Ravel uses 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 illuminate the relationships embedded in the American legal system making them easier to understand and work with.
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. We view design and engineering as practices grounded in active dialogue.