plaintiff-side litigation has operated on a reactive model — a breaking news story, a walk-in client, a referral from a peer. Even the most sophisticated plaintiff firms have had limited tools to detect wrongdoing early enough, or at a scale large enough, to shape the next wave of high-value matters. This deep-dive feature in Above the Law, produced in partnership with Darrow, examines how that is changing — and walks through the platform in detail based on a demonstration from Darrow's Etia Rottman Frand and Mathew Keshav Lewis.
Darrow's mission is to provide an awareness layer that brings clarity, consistency, and transparency to complex legal and technical environments. The company maintains a 160-person team across engineering, data science, and legal research, and has surfaced insights connecting to more than $22 billion in potential legal opportunities — including early intelligence related to the issue that preceded a $40 million resolution involving Bumble's parent company.
The platform operates across three connected stages. The first is Detect. Darrow continuously analyzes billions of data points drawn from federal and state regulatory databases, government health and safety data, court filings, corporate and industry datasets, digital systems behavior including web and app data flows, trade publications, and open-source online discussions. By unifying these signals, Darrow creates an early, high-clarity view of meaningful patterns across a wide range of practice areas. This is not about assuming wrongdoing — it is about giving firms data-backed awareness of areas that may warrant deeper investigation, long before a complaint is filed or a client walks in.
The second stage is Evaluate. Once a signal surfaces, Darrow's attorneys, researchers, and technical specialists work together to interpret what the data actually means. This includes predictive underwriting — building case-level financial forecasts grounded in evidence, comparable matters, and litigation trends — as well as legal and technical review to determine whether an observed pattern aligns with relevant statutes, regulatory frameworks, and precedent. In practice areas involving medical products, environmental data, or digital systems, Darrow provides the scientific and technical context needed to understand why a pattern is emerging and whether it meets necessary thresholds. Darrow's integrated AI assistant, Torch, supports this work by summarizing content, explaining terminology, surfacing related filings, and helping teams navigate complex supporting data. The output is a complete, comprehensible view of what the data suggests — giving firms an informed foundation from which to make strategic decisions.
The third stage is Advance. Whether a matter was surfaced by Darrow's intelligence or originated through the firm itself, Darrow supports ongoing case development by identifying and qualifying potential representative individuals, enriching relevant datasets, validating or challenging new information as it surfaces, and structuring the evidentiary foundation that allows legal teams to focus on strategy rather than discovery. This includes analyzing large-scale trends, cohorts, and population-level outcomes to determine whether observed events are isolated incidents or part of a broader, legally meaningful pattern.
The article closes with a portrait of the practice areas where Darrow's intelligence is most active — those that demand both scale and depth, where underlying information is vast, technical, and fragmented across dozens of public sources. For plaintiff firms looking to move from a reactive to a proactive model, the piece offers a detailed and concrete picture of what that shift looks like in practice, and why the firms that make it earliest are likely to be best positioned for the litigation landscape ahead.
