Thomson Reuters launched HighQ Contract Analysis, a contract review and analysis tool that uses machine learning to answer the specific questions legal professionals want to address – in an easy-to-read report.
Contract review is a critical, yet time-consuming part of transaction or contract management. HighQ Contract Analysis uses machine learning and pre-trained models to help attorneys increase efficiency, reduce risk, and accelerate the contract-review process for transaction due diligence, compliance review and contract investigation. Integrating seamlessly with HighQ, the company’s foundational asset in the collaboration and workflow automation market, it extends customers’ transaction workflow with more insights and greater automation.
“AI-powered applications require three key ingredients – data, subject matter expertise and technology – and HighQ Contract Analysis builds upon Thomson Reuters decades-long leadership in AI-driven products for legal professionals,” said Andy Martens, head of Research Products at Thomson Reuters. “HighQ Contract Analysis begins with the deep knowledge of Practical Law editors who use their expertise to develop proprietary contract review templates specific to legal domains, and then leverages the work of AI experts at Thomson Reuters Labs to train and validate its machine-learning models. The result is a highly tailored, guided review that saves our customers’ time and costs, and improves the accuracy and insights of the contract review process.
HighQ Contract Analysis is built around legal domains, beginning with real estate leases and sales and services agreements and soon extending to other areas, including intellectual property agreements and employment agreements. For each domain, Practical Law attorney editors develop a list of key questions reviewers might want to ask in a contract review exercise. For example, the tool can find answers to questions such as, “What are the landlord’s maintenance obligations?” or “Is there a mutual right to break?”
To kick off the review, the HighQ AI Hub ingests the document, classifies the contract, and identifies essential facts like parties, deal value, language and jurisdiction. The new HighQ Contract Analysis pre-trained domain models then automatically extract and retrieve defined terms and definitions from within the agreement, divide the document into text snippets, evaluate every snippet against the review questions, and returns text that meets the criteria relevant to answering each question. Its intuitive Guided Review interface allows users to assess those answers, comment, annotate and assign risks in the document. Users can analyze contracts in bulk as well as review a single document. HighQ Contract Analysis also allows users to compare contracts to an identified company standard or Practical Law standard documents, enabling reviewers to quickly identify non-standard terms, deviations and additional risks.
“A typical use case would be for a buyer assessing a purchase of an office block, based in part on a review of all the contracts associated with the properties being purchased,” said Rawia Ashraf, vice president of Legal Practice and Productivity at Thomson Reuters. “The buyer needs to identify key risks, such as how much income is generated by these properties, what properties are likely to be vacant and who is liable for things such as insurance and repair. This is fast, easy work for HighQ Contract Analysis.”
HighQ Contract Analysis provides an integrated experience, enabling customers to view and edit machine-learning extraction results, annotate documents and collaborate with teams. Users can leverage HighQ collaboration, workflow and visualization tools to conduct further analysis and generate reports on top of the extracted data.
Later this year, HighQ Contract Analysis will release its AI Model Trainer, which will provide an easy-to-use, end-to-end process to manage, re-train and evaluate the machine-learning data models to refine their analysis of a user’s own contracts. Longer term, users will be able to define their own models, managing the questions and facts to match their, and their client’s, expectations.