At Sleegal.ai, we noticed the huge knowledge gap between customers and lawyers. Sadly, the market uses that gap to its advantage and forces customers to accept exceptionally high fees when working with law firms. The cost associated with legal services is almost intimidating to an average customer.
Besides high costs, customers also feel daunted by the complexity of legal language. When it comes to finding their lawyers, they can only simply rely on their instinct without fully understanding what services they are getting.
Finding a lawyer doesn’t need to be hard, or expensive
That is exactly the problem that Sleegal.ai is trying to solve. We are here to bridge the knowledge gap between customers and lawyers. Help customers avoid unnecessary research and directly go for the best solution that they can truly afford.
How AI removes the unnecessary overhead
There are usually two hidden sources of overhead that you pay for legal services. One is the salaries of law firm secretaries who help you find lawyers. The other is the so-called ‘free’ consultations lawyers set up with you to learn about your cases. Those hidden factors are the reasons why lawyer hourly rates are unnecessarily high.
The good news is, both tasks can be replaced by AI.
How does AI do a better job than law firm secretaries?
Law firm secretaries usually follow a standard series of questions to find you the right lawyers. Lawyers also ask very similar questions every time they start an initial call with their clients. It appears that their work should be easily automated.
Well, it’s not that easy. The challenge of automation lies in secretaries’ and lawyers’ ‘knowledge’.
Secretaries usually have great knowledge about lawyers and their specialties. Some lawyers are good at immigration law. Some are good at mergers and acquisitions for startups. Secretaries know that because they have been working with lawyers for a long time, but such information is not usually searchable on the internet.
Sleegal.ai automated this task by collecting a massive amount of data from real-world cases and the lawyers who have successfully handled them. By training our search algorithm with that data, we constantly ‘teach’ our virtual assistant to find the best lawyers that match the client’s needs.
Decoding lawyer’s deep knowledge
Note that there is another layer of knowledge that is deeper than just lawyers and their specialties. Good lawyers usually ask the right questions with their clients before they take on a new case.
By working with many lawyers and decoding their deep knowledge, Sleegal.ai built an algorithm that mimicked lawyers’ mindset. Our algorithm is trained to trigger the right questions at the right moment, so all the critical information about a case can be fully captured. Lawyers typically use that information to decide whether a case is worth taking on.
For example, a client comes in with a question about wrongful termination due to gender discrimination. There are three tasks that the algorithm needs to fulfill to ensure the client’s case can be successfully handled.
- The first task for the algorithm is to find the lawyers that are specialized in employment law and ideally had previous experience with wrongful termination cases.
- The second task is to find out the cause of the termination, which is gender discrimination in this case. Determining the cause helps trigger a series of follow-up questions.
- The third task is to ask the follow-up questions and collect key information related to gender discrimination. For example, is the client considered a protected class? Does the client have any evidence?
(At Sleegal.ai, we treat client information as extremely private and confidential, and never share their case information with lawyers without their consent.)
Solving the data challenge
What makes legal data special is it usually comes in a hierarchical but inner-connected structure, which makes data training extremely challenging. Different areas of law practice may have very different data structures, but still share a certain level of similarity.
Sleegal solved the problem by building a fully customized machine learning pipeline that collects, curates, trains, and validates data efficiently. Our data structures are collaboratively designed by our data scientists and lawyers. We never buy off-the-shelf datasets or machine learning solutions, because none of them are custom-made for good legal services.
Interested in solving the AI challenge with us? Please check out our careers page. We are eager to have you.