> PageRank, for example, works great when everyone searching for "skiing near Tahoe" is looking for the same popular pages. But when you're doing legal research, a lower-court case that directly addresses your issue but isn't widely cited is much more valuable than a highly-cited Supreme Court case that doesn't address your issue.
This is true where you are doing fine-grained research, but there are plenty of lawyers who are constantly delving into new areas of law that are adjacent to or tangential to their primary area of interest or practice. When this happens, it can be phenomenally useful to get up to speed on the area of law by seeing which decisions have the highest PageRank, or PageRank weighted by certain factors, etc.
In this way, one way to think about the startup that implements a PageRank algorithm is that it is not disrupting electronic legal research, but rather disrupting legal textbooks. In my experience, the fastest way to get abreast of a new field is to find the leading textbook. One could imagine a sophisticated database which could altogether remove the need to consult a textbook by painting a picture of the leading cases, key pieces of legislation, which sections are most often referred by which cases in which context, etc.
As somebody doing a very similar thing to Judicata elsewhere in the world, I have looked into Judicata quite a bit. Although they are in stealth mode, there is some information out there and I have been able to infer a bit about what they are doing. Essentially, they are seeking to use NLP to retrieve certain information from legal texts. The primary piece of information they seek to retrieve is the legal claim, and certain elements surrounding it (e.g. 'breach of s X of Y statute'). They also seek to retrieve a number of other bits of information from case law using NLP.
In line with Peter Thiel / Palantir's philosophy that the human brain is an amazing machine not to be supplanted by computers, but one that should be used to its fullest, augmented by computers, Judicata's software involves using NLP as much as possible, then feeding or 'striating' that information to lawyers or legally trained people, depending on the complexity of the information extracted, for their confirmation. This is in any case necessary because NLP cannot get close to 100% accuracy for the information they are trying to extract, and you need 100% accuracy in the legal domain (e.g. it would be unacceptable to get the legal claim wrong, c.f. Google search).
One consequence of structured legal texts is improved search. What many don't realise is the degree with which structured search on legal texts will improve legal research. E.g., if I want to find all cases in the last 10 years where the plaintiff claimed breach of duty in an occupiers' liability suit, I simply cannot. To find that batch of cases (accurately) would take me hours. If the legal claim was a structured piece of information, I could just search for it. As an ex-lawyer and ex-legal researcher, the number of hours that could be saved per lawyer per year could easily be in the hundreds, and this is at charge out rates of $300-$1k per hour. This is, similarly to the above comments, in line with Thiel's investment thesis to 'improve something 10-fold' or 'make a quantum advance to cause adoption / change consumer behavior'. I think most people seriously underestimate how significant of an improvement structured search would be.
The other thing that Judicata are flying under the radar about, a little bit, is the ability to use structured legal information for other purposes. High on the list is analytics, which Itai Gurari mentioned at the end of a talk, but merely in passing as if it was inconsequential. I think this is pretty clearly a multi-billion dollar market waiting to be made. If you look at what similar firms are doing in niche areas of law, e.g. Lex Machina, and look at what they are charging, and extrapolate the types of questions you can answer with structured legal information, the potential becomes clear. Again, this is in line with Thiel's investment thesis to 'create a market a dominate it, rather than compete in an existing one'.
The primary difficulty for Judicata or somebody undertaking to do the same thing is that the task is mammoth in just about every respect. As such the optimum strategy is likely to attack a niche jurisdiction and then build out the product. You can't go 'full-lean', because you need at least a semi-complete data set, but you can start 'small'. Hence, Judicata have been working on a niche jurisdiction of law as their first project: Californian Employment Law. While I am not in that jurisdiction (not even in America), that seems to me to be a very reasonable area of law to start with given that most legal claims (I think) are found in California's employment law statute (as opposed to other areas where the legal claims are found in Judge-made common law). Furthermore, there are a ton of neat pieces of information in employment law which you can structure, e.g. in a discrimination case, what factor was the plaintiff allegedly discriminated on - race, age, gender, etc. Finally, uptake would be high among employment lawyers who research at reasonably frequent intervals and have a practical need for more accurate search; compare this to constitutional law for example.
While part of the reason they are operating in stealth mode is simply because it takes so long to build up a semi-complete data set, I think the other part of the reason is because the biggest risk for such a firm is that Lexis, West or Bloomberg will start doing something similar. Imho, it's likely they will eventually but the risk of Judicata catalyzing that process is pretty small.
There are a few other firms operating in this space but with fundamentally different philosophies. My view is that these other firms are simply taking the wrong approach and simply want to release a product and build on it now in the lean tradition. Judicata's product is the type of product where the question is not whether there will be adoption, but rather whether or not you can actually build the product on your budget and in the time frame required. Imho, if Judicata can successfully create what they are planning on, it will flatten their competition. The real question is whether they can.
This is true where you are doing fine-grained research, but there are plenty of lawyers who are constantly delving into new areas of law that are adjacent to or tangential to their primary area of interest or practice. When this happens, it can be phenomenally useful to get up to speed on the area of law by seeing which decisions have the highest PageRank, or PageRank weighted by certain factors, etc.
In this way, one way to think about the startup that implements a PageRank algorithm is that it is not disrupting electronic legal research, but rather disrupting legal textbooks. In my experience, the fastest way to get abreast of a new field is to find the leading textbook. One could imagine a sophisticated database which could altogether remove the need to consult a textbook by painting a picture of the leading cases, key pieces of legislation, which sections are most often referred by which cases in which context, etc.