Role of Artificial Intelligence in Claims Litigation

May 16, 2022

As artificial intelligence (AI) continues to disrupt many sectors, including the legal services industry, experts across the world foresee exponential development in AI as a critical technology for bringing new tools and features to improve legal services and access to justice. 

According to AI predictions research, legal tech solution providers will almost certainly adapt and incorporate existing AI technologies, transforming them into tools to assist legal firms in growing. This tendency will continue to make its way to the day-to-day work of lawyers and revolutionize the legal sector, with AI-powered judges, AI robot attorneys, and AI-powered features for contract or team management systems.

The capacity to automate simple and repetitive tasks like legal bill evaluation while allowing human specialists to enhance results beyond what machines or humans could do alone is the most promising characteristic of applying AI in the claims business. 

In this article, we'll look at how Artificial Intelligence may improve efficiency while saving time and money in the legal services industry.

Artificial Intelligence in Claims Management

Law companies may use AI-based software to automate lower-level duties, allowing attorneys to focus on more complicated analysis and client contact. An attorney's capacity to investigate, advise, and assist their clients can be dramatically enhanced by AI. 

AI-based solutions are already being used by certain global corporations to optimize their operations. According to the International Legal Technology Association's Survey, most law firms with 700 or more lawyers utilize AI tools or are working on AI projects. Firms that do not adapt to evolving technologies, particularly larger enterprises, will eventually find it difficult to compete.

Artificial intelligence will automate more aspects of legal practice in the future. In the next two decades, AI might automate more than 100,000 legal support duties, according to a study. Non-attorney legal careers will not be extinguished by AI. Instead, it will almost certainly provide new job opportunities in AI and machine learning, with almost endless possibilities.

Review of Litigation Documents and eDiscovery

One thing that all attorneys agree on is that the profession of law requires a lot of paperwork. Even a basic case can generate a plethora of paperwork, communications, and reports. Attorneys are required to evaluate all discovery materials in a case. If an attorney overlooks essential terminology or revisions, the results might be severe, even amounting to malpractice. Fortunately, artificial intelligence (AI) simplifies document inspection.

AI eDiscovery algorithms understand how a company evaluates documents and sorts out relevant phrases, themes, and other criteria. When AI software understands what to search for, it may recommend significant papers and areas of interest within the information. 

AI addresses two ongoing issues with document discovery: time and cost.

Aside from e-discovery and document review, which is perhaps the most common usage of AI among litigators for resolving claims, there are a few additional important areas in which AI might especially enable litigators to be more successful and efficient: Legal research, prediction of legal outcomes using data analytics, contract management, and creating automated legal arguments

Improved and quicker legal research

For decades, attorneys have employed text retrieval for legal research. It's time-consuming to look for relevant case law. General search phrases might return hundreds of case results, which is inconvenient for a busy lawyer. Because AI learns what an attorney needs, it optimizes search results. The AI tailors the more data and information an attorney supplies to reduce the scope. Instead of 1,000 possible instances with significant precedent, the AI may just present the attorney with a few of the most relevant. Furthermore, AI may continue the search even after the first query has been made. It's like having a legal assistant researching for a client at all hours of the day and night.

Legal result forecasting

"Do you believe I'd win this case if it got to trial?" and "Should we settle?" are two of the most typical queries clients ask attorneys. What is an acceptable answer? Attorneys make decisions based on their years of litigation expertise and knowledge of area judges and opposing counsel.

Artificial intelligence raises the bar on prediction. Identical instances with similar information may be analyzed by AI, which can then give a statistical analysis to reliably anticipate lawsuit outcomes. Attorneys may reliably advise clients on how and whether to proceed with litigation using this tool.

But is AI, however, accurate in predicting lawsuit outcomes? Over a year, a London legal practice examined data from more than 600 cases to estimate the feasibility of many personal injury lawsuits. This study found that Artificial intelligence outperforms human specialists in forecasting Court decisions.

Creating automated legal arguments

By simply uploading a complaint or discovery request and including jurisdictional criteria, AI is currently being utilized to produce responding pleadings, discovery answers, and associated documents. For example, a tool using an AI algorithm can create some of the more common responding litigation papers, such as replies and responses, as well as objections to demands for production and interrogatories, in only a few minutes.

Contract administration 

Contract management is essential for attorneys who deal with significant numbers of contracts daily. Artificial intelligence allows you to arrange, track, and negotiate contracts quickly and efficiently. AI gathers data over time to assist lawyers in concluding, developing future contract strategies, and uncovering new insights inside contract provisions. AI software gives lawyers more confidence in contract discussions, which leads to better results for their clients.


Finally, technology may help us improve results and allow individuals to resolve public disagreements in ways that were previously impossible. AI technology might be used in the future legal system to assist resolve conflicts without the need for attorneys or the existing court system. 

While this change may not resolve all issues with the legal system or access to justice, it can provide a significant improvement provided by AI as a service. It is possible that within a relatively short period, we will have systems that can forecast the results of court rulings based on previous decisions utilizing predictive analytics. Imagine instead of waiting for a court date (and the backing of the traditional legal system), individuals could utilize an AI & Machine Learning system that predicts a case's likely result and then accepts that prediction as a binding verdict.

Latest Blogs
This is a decorative image for Project Management for AI-ML-DL Projects
June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:,product%20on%20the%20right%20platform.

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.


The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.


In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.


Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:


Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 


As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online


As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 


Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.



Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 


An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.


In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.




This is a decorative image for Top 7 AI Startups in Education Industry
June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure