In this bylined piece for Artificial Lawyer, Darrow's CEO Evyatar Ben Artzi identifies what he believes is the most underappreciated problem in legal technology today: not how to make existing legal work faster, but how to surface the cases that should exist but never get found in the first place.
Ben Artzi opens by acknowledging that legal technology has made genuine progress. Document review is faster, case management is smoother, research is more efficient. But all of these advances share a common assumption — that someone already knows they've been harmed and has brought that harm to an attorney. The front end of legal practice, the work of actually uncovering violations before anyone asks for help, has remained largely untouched. That is the blind spot.
The traditional reactive model, Ben Artzi argues, made sense given the technological constraints of the past. Attorneys conducted research, but the process started when a client arrived with a problem. The costs of this model are significant: by the time harm reaches a legal desk, it has often been occurring for months or years. Evidence degrades. Memories fade. The optimal window for legal intervention may have already closed. Entire categories of systematic harm — environmental violations, consumer fraud, coordinated corporate misconduct — remain fragmented and hidden in plain sight, never coalescing into cases because no one has connected the dots.
Legal intelligence addresses this directly. At Darrow, Ben Artzi describes scanning vast databases of public information — regulatory filings, consumer complaints, corporate disclosures, court records — using AI to cluster patterns that suggest systematic harm. The result is the ability to identify potential violations months or years earlier than traditional channels would surface them, giving attorneys the opportunity to move from reactive to proactive practice.
The scale challenge is central to his argument. A pharmaceutical company generating thousands of adverse event reports across multiple databases and jurisdictions is a practical example: traditional research might surface a handful of concerning cases while missing the larger pattern entirely. Machine learning can process millions of data points simultaneously, identifying statistical anomalies that would be invisible to human analysis. Ben Artzi cites Darrow's work helping partner firms uncover defective medical devices, securities fraud, and environmental contamination — cases where the legal merit was present but required a combination of intelligence, data analysis, and human judgment to become visible.
He also addresses case economics. One of the most expensive parts of plaintiff practice is upfront case evaluation — weeks of research with no guarantee of return. Legal intelligence front-loads much of that analytical work, allowing attorneys to begin with a full picture of potential harm, estimated class sizes, damage calculations, and comparable precedents before committing significant resources. Lawyers still evaluate law and assess risk, but from a position of far greater information density.
Ben Artzi closes with a clear-eyed view of the human-AI partnership. AI will not replace attorneys — interpretation, judgment, and client advocacy remain irreducibly human. What AI enables is the detection of violations that would otherwise never be found, and the handling of a scale problem that has historically limited the profession's ability to identify and address systematic harm. The firms that embrace these capabilities earliest, he argues, are already seeing the competitive advantage — identifying high-value cases before competitors know they exist.
