How JudgeLens Works
From your argument to a data-grounded response in each justice's authentic oral argument style — every step transparent.
The Pipeline
Six steps from your argument to a grounded response. Click any step to learn more.
Data Scale
Every number represents real Supreme Court oral argument data.
Model Architecture
Five components working together — no single model does all the work.
nomic-embed-text-v1.5
8,192 token context window. Converts arguments into 768-dimension vectors for semantic search.
ChromaDB
Stores and indexes 5,387 real oral argument exchanges for fast similarity search.
cross-encoder/ms-marco-MiniLM-L-6-v2
Reads each candidate exchange alongside the query to produce accurate relevance scores.
Claude Sonnet
Generates Alito-style responses constrained by verified behavioral patterns and real exchanges.
Oyez Project
16,951 transcript JSON files from Chicago-Kent College of Law. 8,551 unique recordings.
What the Data Reveals: Justice Alito as a Case Study
Patterns discovered by analyzing one justice's 14,691 speaking turns — not assumed, measured. Each of the 9 justices has their own data-driven profile.
of turns start with "Well" — and it never means agreement. It signals a pivot, a challenge, or a reframe.
"Suppose" is used 19.9x more than attorneys do. It's his primary tool for constructing vivid, everyday hypotheticals.
of deep exchanges end with a statement, not a question. He uses questions to build toward a conclusion.
All 9 sitting justices have their own data-driven profiles built from this same analytical approach.
See the Pipeline in Action
Present your own legal argument and watch JudgeLens transform it into a data-grounded response.
Open the Simulator