Imagine the following scenario: Numerous residents of a small Illinois town begin experiencing headaches, nausea, rashes, and even tumors. Some write about their symptoms on social media in online support groups, others file complaints in consumer protection databases, and a few stories even make it onto the local news. At first, the symptoms appear unrelated. But when pieced together, it becomes clear that they’re related: Residents are all being affected by toxic waste emissions from a nearby manufacturing plant.
Yet no one is making this connection.
Traditional legal processes rely on an individual reaching out for help. While there’s nothing inherently wrong with this, the process is slow, reactive and excludes most of the people affected by unseen violations. What’s more, many violations never even reach this stage. Evidence for violations remains buried in disconnected data sources, unnoticed by the people harmed and undetected by legal teams. Without upstream tools, wrongdoing can remain legally invisible.
This is what’s missing in today’s legal system: investigative technology that allows attorneys to be proactive in their efforts to uncover violations and evaluate their worth long before litigation begins.
This is where legal intelligence comes in.
What is legal intelligence?
Legal intelligence is the structured process of using AI, data analytics, web intelligence and legal expertise to uncover legal violations hidden in public data. At Darrow, I lead a team of analysts, technologists and lawyers responsible for putting this process into action.
We create and deploy technological assets that process large volumes of publicly available data points. These assets combine automated data extraction with legal analysis to uncover violations in areas such as environmental law, data privacy, antitrust, product liability, amongst others.
The goal of legal intelligence is to move the legal system upstream, so attorneys can act before harm spreads.
Legal intelligence vs. traditional legal research
I’ve been asked whether legal intelligence is just another version of legal research. It’s not.
Legal research helps lawyers interpret the law. Legal intelligence helps lawyers discover the need for legal intervention in the first place.
Research begins with a known issue and seeks to clarify it. Legal intelligence begins with ambiguity and seeks to identify a problem worth pursuing. The process is more akin to that of a journalist or an analyst than to that of a litigator.
This distinction is important. Legal research helps you understand the law, but legal intelligence helps you decide where to apply it.
Detecting legal violations at scale
Detecting early-stage legal violations is difficult in itself. Evidence is often hidden, buried in dense disclosures and public databases, with disconnected data that’s further obscured by sheer volume.
To clarify and create order from this scattered data, my team follows a five-stage process: asset development, data collection, pattern analysis, legal validation and operational planning. The process mirrors traditional investigative disciplines, borrowing from models used in national security and law enforcement. But the output is entirely legal: evidence that can be used to build strong legal cases.
Carrying out this process requires three core capabilities:
- Cross-source data aggregation: Signals are scattered across consumer reviews, environmental reports, financial filings, complaint databases and more. No single source tells the full story. The legal intelligence process involves collecting and normalizing data from multiple domains to detect anomalies that span across formats.
- Pattern recognition grounded in legal context: Not every signal is a violation. We must distinguish between patterns that are merely interesting and those that meet legal thresholds for action. This requires applying legal expertise to contextualize data patterns, a task that most AI tools are not equipped to perform.
- Structured, actionable outputs: Detection is only useful if it leads to action. Legal teams must translate raw signals into fully developed case assessments that include factual background, jurisdictional strategy, class size estimates, and potential damages. This ensures that attorneys can move quickly once a violation is confirmed.
The role of AI (and its limitations)
AI certainly plays an important role in the legal intelligence process. Large language models sift through thousands of complaints, extract structured information and identify recurring fact patterns. Clustering algorithms can detect statistical anomalies that point to systemic harm. Generative AI can summarize findings and draft preliminary reports.
But technology alone is not enough.
Every detection must be assessed for legal significance. This requires experienced attorneys who can apply precedent, interpret regulatory frameworks, and determine whether a case meets merit standards. Every case that moves forward at Darrow is first reviewed and validated by our Legal Intelligence team.
The goal is to combine machine-scale pattern recognition with human legal judgment so the legal system can respond to risk with speed and accuracy.
Building an investigative legal culture
The future of legal work depends on our ability to detect wrongdoing early and respond before the damage becomes widespread. Legal intelligence offers a model for this upstream practice. It invites attorneys to adopt a more investigative posture and to think beyond traditional case sourcing tactics.
Justice should not rely on chance discovery, but rather begin with intelligent and data-driven violation detection.