Legal tech company Litera has launched a new product that leverages generative artificial intelligence to construct corporate deal term databases from a law firm’s existing documents. The Foundation Dragon gives legal teams quick access to vital data points from past transactions.
Structured as a searchable collection, Foundation Dragon can extract around 300 pertinent data points from unstructured information in conclusion documents across mergers & acquisitions (M&A), real estate, finance, and similar deal types. These information details incorporate every matter, deal, and negotiation data.
Two principal use cases are foreseen for law firms when using Foundation Dragon. In deal negotiations and in business development, legal professionals can extract relevant market terms and previous examples. If a client is seeking experience in a specific asset transaction type within a particular jurisdiction, Foundation Dragon can be used to acquire that information and to market their services more proficiently.
Another possible application suggested by Adam Ryan, Litera’s head of product, is to support thought leadership by helping professionals to detect M&A trends and issues. As per Ryan, by utilising AI to execute this weighty manual task, Litera is uniting two valuable data sets — deal terms and experience — and making this data accessible to more firms than ever before.
Foundation Dragon was developed in collaboration with several law firms that have long been Foundation customers, including Frost Brown Todd LLP and Goodwin. The idea is to identify the key transaction points for Foundation Dragon to extract. Adam Ryan states Dragon surpasses usual manual-entry deal term databases by dissecting and fusing a firm’s deal intelligence with experience data via Litera Foundation, improving accuracy and productivity over manual review and data entry by attorneys.
Its functionality consists of two components, uploading deal documents and subsequently exploring and searching the accumulated deal data. When a firm uploads a document, a type of deal and the client number from the firm’s practice management system are selected. The AI then extracts key deal points including those identified by the American Bar Association (ABA) in its M&A deal points study.
Ultimately, Litera’s product aims to convert firms’ collective experiences into quantifiable insights, minimize the cost and effort of maintaining a deal point database, negotiate more strategically by leveraging historical data, and make it easier for lawyers to find deal precedents comparable to current deals, ensuring that the data is always up to date.
References: LawNext