Ravel uses its deep expertise in computational linguistics, language analysis, and annotation to extract the legal knowledge graph from unstructured data and understand the letter and spirit of United States caselaw.
We organize, summarize, and mine United States caselaw using modern machine learning techniques.
The Ravel engineering team uses Scala, Spark, MongoDB, on SolrCloud on AWS to analyze the law and deploy our responsive API. We are active in related open source communities.
We use Ember.js and D3 to build interactive data visualizations.
With roots in Stanford's d.school, Ravel's collaborative processes bring together lawyers, designers and engineers.
Our approach to product development is iterative and agile. Our teams work closely with our customers and participants in our Ravel Labs program.