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by blee37 3290 days ago
I recommend the program to anyone thinking about starting a startup. The highlight was having live mentorship from very successful startup founders. My mentor had raised tens of millions in VC funding for his startup. He spent 2 hours with us every week.

My presentation: https://www.startupschool.org/presentations/242
2 comments
anindha 3290 days ago
I had a similar problem looking for a immigration lawyer. The problem with only looking at court records and other public information is it skews in favor of lawyers that take on slam-dunk cases.

If a law firm successfully processed 500 green card applications for Google that isn't as impressive as being successful in processing 10 EB-1 applications for early stage startup founders.

My solution was to ask for recommendations from people that required the same service as me. Could you integrate anonymous yelp style reviews for lawyers where the person giving the review could be verified?

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blee37 3290 days ago
It's a good idea to search for a lawyer who is good at the specific service you require. We're working on making the data as narrowly tailored as possible to the problem the user is searching for. Right now, the data is filtered to the level of case type. Later on, we can add additional specific factors.

Verified reviews are also something we'd like to add. Also, we'd want to filter the reviews to show more prominently reviews from past users who used the lawyer for the same issue.

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vmarsy 3290 days ago
Interesting concept, however assuming you become popular enough, then you could be victim of lawyers trying to game the metrics.

What happens if a lawyer choose to only defend a lot of easy to win cases to bump up their track record close to 100%. How do you identify how hard a case was in the first place, and find the lawyers who do go after hard cases successfully.

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blee37 3290 days ago
We'd like to be able to pinpoint a lawyer who is good at the specific legal problem that the user has. So we'd want to filter cases so that data can be counted on narrow ranges of very similar cases. Right now we're filtering the data at the level of case type, and we'd like to add additional filtering on specific factors in the future.

Some of the filtering and case patterns would probably be best identified with machine learning.

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