LexisNexis Context
Transform legal strategy with high-velocity language analytics and judicial intelligence.
Turning legal data into actionable litigation intelligence for predictive legal strategy.
Loom Analytics is a sophisticated legal research and data visualization platform designed to convert unstructured court decisions into structured, actionable intelligence. By 2026, the platform has matured its NLP (Natural Language Processing) engine to ingest multi-jurisdictional case law, specifically focusing on the Canadian legal market, including Superior Courts and specialized tribunals. The technical architecture revolves around a proprietary taxonomy that classifies case outcomes, motion success rates, and judicial tendencies with high granularity. Unlike traditional legal databases that focus solely on text retrieval, Loom Analytics emphasizes quantitative analysis—enabling law firms to calculate the statistical probability of specific outcomes based on the judge, the opposing counsel, and the legal matter type. Its 2026 market position is defined by its role as a critical 'Strategic Intelligence Layer' that sits atop primary legal research tools, providing the empirical evidence needed for client reporting and litigation budgeting. The system utilizes machine learning to normalize legal entities and interpret the sentiment and finality of complex rulings, significantly reducing the manual labor required for case law quantification.
Aggregates historical rulings to visualize how specific judges lean on specific motions (e.g., Summary Judgment).
Transform legal strategy with high-velocity language analytics and judicial intelligence.
Transforming judicial data into strategic intelligence through predictive AI and legal analytics.
Mapping the legal genome for high-precision litigation research and brief analysis.
Enterprise-grade legal management driven by AI-powered analytics and e-billing optimization.
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Uses NLP to extract dollar values awarded in personal injury and civil litigation cases.
Cross-references law firms and individual lawyers against case outcomes and motion success.
Breaks down case history into individual procedural motions rather than just final judgments.
Clusters variations of law firm names and corporate parties to ensure clean statistical data.
Multivariate filtering including judge, counsel, area of law, and date range.
AI model that estimates case success based on similar fact patterns in the database.
A client needs to know the statistical likelihood of winning a summary judgment motion in Ontario.
Registry Updated:2/7/2026
Export data for client presentation.
An insurance company needs to set an accurate reserve for a motor vehicle accident claim.
A corporation needs to choose a firm with the best track record in environmental law.