Starting from scratch or ground zero on a new case is the normal, NOT the ideal though. In all cases, similar or opposite there is certain patterns in the evidence that attorneys and law firms seek in every case. By using A.I. and training it through the process of modeling, that is feeding it samples of docs that are Yes’s and No’s of what it is your looking for, after a couple of hundred or so samples , you can run the model and see the results. If the results are not satisfactory great feed it some more samples, run the model and repeat until the desired results are reached. Now putting all the work into lets say a “privilege” model inside of an anti-Trust case and being able to reuse that work product on a future anti-trust case, now that is what we call Ideal. By using Deep machine learning, the A.I. will store what it learned on this case, and can transfer it to a future case. With each case it continues to learn and grows in size and depth of its knowledge of privilege. Imagine what that wealth of knowledge would look like after the 6th or 10th case it was used on. No need to start at ground zero, use the model and get to the documents right away. Build models of Responsive, Privilege or practice area specific models like Employment law, sub-practice areas like wrongful termination, sexual harassment or retaliation and you can break it down even further for example discrimination, age, sex, gender, ADA, or race they are all types of employment law cases able to be built into portable models. It is the future of eDiscovery and we at NimbleSystems are leading the charge. Lets us show you how powerful it is give us a call and set up a demo.