Litex
1st LegalTech start-up leveraging Natural Language Processing to perform data extraction and judgment prediction for personal injury cases in Hong Kong.
Incubated at the Hong Kong Science and Technology Parks and the Hong Kong AI Lab.
A user-centric research engine for personal injury cases
Traction
Champion
Champion
Entrepreneur
Programme Cohort
HKAI Lab Incubatee
Second Runner-Ups
Resources Hub
Support
As Seen On
News
The Legal Technologist, 22 May 2021
Transforming Hong Kong into Asia’s Legal Tech Hub
"Article written by Chloe Chan, the co-founder of iDendron startup Litex is featured in The Legal Technologist. Adopting an inter-jurisdictional and interdisciplinary approach, her article seeks to dissect the success formula behind the growth of legaltech hubs in the U.S., China and Singapore before proposing a legaltech hub development model suitable for HK."
MingPao, September 2020
Start-up Founded by 5 University of Hong Kong Students Won Iron Tech Lawyer (Trans.)
"Students from the University of Hong Kong won the inaugural Iron Tech Lawyer Invitational organised by Georgetown Law with an AI-powered casebank to assist injured workers to get the compensation they deserve. In May 2020, they won the second runner-up of the 6th Hong Kong University Student Innovation and Entrepreneurship Competition."
Hong Kong Lawyer, May 2020
Hong Kong University Students Win Inaugural Iron Tech Lawyer Invitational by Creating AI-powered Platform to Assist Injured Workers
“The experience of bringing about EC Bank has boosted my sensitivity towards the closer connection between technological possibilities and legal realities, as well as getting acquainted with the adoption of LegalTech in streamlining legal processes,” said project leader Chloe Chan.
Jumpstart Magazine, 9 April 2021
Why Hong Kong Needs To Step Up Its Legaltech Adoption
"Litex Co-founder and CEO Chloe Chan talks to Jumpstart about the legal education - why it needs to adapt to the evolving technology landscape, and how to equip future lawyers for the booming legaltech market."
MingPao, September 2020
Start-up Builds Research Engine to Help Injured Workers’ Rights-Bargaining (Trans.)
"The ultimate goal of Litex is to build an AI which can automatically scrape, summarise and extract the key factors from workers' injury judgments. Litex is currently using Google's BERT as it is equipped with Deep Learning and Natural Language Processing capabilities. Currently, there are over 500 judgments in Litex's depository and research engine."
Read More
Dec 2019
College Students' Innovative Project helps promote social welfare (Trans.)
"When building our solution, we adopted human-centric design thinking to ensure product-market fit and that we are solving users' pain points."
The Story Behind Litex
01
Vision
We aim to create a legal research system that is accessible and hassle-free.
We envision it to be as streamlined as possible, but at the same time maintains a function set that is on par, if not better than the existing alternatives. And to do this, we had to completely rethink how legal research works.
02
Model Training
We started off by building a database with all the key information from personal injury judgments labelled. In particular, we identified certain “key factors”, which are the key information that affects the assessment of damages, e.g. location and nature of injury, occupation, the existence of residual pain. We then train our AI model with these labelled judgments.
03
User-centric
design
To enhance efficiency, key facts are displayed on the results page. With the filters being the key factors we have identified, we are able to help users accurately estimate the damages. Our system is co-created with lawyers and non-profits.
04
Access to
Justice
Litex transformed the dispute resolution process — as opposed to blindly accepting an insurer’s arbitrary settlement offer, with Litex's research engine, injured workers could estimate the damages they would be entitled to should they decide to reject the offer and initiate legal proceedings, thus enabling a more informed decision-making process.
Footnote
-
Based on our Key-Factor-Matching searching mechanism, only results matching your stipulated key factors would be displayed.
-
Based on our internal testing conducted by law students on three different search subjects compared to two other legal research engine. Detailed research methodology are specified and explained under the section “Efficiency”.
-
Based on the feedback from lawyers who helped us test the system or signed up for the free trial.