Legal Services — Securities Litigation

Levi & Korsinsky — AI-Powered Securities Litigation Research

Levi & Korsinsky — AI-Powered Securities Litigation Research

55%

55%

Reduction in precedent research time

0

0

Missed or overturned controlling cases in reviewed briefs

About

Levi & Korsinsky's securities litigation team was losing days to manual precedent checks under tight filing deadlines, with associates combing case law by hand and risking missed or overturned precedents. We built a semantic legal-research assistant over case law and SEC filings that returns cited, Bluebook-ready findings in minutes instead of days.

Industry

Legal Services — Securities Litigation

Company size

50 – 200 employees

Founded

2003

The Company

A securities litigation practice built on precedent and speed

Levi & Korsinsky is a plaintiffs' securities litigation firm representing defrauded investors in class actions and shareholder recoveries nationwide, with a docket that regularly includes tight filing deadlines against opposition motions to dismiss.

In securities litigation, missing a controlling precedent — or citing one that has since been overturned — can materially weaken a filing. Associates spent significant time on manual case-law search to guard against exactly that risk.

The challenge

Two days of manual search for every opposition brief

A single opposition brief could require two full days of Westlaw research to confirm the relevant precedent set was current, correctly characterised, and comprehensive — time that came directly out of the window available for building the substantive argument itself.

The stakes of getting it wrong were high: an overturned or superseded precedent cited without the team catching it could undermine a filing's credibility with the court, and the manual search process offered no systematic way to guarantee that risk was fully covered before a deadline.

The Solution

Semantic search across case law and SEC filings, Bluebook-ready

We built a GPT-4 semantic search layer over roughly 12 GB of securities case law, combined with a QLoRA-tuned MiniLM retrieval model for direct natural-language querying of SEC EDGAR filings — 10-Ks, 10-Qs, 8-Ks, and proxy statements — so associates could ask plain-English questions across both case precedent and underlying filing evidence in the same workflow.

The system flags citation risk on any precedent that has been narrowed, distinguished, or overturned since it was decided, and exports findings directly into a Bluebook-formatted memo ready to drop into the drafting environment, with query results cached in DuckDB to support the audit trail the firm's compliance process requires.

The Results

55% faster research with zero missed controlling cases

Precedent research time fell 55%, and the associate team has not missed a controlling case in any brief reviewed since the system's rollout. SEC filing research — previously constrained by EDGAR's disclosure-first interface — now runs 3.4x faster through the semantic query layer.

On at least one major opposition brief, the accelerated research turnaround meant the filing went out with time to spare before the deadline rather than against it, and the brief was accepted without challenge on its precedent basis.

professional portrait

The tool didn't just save us time — it removed a category of risk. We no longer have to wonder whether a citation check was thorough enough under deadline pressure.

Litigation Partner, Levi & Korsinsky

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