All lawyers are not created equal. Some are quite simply better, whether by knowledge, skill in advocacy, connections or case management. Whom you choose is often a matter of little more than luck. The public has for centuries chosen liti- gators by perceived ability, or peer recognition. Sadly this often had little to do with the results they achieved for their clients who would usually only find this out years later at the end of expensive and ultimately unsuccessful cases. There had to be a better way to choose. The best ideas appear obvious in hindsight. Why not pick lawyers based on how often they win?

It’s an obvious thing to do, but people had little way of doing it. Court data is in the infancy of being put online. Most courts run on disparate systems that enable looking up individual cases, but are woefully inadequate for those looking to compare case types, average durations, judges or lawyers. These were open secrets, lying in the public record for anyone to see. Anyone who could read through the 41,000 civil cases filed in America each day and compare the data, that is.

Enter Premonition, a Big Data start up from Miami. Although in existence for only a little over a year, it has already revolutionized law, discovering numerous creative ways to automate the collection of court data at scale. “It’s like searching for a needle in a haystack in a hurricane,” says Premonition Co-Founder and Inventor, Toby Unwin. Creating an Artificial Intelligence system to download, read and understand cases has resulted in, what they claim, is the world’s largest database of lawsuits, bigger than every major legal database combined.

Premonition is an Artificial Intelligence system that mines Big Data to find out which attorneys usually win before which Judges. It is “A very, very unfair advantage in Litigation,” according to the company’s web site, www.premonition.ai. Premonition recently published a report on the win rate of every Barrister and law firm in the United Kingdom’s High Courts. “Data wise, it was a rounding error for us – 4,000 cases a year versus 41,000 cases a day,” Unwin explains. The result was a furor.

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