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There are plenty of challenges with cross browser testing and I have addressed them in great detail in one of my articles: Is Cross Browser Testing Still Relevant?Let me abstract the pointers for you! :)Right now if you look at different browsers such as Google Chrome, Mozilla Firefox, Safari, Opera, and more. You will realize that each of these browsers uses a totally different technology in the backend, for example, Google Chrome uses a browser engine called Blink and JavaScript engine called V8. On the other hand, Mozilla Firefox uses a different browser engine called quantum and JavaScript engine called SpiderMonkey. As each browser has a different engine working in the backend to render the same piece of an HTML, CSS and JavaScript code, then the results may come out to be totally different in different browsers.So the major pain point with Cross Browser Testing is that you would have to test the same piece of code for your website or web application across all the different browsers, operating systems and mobile devices. And as much as we may wish to avoid the browser compatibility issues, unfortunately, there is no workaround for this. Even if your developer team happen to follow the best guidelines at present by taking all of the well-supported web elements for your web application. There are chances that any one of those web elements may end up a stray from the browser support on a later date. And figuring out the ripple effect of that can be very exhausting.So it is recommended to perform cross browser testing in every release cycle, on all of the browsers, browser versions, and operating systems. If not all then you can start off with the browsers that matter the most to your business and based on that you can define your cross browser testing strategy.Each browser has a different demographic, so you can differentiate based on the target audience. Your website or web app that you’re going to build would have differences in the targeted audience on the basis of the device they’re using.Which Browsers Are Important To You For Cross Browser Testing?So for example, if it’s a banking application, banking software which is used by mainly can say bank employees then it would be a target for desktop browsers. However, if you’re building a news website or social media channel then you know most of the users would be using it on mobiles. So basically, you can priorities your testing based on your audience. There are a number of tricks which you can do.First, is to obviously realize the targeted audience. You can refer to it as Predictive Analysis, where you are predicting which type of audience will come and based on that you are creating your cross browser testing strategy.The other one would be using the Reactive Strategy. For example, you would have the Google Analytics code configured in your Website or Web-application. You can leverage that tag to get information from Google Analytics about the browsers, operating system even browser versions, and devices that are being used by your website visitors to access your website.You can find it inside Google Analytics by going to the Technology section in the User Section based.On that basis, you can define a strategy and build your cross browser testing matrix indicating the most important browsers, browser versions and devices on which you need to test your web application for browser compatibility.After realizing the most important browsers for your business, and developing a matrix. The next step is to figure out the execution part. What would be the best approach to test on all these browsers?Earlier what people used to do was that they would create an in-house device lab. If you are a big enterprise then you can purchase multiple different devices or multiple desktops.So for example, say you want to test on Safari browsers for which you would need a Mac device, either laptop or tab or maybe both. Similarly, if you want to test on the Edge browser versions then you would need a Windows device. If your targeted audience uses both Edge and Safari browsers then you would require various Mac devices and Window devices. This way, you would have to invest a considerable amount of figure over different hardware.This is an expensive method, agreed! But if you have a very limited number of browsers that you want to test on, then this is the fastest ideal solution for you. However, if you have to test on a huge list of browsers and browser version then the best solution right now would be to opt for a cloud-based approach.This is where cross browser testing cloud such as LambdaTest can be a perfect choice for you. With LambdaTest, you can test across 2000+ real browsers running on real operating systems, hosted on the cloud. So you can completely eliminate the hassle of investing and maintaining an in-house device lab as you can go for a cloud-based solution with zero-downtime. That is not all though, you can also enjoy the benefits of third-party integrations with tools for bug-tracking, project management, instant messaging, and continuous integration.Now you can even ditch manual cross browser testing as you make a move to automation testing with Selenium. But that too has few downsides to it.Challenges of Cross Browser Testing with SeleniumSelenium Grid is definitely a great way to set up an automation system and test on different browsers. However, you would need a cloud-based infrastructure for that and there are two ways you can go about it!Approach 1: Setting up your own Infrastructure on cloudCreate and set up an infrastructure for yourself. So you go to cloud-based infrastructure provider, for example, AWS(Amazon Web Services), Rackspace or Digital Ocean. Or you can even go to a Bare Metal Host Server provider, and then use containers, dockers, and Zalenium itself, to create a Grid of your own.However, there are many challenges that may come across.Challenge 1: Limited Number of Browsers!The challenges here would be around the limited number of browsers over which you can perform cross browser testing. But that is not all, there are further limitations on that as well. For example, mac machines if you want to test on safari then you need an online Mac solution provider and they are totally different than Windows or Linux solution providers.So Mac only works with authorized Mac resellers and are very costly to even lease on different resellers. You can go to MacStadium and ask them to lease you some of the Mac devices online.Challenge 2: Updating & Maintaining The Existing Browser LibraryAnother challenge here is to maintain the browser versions so Google Chrome has a very fast release cycle every 2-3 months they come out with a new browser version, Firefox is toe to toe on that context. Now, every time a new version is introduced, you would have to change your Docker image to update this change in the browser. You would also have to maintain your browser infrastructure every time.This drill would also be required whenever a new operating system is launched so, for example, Mac is going to launch its new operating system named Catalina. Now, every time when a new operating system is launched you have to update your all the infrastructure to match the new updates.Challenge 3: Latency Issues with Test ExecutionThere is another challenge which comes in the execution of the test itself. A cloud-based server will usually have a little bit of latency because there is a cloud network involved. Which means that every command thread will take a little bit longer to go from your local machine to the cloud infrastructure and then execute back.So test thread wise execution takes a little bit longer. However, the advantage here is that you can leverage parallel testing. With parallel testing, you can create multiple different machines at the same time, and you can test on these machines, simultaneously. This will reduce your test execution time by many folds.Example Scenario:Say you have 100 tests and each test takes 1 minute to execute. That means it would take 100 minutes to execute your whole test suite in a sequential manner. However, if you have 10 different machines and you are leveraging them for parallel testing then your whole suit will complete within 10 minutes so you cut down your test series execution by a factor of 10 by simply increasing the number of machines over which you want to test on and this again increases a new added work for the DevOps team that they have to maintain test infrastructure of ten different types of machines they have to scale up those machines, scale down they have to optimize the performance they have to clean these machines every time the test is completed.The result from Example Scenario:This is an added overhead related to the DevOps team but it is said by the fact that you now have a better cross browser testing coverage under better test execution time.Approach 2: To go for Cloud-Based Service ProviderYou can go to a service provider for Selenium automation testing like LambdaTest which have built-in machines that are ready to fire up, ready to scale up, allowing you to run your test on them directly.Approach 1 vs Approach 2! Which one to choose?My pick would be approach 2 and there is a good reason behind it. There is you can say a significant cause involved as well when you set up your own Grid. For example, if I want to set up my own Grid and run successful tests using the AWS and I want them to run on a decent pace.I would have to go with a machine that has around 8-16 GB Ram with an SSD hard drive. These are usually what we call c5d 2x large machine and they cost at least $0.752 per hour.Now, if we calculate on this basic, I want to run 100 tests using this machine and I want to run these tests for around 12 hours every day, over the period for 30-40 days during the 6 months of the development process. Let’s calculate how much would that be?Here’s the costing of 101 (100 Selenium Node VMs and 1 Hub VM) machines with 12 hours per day uptime.→ c5d.2xlarge(8Core CPU, 16GB Memory, 200NVMe SSD) costs $0.752 per Hour→ Monthly cost = 0.752*12*30*101 = $27342.72 Per MonthNow, let me tell you about LambdaTest. Each machine provided by LambdaTest has 8Core CPU with minimum 16GB of RAM memory, with NVMe SSD to further boost test execution time.And to run your tests over 100 parallel sessions, the pricing for LambdaTest would be $7900 Per Month.Wondering about the significant difference in cost?Let me explain why a cloud-based provider turns out to be a cheaper cost overall.Basically, a cloud-based provider offers a shared infrastructure. So once you perform a test execution over a machine, then those machines are been cleaned-up and then they are handed over to other users, which builds a community of users who are using that shared infrastructure.On a shared basis, you can say that the test margin of maintaining that infrastructure goes down, so in the long term these cloud-based test service providers come up cheaper.But wait, there is more!The other aspect is if you want to run a test over 100 different machines, you have to maintain those 100 different machines too! This would require significant resource and bandwidth consumption.So running 100 different machines will require to use different tools for configuring and supporting your test environments. These tools would involve names like VMware, Container, Docker, you would have to create templates of all these 100 machines. You would have to maintain those templates and you have to upgrade those template too!Then you would have to create scripts that will automatically scale up those machines and one that accessing is them they would automatically clean machine so that in the next testing cycle, so you would have to clear all the cookies history, and cached data.You can avoid this hectic drill by opting for LambdaTest as we will provide you with a well-maintained infrastructure on the cloud. LambdaTest will help you eliminate the need for infrastructure development, infrastructure maintenance, infrastructure scaling in-house.We are also eliminating the need for having a dedicated DevOps team just for testing so that you can focus more on building code with better quality, resulting to faster shipping of products after validating them for cross browser testing.Cheers!

I would call you a business owner and I would call your business a start-up. I would not call you an entrepreneur. Actually I probably wouldn’t call you a startup just because too many people use that term in relation to Silicon Valley nowadays, so while I consider it correct, I would use a less ambiguous term like ‘new’.My wife started a dental practice, while I started a consulting company and (separately) purchased an established business.The thing about dental practices is that there are quite a lot of them and the services they offer are all broadly similar. For example we used a ‘dental fit-out’ company to actually build the practice. Also from a business model perspective we were able to build relatively accurate patient number, revenue and cost projections going forward years before we even signed the lease. We were also able to borrow money at a fairly sensible rate to buy major equipment because the whole business proposition was well understood by the supplier.I don’t mean to say that all dental practices are the same, or that patients will get the same service at any dental practice. What I mean is that once you abstract back to the business model, those personal details disappear and you’re dealing with a largely predictable future.By contrast I started a consulting company helping businesses with marketing technology. That business has no meaningful revenue projections and we’ve even tweaked the business model several times.There is a big difference between what my wife is doing now as a business owner and what she did previously as a dentist. However I would really struggle with calling her an entrepreneur, to me that term implies far more ambiguity in the business. She’s never going to make millions doing her work. Nor is she fundamentally trying to change the face of dentistry. She is delivering a high-quality professional service.I frequently refer to her practice as a start-up practice. Everything had to be decided on and created. While we had many templates, it was still a very different proposition to taking over an existing business. I’ve done that too and you inherit a complete set of systems - most of them good. That’s a completely different proposition to starting with nothing and trying to create a complete set of systems, even if your goal is to end up in the same placeIn my opinion you are a business owner but not an entrepreneur. Also if you start the business from scratch then I’d refer to it as a startup for the first couple years.Lastly, they’re just words. Use whatever you feel most comfortable with. It’s not like the grammar police are going to lock up your business because you referred to yourself as a startup entrepreneur.

AI in Law: Current Applications – Insights Up FrontBased on our assessment of the companies and offerings in the legal field, current applications of AI appear to fall in six major categories:Due diligence – Litigators perform due diligence with the help of AI tools to uncover background information. We’ve decided to include contract review, legal research and electronic discovery in this section.Prediction technology – An AI software generates results that forecast litigation outcome.Legal analytics – Lawyers can use data points from past case law, win/loss rates and a judge’s history to be used for trends and patterns.Document automation – Law firms use software templates to create filled out documents based on data input.Intellectual property – AI tools guide lawyers in analyzing large IP portfolios and drawing insights from the content.Electronic billing – Lawyers’ billable hours are computed automatically.Next, we’ll explore the major areas of current AI applications in law, individually and in-depth:(We’ve done our best to place companies into the category that best represents their product offering, but it’s important to note that there is overlap on many of the groupings we’ve chosen.)Due DiligenceOne of the primary tasks that lawyers perform on behalf of their clients the confirmation of facts and figures, and thoroughly assessing a legal situation. This due diligence process is required for intelligently advising clients on what their options are, and what actions they should take.While extensive due diligence can positively impact long-term shareholder returns (according to a study by the City University of London), the process can also be very time-consuming and tedious. Lawyers need to conduct a comprehensive investigation of meaningful results. As such, lawyers are also prone to mistakes and inaccuracy when doing spot checks.Kira SystemsNoah Waisberg, a former M&A lawyer who founded the software company Kira Systems, thinks that due diligence errors by junior lawyers often occur for a number of reasons. These include working very late at night or on the eve of a weekend, forgetting to perform due diligence before the end of the workweek, and failing to act on it when a deal structure is completely revised.He adds, “Many associates are in a certain negative mood about the efficacy of manual due diligence. Lawyers, being human, get tired and cranky, with unfortunate implications for voluminous due diligence in M&A.”Kira Systems asserts that its software is capable of performing a more accurate due diligence contract review by searching, highlighting, and extracting relevant content for analysis. Other team members who need to perform multiple reviews of the content can search for the extracted information with links to the original source using the software. The company claims that its system can complete the task up to 40 per cent faster when using it for the first time, and up to 90 per cent for those with more experience.LEVERTONLEVERTON, an offshoot of the German Institute for Artificial Intelligence, also uses AI to extract relevant data, manage documents and compile leases in real estate transactions. The cloud-based tool is said to be capable of reading contracts at high speeds in 20 languages.In 2015, IT firm Atos sought the help of real estate firm Colliers International, which used LEVERTON in performing due diligence of a company that the former was about to acquire. Through the use of LEVERSON’s AI, information such as payable rent, maintenance costs and expiration dates were extracted from thousands of documents and then organized on a spreadsheet.eBreviaHowever, lawyers can be burdened by reviewing multiple contracts and they may miss important edits that result in legal issues later on. This is the same problem that Ned Gannon and Adam Nguyen, co-founders of eBrevia, experienced when they were still working as junior associates. They built a startup in partnership with Columbia University with the intention of shortening the document review process.eBrevia claims to use natural language processing and machine learning to extract relevant textual data from legal contracts and other documents to guide lawyers in analysis, due diligence and lease abstraction. A lawyer would have to customize the type of information that needs to be extracted from scanned documents, and the software will then convert it to searchable text. The software will summarize the extracted documents into a report that can be shared and downloaded in different formats.JPMorganOther organizations such as JPMorgan in June 2016 have tapped AI by developing in-house legal technology tools. JP Morgan claims that their program, named COIN (short for Contract Intelligence), extracts 150 attributes from 12,000 commercial credit agreements and contracts in only a few seconds.This is equivalent to 36,000 hours of legal work by its lawyers and loan officers according to the company. COIN was developed after the bank noticed an annual average of 12,000 new wholesale contracts with blatant errors.ThoughtRiverOther AI industry players includeThoughtRiver, which handles contracts, portfolio reviews and investigations for improved risk management. Its Fathom Contextual Interpretation Engine was developed together with machine learning experts authorities at Cambridge University.The company states that it designed the product to automate summaries of high-volume contract reviews. While users read content extracts, they can also read the meanings of clauses provided by the AI. The system is also said to be capable of flagging risky contracts automatically. The company provides a brief tour of their product in the 3-minute video below, including a detailed look at the user interface and basic functions of the software:LawGeexLawGeex claims that its software validates contracts if they are within predefined policies. If they fail to meet the standards, then the AI provides suggestions for editing and approval. It does this by combining machine learning, text analytics, statistical benchmarks and legal knowledge by lawyers according to the company.In this video, LawGeex CEO Noory Bechor further explains how his product can cater to legal services.The company also claims that with their tool, law firms can cut costs by 90 per cent and reduce contract review and approval time by 80 per cent (though these numbers don’t seem to be coupled with any case studies). The firm lists Deloitte and Sears among some of its current customers.Legal RobotOn the other hand, Legal Robot, a San Francisco-based AI company, currently offers Contract Analytics, its answer to the growing contract review software market. Currently, in beta, the company states that its software is capable of changing legal content into numeric form and raising issues on the document through machine learning and AI.A video presenting how the software works state that it builds a legal language model from thousands of documents. This knowledge is used to score the contract based on language complexity, legal phrasing, and enforceability. With the issues flagged by the software, it then provides suggestions on improving the contract’s compliance, consistency, and readability by evaluating it on best practices, risk factors and differences in the jurisdiction.Ross IntelligenceEvery lawsuit and court case requires diligent legal research. However, the number of links to open, cases to read and information to note, can overwhelm lawyers who have limited time doing research. Lawyers can take advantage of the natural language search capability of the ROSS Intelligence software by asking questions, and receiving information such as recommended readings, related case law and secondary resources.BakerHostetler, in what seemed like a break in tradition, employed ROSS in its bankruptcy department, 100 years after the law firm’s founding. The law firm’s chairman, Steven Kestner, explained in an interview that they decided to employ the software to work on 27 terabytes of data. A Forbes report describes ROSS’ function in the law firm’s operations: “ROSS will be able to quickly respond to questions after searching through billions of documents.The company claims that lawyers can ask ROSS questions in plain English such as “what is the Freedom of Information Act?” and the software will respond with references and citations. Like most machine learning systems, ROSS purportedly improves with use.CasetextOn the other hand, Casetext’sCARA claims to allow lawyers to forecast an opposing counsel’s arguments by finding opinions that were previously used by lawyers. Users can also detect cases that have been negatively treated and flagged as something that lawyers may deem unreliable.Casetext claims large law firms such as DLA Piper and Ogletree Deakins as its clients.Other Assorted ApplicationsOther software products also combine machine learning and legal analysis to assist lawyers with legal research but with limited coverage. For example:Loom features win/loss rates and the judge ruling information but only for civil cases in select Canadian provinces. In an interview with Stanford Law School, Mona Datt (co-founder of Loom Analytics) elaborates:“Instead of performing open text searches looking for personal injury precedents, a lawyer could use Loom’s system to see all personal injury decisions that were published in a given time span and then break them down by the outcome.Instead of combing through individual decisions looking for ones written by a particular judge, Loom’s system can show all decisions authored by that particular judge and provide an at-a-glance snapshot of their ruling history. In short, we’re providing quantitative metrics on Canadian case law.”Judicata, on the other hand, only serves California state law as of this writing. Its software, Clerk, is said to be capable of reading and analyzing legal briefs. It also evaluates their pros and cons and then assigns a score for each brief based on arguments, drafting and context.The recommendations intended to reduce content mistakes are listed as part of the action items for the user. In the company’s blog, Product Manager Beth Hoover explains, “Clerk helps reduce these errors by identifying the quotations in a brief and cross-checking them against the cited case to ensure the text is identical and the page numbers are correct.”Potential Bias ConcernsIn a recent paper by Susan Nevelow Mart of the University of Colorado Law School tested if online legal case databases would return the same relevant search results. She found out that engineers who design these search algorithms for case databases such as Casetext, Fastcase, Google Scholar, Lexis Advance, Ravel, and Westlaw have biases on what would be a relevant case that their respective algorithms will show to the user.For example, newer databases such as Fastcase and Google Scholar have generated less relevant search results compared to older databases such as Westlaw and Lexis. Mart argues that search algorithms should be able to generate redundant results on whatever legal online database is used since lawyers need only the most relevant cases. However, because these engineers have biases and assumptions when developing their algorithms, users are recommended to use multiple databases in order to find out the cases that fit their needs.By no means does a single paper (or a dozen papers) imply that legal AI tools shouldn’t be used, but rather that their pros and cons are yet to be weighed out thoroughly. Bias is by no means specific to the legal field, as machine learning systems are always influenced by the data that they’re trained on.Readers with an interest in AI bias and the ethical considerations of discrimination by machines may benefit from reading our recent article in collaboration with the IEEE: Should Business Leaders Care About AI Ethics?EverlawThere has been a growth in the number of e-discovery product manufacturers that harness AI and machine learning. Everlaw uses its predictive coding feature to create prediction models based on at least 300 documents that were classified before as relevant or irrelevant by the user.The AI looks into the contents and metadata and uses such information to classify other documents. The company claims that the prediction model’s results can help users easily identify which documents are most relevant. It also recommends actions on the part of the user on how to improve the software’s predictive accuracy of the model.You can check a demonstration of Everlaw’s Prediction Technology feature in this video:DISCODISCO claims to deliver faster results using its cloud technology for document search on large data volumes. Similar to Everlaw, it also employs prediction technology to suggest which documents are most likely to be relevant or irrelevant to the user.The AI works by assigning scores on tags (on a scale of -100 to +100) in order to improve its prediction results. The software displays its search results with each document’s score and suggests which material is most likely useful for the reader. In the promotional video below, Dr Alan Lockett (DISCO’s Head of Data Science) explains the company’s technology in simple terms:CatalystDenver-based Catalyst markets its Automated Redaction product to help lawyers and legal reviewers remove sensitive and confidential information on documents. “Manual redaction”, as the company claims, is cumbersome considering the amount of time that a reviewer spends on locating content on a digital document and then applying black boxes on these statements.Their tool allows users to convert a document to digital format and then perform multiple sets of redactions for a single document by searching for a word or phrase. Users can also set patterns such as social security numbers on the software to be redacted. An overview of the Automated Redaction feature for e-Discovery introduces its uses.ExterroExterro’s WhatSun claims to combine the functions of a project management software with the capabilities of performing e-Discovery. In other words, users can perform their legal research and then collaborate with others using the software.According to one of Exterro’s law firm clients, they were able to cut down on redundant workers from 100 lawyers down to 5 when they started to employ the system. The software was able to perform the e-Discovery tasks of the lawyers at a 95 per cent cost savings according to the law firm. The company claims AOL, Microsoft, and Target among its marquee customers.Brainspace DiscoveryBrainspace Discovery clusters and sorts documents to match closely to a user’s document search. When finding documents, the AI employs concept search (searching for documents that are similar in concept but not necessarily in words or phrases), term or phrase extension (instructing the software to remove terms incorrectly associated with the results), and classification (specifying another category to refine the search). The topic of document digitization and search is explored further in our article on Document Digitization in Finance. The company claims that by combining these three features, the software can better deliver document search results closer to a user’s needs.Other AI-powered contract review platforms that cater to due diligence for legal professionals include:iManage’s RAVN whose M&A Due Diligence Robot is designed for M&A documents to automate the review process and extract data from cluster sets;LitIQ, which capitalizes on computational linguistics technology to reduce contract-related disputes (Gary Sangha, founder of LitIQ shares his thoughts on the relationship of machine learning and law in this interview);LegalSifter, which claims to cut time and financial costs through its AI software that looks for specific concepts in documents such as general terms and conditions and confidentiality agreements;Seal, whose software is used by Dropbox, PayPal and Experian, was able to reduce the time spent to 48 hours from 255 days by a utility company by searching for thousands of contracts with specific clauses according to their case study; andLuminance, which claims to be the only tool that searches and ranks unusual and anomalous documents and clauses for lawyers.While there has been a growth in the use of e-Discovery tools, its application has become a public issue in states such as California. In 2015, the State Bar released an amended Proposed Formal Opinion, requiring lawyers to have a decent knowledge on the to use the e-Discovery system or they will warrant discipline after being proven to have committed intentional or reckless acts. The State Bar also suggests that if a lawyer is incompetent on the facility, he should learn the skill, hire someone who’s knowledgeable, or just simply decline representation.Prediction TechnologyIn 2004, a group of professors from Washington University tested their algorithm’s accuracy in forecasting Supreme Court decisions on all 628 argued cases in 2002. They compared their algorithm’s results against a team of experts’ findings. The statistical model by the researchers proved to be a better predictor by correctly forecasting 75 per cent of the outcomes compared to the expert’s 59 per cent accuracy. Although in a separate industry with its own separate problems numerous additional use-cases of predictive analytics can be found in our article on Predictive Analytics in Banking. This article illuminates AI programs utilizing predictive analytics to solve real-world issues.Expanding the coverage from 1816 to 2015, Prof. Daniel Katz of Michigan State University and his two colleagues achieved a 70.2 per cent accuracy on case outcomes of the Supreme Court in their 2017 study. Similarly, Nikolaos Aletras of University College London and his team used machine learning to analyze case text of the European Court of Human Rights and reported a 79 per cent accuracy on their outcome prediction.Prof. Daniel Kantz, in his 2012 paper, stated, “Quantitative legal prediction already plays a significant role in certain practice areas and this role is likely to increase as greater access to appropriate legal data becomes available.”IntraspexionIndeed, several AI companies have ventured into this field such as Intraspexion, which has patented software systems that claim to present early warning signs to lawyers when the AI tool detects threats of litigation.The system works by searching for high-risk documents and displays them according to the level of risk that the AI has determined. When a user clicks on a document, risk terms as identified by subject matter experts through the algorithm are highlighted. According to the company, users can which documents put them at risk for litigation when they use the software.Ravel LawAnother tool, Ravel Law, is said to be able to identify outcomes based on relevant case law, judge rulings and referenced language from more than 400 courts. The product’s Judge Dashboard feature contains cases, citations, circuits and decisions of a specific judge that is said to aid lawyers in understanding how the judge is likely to rule on a case.The firm’s CEO, Daniel Lewis, affirms such claim in this interview when he explained that the Ravel Law can aid in litigation strategy by providing information on how judges make decisions.Lex MachinaLex Machina’s Legal Analytics Platform has a variety of features that are said to assist lawyers in their legal strategy. For example, the Timing Analytics feature uses AI to predict an estimated time when a case goes to trial before a specific judge.The Party Group Editor, on the other hand, allows users to select lawyers and analyze their experience before a judge or a court and the number of lawsuits they were involved in before, among others. In the video below, the product’s user interface is featured and sample analytics results are presented:PremonitionFinally, Premonition, which claims to be the world’s largest litigation database, asserts to have invented the concept of predicting a lawyer’s success by analyzing his win rate, case duration and type, and his pairing with a judge at an accuracy of 30.7 per cent average case outcome. According to the company, the product can also aid in looking at different cases and how long they’re going to take for each attorney.But similar to any analytics platform, AI tools that deal with predictive technology need a lot of data in the form of case documents to fully work according to Kantz. In an article, the model is described as “exceptionally complicated.” That’s because it needs almost 95 variables (with almost precise values up to four decimal places) supported by almost 4,000 randomized decision trees to predict a judge’s vote. Kratz admits that a database that will fully support his product is still not readily available except for a few ones that charge access fees to obtain data.Legal AnalyticsCase documents and docket entries provide supplementary insights during litigation by lawyers. Current AI tools claim that today’s software products are able to extract key data points from these documents to support arguments.Lex MachinaHogan Lovells litigation attorney Dr Chris Mammen uses Lex Machina’s Legal Analytics software to find out “who is the plaintiff, who is their counsel, who have they represented, and who else have they sued.”The software generates data that can be used to analyze an opposing counsel’s likelihood of winning or losing a case. He claims to save time through the analytics results when creating a narrative. “You send an email to the research department, get first results, iterate the process – that usually takes a day or more.”Intellectual property lawyer Huong Nguyen also used the software when she represented a generic pharma company. Using the Legal Analytics tool,, she found out that the judge’s history of ruling cases tends to favour cases like hers. Both parties settled in the end, which was a better resolution according to Nguyen.Ravel LawThe data can also be used in pitching a law firm’s services to potential clients by providing intelligence on the opposing counsel, generating values on the probability of winning the case and identifying litigation trends to use in their marketing campaigns.Apart from prediction technology, Ravel Law’s software also claims to provide lawyers with judges’ data on cases, circuits and ruling on their dashboard, which can be used in landing new clients. Currently, the company is bolstering its data minefield by working with Harvard Law School in digitizing the faculty’s US case law library to be made available on its tech platform.Settlement AnalyticsIt must be noted that legal analytics still has its limitations. Robert Parnell, CEO of SettlementAnalytics, explains in his paper that there is uncertainty in producing results with a high level of accuracy with this kind of technology. This comes in very small sample sizes after filtering, cognitive biases, the tendency to interpret random trends as valid patterns, and having a lot of data noise.Parnell opines, “Overall, the quantitative analysis of legal data is much more challenging and error-prone than is generally acknowledged. Although it is appealing to view data analytics as a simple tool, there is a danger of neglecting science in what is basically data science. The consequences of this can be harmful to decision making.”Document AutomationA McKinsey & Company report estimates that knowledge work automation will most likely be one of the top disruptors in the global economy.A screenshot from McKinsey’s report cited above. See the orange bar to the right of “2. Automation of knowledge work”Some law firms are also beginning to adopt such technology by drafting documents through automated software. Many such software companies claim that the final document, which could take days by manual human drafting, is generated in a matter of minutes. This technology is utilized in finance today; discover real-world banking-sector examples of the concepts outlined in this article.PerfectNDANeota Logic System claims that it's software PerfectNDA shortens the nondisclosure agreement (NDA) process by offering templates selected by AI according to a user’s scenario. The user answers questions and a pre-filled template is then generated. In addition, the software also features document filing and integrated e-signatures to streamline related manual processes involved in NDA drafting.Intellectual PropertySecuring patents, copyrights and trademarks is often best left to a lawyer’s expertise. However, the entire patent application process can be long and arduous. Traditional trademark and patent search, for example, involves looking into hundreds, if not thousands, of results through manual research. This takes so much time, which is ironic considering that patent applications are time-sensitive.According to US patent attorney Patrick Richards, “You only have one year from the first time the invention is publicly disclosed [i.e. sold] to file a patent application; if as a business owner you launched a product within the last year, you need to talk to a patent attorney right away to make sure it is protected.”TrademarkNowTrademarkNow is a company taking on some of the manual knowledge work of intellectual property application with AI. It uses a complex algorithm that is said to shorten weeklong searches for patents, registered products and trademark using the Trademark Clearance platform, which returns search results in less than 15 seconds according to the company’s claims.The system analyzes the results and ranks them according to relevance to the user as identified by the algorithm. As this many e-discovery applications, the solution promises efficiency of spent attention for legal teams.ANAQUA StudioThe cloud-based ANAQUA Studio, on the other hand, is specifically designed for drafting patents and prosecution. The company’s datasheet states that it’s the first patent application-drafting tool for lawyers that save four hours on provisional patent application and 20 hours and non-provisional types. The system is said to be able to detect document errors, circular claim references and formatting defects aside from automatically generating literal claims support.SmartShellSeattle-based TurboPatent released SmartShell to support paralegals performing document reviews, drafting, formatting and identifying issues on patent applications. The software uses AI and natural language processing to assist in creating legal claims.In a case study listed on TurboPatent’s website, two paralegals from the Pacific Patent Group used the software to perform document retrieval, bibliographic data research, examiner remarks review and rejection issues discovery. TurboPatent claims that Pacific’s paralegals were 500-800% more productive in their tasks when using SmartShell (thought the case study isn’t clear what exact tasks were relevant for the software, and which weren’t – we can assume that many paralegal tasks aren’t currently improvable with AI).A brief overview of how the product’s functions and value proposition can be seen in the 1-minute video below:Electronic BillingElectronic Billing platforms provide an alternative to paper-based invoicing with the goal of reducing disputes online items, more accurate client adjustments, (potentially) more accurate reporting and tracking, and reduced paper costs. Firms in the healthcare space are also utilizing AI for medical billing; this concept is further explained in our article Artificial Intelligence for Medical Billing and Coding.BrightflagBrightflag offers centralized legal pricing software that automatically adjusts line-by-line items. It also allows users to centralize the invoice review so that all documents submitted are routed directly to the correct approver. In addition, the AI provides analytics features by tracking and categorizing all pricing data to determine alternative fee arrangements (AFA) and budgets.The company claims that its average client reduces administrative costs related to payment management by 8 to 12 per cent by using the platform’s assisted review feature. The company lists telecom giant Telstra and ride-hailing company Uber among its current marquee clients.SmokeballSmokeball’s cloud-based legal practice management tool automates the recording of time and activities by law firms. One major feature of this tool is the capability to track all activities including emails that are valid for billing. It claims to have automated more than 600,000 forms and managed over 10 million documents according to its website. The video below explains Smokeball’s software: