Could you shortly tell us what legal-i is?
legal-i is like a virtual lawyer, based on artificial intelligence (AI). It specializes in medical insurance cases (such as Invalid-, Accident-, Health-, BVG, Liability-Insurance etc.). In this field lawyers and insurance experts need to read through hundreds and thousands of pages in case-files. legal-i assists these experts to find the relevant data ten times faster. It also compares new cases with similar ones in the archives to predict their length, complexity and cost. Additionally, it can determine the internal expert with the most competence to work on the new case.
Symbiosis between Man and Machine
legal-i does not replace technical experts. Rather, it creates a symbiosis between man and machine. While the machine can search an enormous amount of data in a few seconds, the expert is essential in evaluating the results, putting them into social context and making argumentation and decisions based on them. As we can see, man and machine do not stand in competition with each other but work together, effectively complementing each other. This is often portrayed very differently in press articles and science fiction movies.
What is legal-i’s background story?
The founder of legal-i, Achim Kohli, is a trained lawyer with a three-year experience from a law firm. During his time at the firm, he became aware of the time-consuming and ineffective study of documents. He also found it frustrating that archived knowledge was not systematically reused. He discussed these issues with various persons from the insurance and legal industries and found that his problem was shared by them as well as by companies at large. As a consequence, Achim decided to leave his job in order to build a first prototype of legal-i, together with CTO Markus Baumgartner and AI specialist Prof. Erik Graf. This first prototype immediately met with very positive response from the industry.
Why is it important that legal-i exists?
Insurance experts and lawyers should spend their time with value added activities. They are too expensive and too well trained to “waste” their time with “boring” and “error-prone” work that a machine can do better. Today, there is still a lot to improve in this regard. A case in the medical insurance field often contains between 400 and 5,000 pages spanning over 200 separate documents. Insurance companies, lawyers, legal expense insurers, experts and judges, among others, need to look through all these documents – a time-consuming endeavor. The pages are read, either by scrolling (in a PDF-Reader) or in printed form. Then they are sorted in order to find the relevant data in the relevant documents. This way highly qualified medical insurance experts waste a lot of time doing bulk work, without guarantee to find all relevant information.
Who can profit from your services?
Especially Insurance companies, medical experts and law firms who work with Invalid-, Accident-, Health-, BVG and Liability-Insurance-Cases can profit from legal-i.
legal-i is all about empowering them in the effective use of their highly qualified expertise.
Can you give some more specific examples of legal-i’s tasks?
legal-i has several AI-models with the following functions:
What are your biggest challenges?
Our biggest challenges are to find the best IT-experts in our field. This is necessary as the extraction of relevant facts from unstructured medical and legal data is very complex and challenging.
We also want to be able to ever more deeply and thoroughly understand the need of each individual customer.
How do you see the future of legal-i and what is your long-term goal?
Our vision is the perfect symbiosis between man and machine in the domain of medical insurance. This is a symbiosis that empowers medical insurance experts to fully focus on added value to their activities while our AI assistant accomplishes the case-file study in a near perfect way.
Our long-term goal is to bring legal-i to the DACH-region and then to become the world’s leading insurtech start-up in the case-file study of medical insurance cases.