By Aprajita Tyagi, L.L.M. Berkeley Law


Artificial Intelligence (AI) is revolutionizing various fields, and the law is one of them. From a distant dream of a first-year associate who manually reviewed vast piles of documents, to an affordable everyday business solution, AI tools have evolved to become a ubiquitous solution, enabling faster, cost-effective delivery of legal services. AI solutions are being deployed by Big law, mid & small-sized firms, in-house teams as well as individual practitioners. Some tools are aimed at specialized areas of practice, like Lecorpio (Anaqua) (for IP management), whereas others, like Catalyst (used to redact information in documents), are not specific to any area of practice. Some of these solutions offer an affordable price range for mid-size law firms/individual practitioners (like CaseText, at $65/month, which is an alternative to the popular research platforms like Lexis and Westlaw).

These AI tools are primarily used for due diligence, prediction, analysis, and Intellectual Property Management. Some popular tools that you may explore include:


Due diligence:  AI tools that  can help law firms save time and money with due diligence:  

  • LawGeex- Automates the contract review process to accelerate deal closures and future-proof contracts.
  • LegalMation- Produces responsive pleadings, discovery requests, discovery responses, and other related documents. These documents are tailored in line with the uploaded claims, allegations, and requests, and incorporate jurisdictional requirements LegalMation is currently offering free usage of their products to help the legal community during COVID. 
  • ThoughtRiver- Their contract review technology speeds up the contracting process by pre-screening the contract, answering key legal questions, and then serving up detailed advice guiding users through remediation.
  • Kira Systems- They use a machine learning software that identifies, extracts, and analyzes text in your contracts and other documents.
  • Everlaw- Streamlines every step of the e-discovery process, from data upload and processing all the way through search, review, and production.
  • DISCO- Can be used as a large volume discovery tool.
  • Exterro- Combines e-discovery and project management capabilities allowing collaboration with project members on one platform.
  • RAVN- Enables M&A due diligence by automating the review process and extracting data from cluster sets.


Prediction- AI tools that are being used to make  legal predictions:

  • Intraspexion –A patented software system that claims to present early warning signs to lawyers when the AI tool detects threats of litigation. The AI assess text, including text in emails and text related to images, to discover these warning signs.
  • Ravel Law- Identifies potential outcomes based on relevant case law, prior judicial rulings, and referenced language from more than 400 courts. This tool can also be used for gathering intelligence on the opposing counsel, probability of winning, and for identifying litigation trends.


Analytics- AI tools leveraging data points to support arguments:

  • PerfectNDA-Analyzes a user scenario to offer prefilled NDA templates, shortening the timeline, and doing away with the need to tailor-make drafts.


Intellectual property management- Managing IP strategy can be a cumbersome task for any organization. Trademark and Patent search are particularly challenging given the time limitations involved in the filing process and volume of data that needs to be analyzed. Various tools are helping lawyers and legal teams with the research process. Some popular tools include:

  • TradeMarkNow- Claims to reduce the time period for patents, registered products, and trademark searches from over 7-8 days to less than 15 seconds.
  • ANAQUA- Helps in drafting patents and prosecution. This tool can detect errors in a document, circular claim references, and formatting defects. It can also automatically generate claims support.

Each category presents unique challenges both for platform developers and users. For example, any tool which analyzes data to make a prediction requires large volume of data. This can be challenging for a mid-sized or small practice. Further, AI algorithms are prone to bias , which is entangled with the data used to train them. This is a crucial thing to bear in mind, given the legal requirements under some regulations which require algorithmic transparency. For example, the GDPR has specific provisions on automated individual decision-making (making a decision solely by automated means without any human involvement) and profiling (automated processing of personal data to evaluate individuals). Another issue with the usage of AI tools is protecting clients against the biased results of opposition research since it is rather difficult to prove such algorithmic bias. However, even though these tools present unique challenges and limitations, their acceptance and role within the legal community are on the rise. The next step is for the legal community to coordinate with developers to create more efficient tailor-made solutions.

Posted On: Aug 17, 2020

Tags: Artificial Intelligence