L'AILA | Tech Mahindra

Streamlining Legal Reviews

Tech Mahindra’s legal artificial assistant, L’AILA, is a natural language processing (NLP) based platform powered by artificial intelligence (AI) that can extract critical clauses from contract documents and perform subsequent actions. A legal team can spend around 65% of their time reviewing document and extracting critical insights from it. L’AILA helps cut that time significantly.

Solution Overview

L’AILA automatically reviews the content of contracts and extracts important clauses and insights from them. The solution also assigns the content subtexts from the document automatically to designated stakeholders for further processing. L’AILA provides legal users with an easy and standard solution to simplify their hectic schedule of manual tasks. Each AI functionality is deployed as a microservice which helps in easy integration with any contract lifecycle management platform.

Features of the Solution

Document Classification

Document classification will identify the type of contract that is uploaded on the platform. There are multiple types of contracts like MSA, NDA, lease agreement, vendor agreement, MOU, etc. This feature will identify the type of contract that was received by the legal user.

Document Similarity

Document similarity helps in identifying the existence of duplicate contract documents already present in the repository.

Multiple Levels of Content Classification

Multiple levels of content classification for identifying important clauses (payment term, IPR, termination, limitation of liability, etc.). Key information like payment period, mode of payment, currency of payment, and invoice period are also extracted from the contract.

Identifying Stakeholders

Identifying stakeholders (tax, finance, insurance, delivery departments) of the organization who will review document content to ensure compliance with organizational standards.

Active Learning

Active learning from the feedback of users features captures responses from users for wrongly predicted data and then records this in a backend database, which is further used for retraining the NLP models to improve accuracy.

Performance Metrics

Performance metrics to understand the performance of the application. This includes accuracy metrics, operation metrics, and prediction metrics.

Data Distribution

Data distribution details for understanding the drift in data used for training the models. This feature provides an option for users to add/modify the data that will be used for training.

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