Cortical.io announced a new release of its Cortical.io Contract Intelligence software. Utilizing a patented natural language understanding (NLU) approach based on semantic folding theory, the software analyzes the content of large quantities of documents with a degree of accuracy that is difficult to achieve with manual labor or other automation tools. It automatically and accurately searches, extracts, classifies and compares key information from agreements, contracts, and other unstructured documents like policies and financial reports. The Cortical.io Contract Intelligence solution understands the meaning of whole sentences and concepts, instead of just keywords.
“Other vendors offer contract review and analysis software, but are limited to pre-defined contract types. Cortical.io offers the capability to easily customize the extraction and classification to any type of document and to the specific corporate requirements with little training data,” said Cortical.io COO Thomas Reinemer. “Because it is meaning-based, the solution reaches higher levels of accuracy even when extracting whole sentences and paragraphs which makes it very reliable and provides efficiency and savings to our customers.”
Cortical.io’s newest Contract Intelligence version capabilities include: high-fidelity rendering of documents, improved extraction capabilities and advanced search (i.e. the ability to perform range queries that allow you to search for numerical and date ranges). Benefits include:
- Improved operational efficiency and money saved by reducing extraction and review time by up to 80%
- Faster turnaround by reducing extraction and review time
- Higher accuracy that reduces the cost of improperly reviewed documents (for example, coverage gaps in insurance policies, risks of not monitoring important provisions)
Cortical.io Contract Intelligence helps dramatically reduce the time and costs for any organization that needs to review and extract information from a large number of unstructured documents. It also helps reduce human errors inherent to a boring repetitive task and make better use of expensive subject matter experts. This a major boon for markets such as insurance, that review and extract sensitive information from policies and loss run reports on a large scale.