Your documents contain a treasure trove of information. Our technology can help you extract, understand and use it.
More than machine learning
Our technology is built around state-of-the-art machine learning models, including neural networks, Bayesian analysis, and bespoke algorithms. Our machine learning models have been built with the benefit of our long experience with complex documentation as lawyers.
But machine learning is not a miracle cure-all. We also provide more: workflows designed to efficiently guide users through the often unavoidable manual element.
More accurate and complete data
Our technology is not designed simply to add automation to the extraction of data. Our technology is designed to do more: to help you extract the most accurate, complete and actionable data possible. We use multiple layers of algorithms that learn from each other to sift, refine and ultimately get far beyond mere text-tagging all the way down to granular, consistently formulated data -- regardless of how the original text is formulated.
Time and again, data extraction projects fail after considerable investment because requirements are too dynamic for rigid software and related processes to handle. Setting out every item of data you might need in advance is often impossible, even if you invest considerable resources in trying.
That's why we again provide more: a fully flexible system, allowing you to update data needs at any time and extract new data quickly, often within minutes. Our technology was built from the ground up with inevitable retread exercises in mind. They've never been easier.
Tag provisions or other text automatically using a mixture of state-of-the-art machine learning and determinative logic
Extract granular, actionable data from documents, attached directly to the relevant text, using dynamic algorithms
Create spreadsheet reports and charts with comprehensive and easy-to-use tools showing real-time results
Analyse text and data directly within the user interface or using our simple but powerful REST API