Digital Reasoning

Digital Reasoning is a leader in Cognitive Computing for the Enterprise.

Founded on the vision that someday all software will learn, Digital Reasoning is leading the next generation of research and development in cognitive computing. With an experienced management team that includes former executives from Oracle, Computer Associates, MarkLogic, OpenText, LexisNexis and the CIA, the company currently employs 70 people across offices in Washington D.C., New York, London and Nashville. Digital Reasoning, backed by In-Q-Tel, Silver Lake, and the Partnership Fund for New York City, is home to six PhD’s as well as published author and CTO Matthew Russell, and boasts an impressive list of patents (5 granted; 6 pending). Digital Reasoning was recently recognized by Deloitte’s Fast 500, CRN’s Big Data 100 and named Gartner Cool Vendor for Smart Machines. Digital Reasoning has spent over a decade developing and enhancing its advanced analytics solutions, resulting in deployments at US Intelligence and DoD agencies and more recently at global financial institutions. Its flagship product Synthesys is an open, java-based software platform for analyzing structured and unstructured data. Synthesys uses Natural Language Processing (NLP) technologies and Machine Learning techniques to understand human communication by analyzing entities and relationships in context, within vast amounts of data — revealing what’s most critical to our clients. This extracted knowledge is accessible through a robust Application Programming Interface (API). Digital Reasoning has pioneered work in detecting various behavioral patterns in electronic communications indicative of suspicious and unlawful activity. By analyzing millions of emails, chat logs and transcribed phone conversations, Synthesys can automatically identify specific sections and sentences of these documents that pose a risk or concern. Once flagged for further review by an analyst, a qualification workflow is triggered that ultimately results in formal investigations being opened against a person of interest. Synthesys is able to learn from analysts’ decisions on what constitutes good and bad examples to improve its predictions on new data moving forward. As such, Synthesys gets smarter over time and more effective at uncovering the patterns and behaviors that matter most.