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Nasscom AI top 50 use cases

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Nasscom AI top 50 use cases

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AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics

AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics

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Nasscom AI top 50 use cases

  1. 1. Compendium of Best AI Solutions Use Cases Top 50 AI Game Changers
  2. 2. Foreword If there is one technology that has disrupted every aspect of human existence it will have to be Artificial Intelligence. AI has pervaded across every industry, every country, and every sphere of life. It is transforming businesses, economies and engagements across the world. India is uniquely positioned to gain immensely from this prospect as we take huge strides to find our place in the sun. The Government of India too has recognized this game changing phenomena and has crafted a comprehensive strategy for building a vibrant AI ecosystem in India. To showcase and recognize the innovative, high impact and hi-tech AI solutions that organizations have delivered from India, NASSCOM Centre of Excellence for Data Science & Artificial Intelligence (CoEDSAI) launched the first “NASSCOM AI GAME CHANGER AWARDS 2018” We received an overwhelming response with over 300 use cases and after a stringent process of evaluation, the esteemed jury shortlisted the best 50 top use cases which is presented in this compendium. We are highly encouraged by the depth and breadth of use cases covered, be it in the highly evolved area of BFSI or niche areas like fraud detection, smart policing and healthcare. What is heartening to note is that the innovation, tech stack and the implementation approach followed by these firms are highly competitive and adhering to global standards. We can confidently say that AI can accelerate growth not only for the industry but for India by addressing bottlenecks in efficiencies, providing quality healthcare, education and improve the overall well- being of the nation. We hope that these use cases will help the reader envisage a clear picture about the immense potential and opportunity that AI solutions has created not in labour and cost savings but in actual tangible growth. Happy Reading! Debjani Ghosh President, NASSCOM
  3. 3. Objective of the report 3 To showcase and recognize the innovative, high impact and hi-tech AI solutions that organizations have delivered from India, NASSCOM Centre of Excellence for Data Science & Artificial Intelligence (CoEDSAI) launched the first “NASSCOM AI GAME CHANGER AWARDS 2018”. We received an overwhelming response with over 300 use cases and after a stringent process of evaluation, the esteemed jury shortlisted the best 50 top use cases. This report is a compendium of the Top 50 AI Game Changer Solutions. It covers the best use cases we received, applicable across verticals and horizontals. The purpose of this compendium is to restate the growing significance and impact of AI applications and to ascertain India as a emerging hub for innovative and transformational AI solutions and investments.
  4. 4. Table of Contents Click to Navigate Glossary 13 AI Basics Top 50 AI Game Changer Solutions Horizontal Solutions Vertical Solutions Advanced Analytics18 Conversational Bots27 Quality & Security56 Financial Services34 Healthcare38 Insurance44 Manufacturing50 Retail60 Social Impact65 Travel & Logistics69 Miscellaneous74 80 5
  5. 5. 5 AI Basics
  6. 6. What is Artificial Intelligence (AI) Source: NASSCOM, Expert System, McKinsey & Co., SAS 6 Artificial Intelligence Ability of machines to perform functions similar to that of human mind like perceiving, learning, and problem solving Machine Learning Machine learning refers to ability of computer systems to improve their performance by exposure to data without the need to follow explicitly programmed instructions Deep Learning Supervised Learning Unsupervised Learning Reinforcement Learning In Supervised Learning, the machine is trained on data which is labeled and tagged. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly. Ex: Regression Analysis In Unsupervised Learning, data used by machine is neither classified nor labeled allowing the algorithm to act on that information without guidance. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Ex: Clustering Analysis Reinforcement learning is more of an experience based learning in which decisions are made sequentially. In this, the learning method interacts with its environment by producing actions and discovers errors or rewards. A type of machine learning which sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using multiple layers of processing AI is the need of the hour for efficient and effective industrial , economic and social growth AI promotes innovation which is must for the growth in today’s era AI enhances workforce skills and abilities making them to be more powerful AI helps automating complex solutions intelligently for better efficiency
  7. 7. Source: PWC • Natural language • Audio and Speech • Machine vision • Navigation • Visualisation • Robotic process automation • Deep question and answering • Machine translation • Collaborative system • Adaptive systems • Knowledge and representation • Planning and scheduling • Reasoning • Machine learning • Deep learning AI that can sense… Hear See Speak Feel AI that can think… Understand Assist Perceive Plan AI that can act… Physical Creative Cognitive Reactive Statistics Econometrics Optimisation Complexity theory Computer science Game theory Foundation layer What can AI do? 7
  8. 8. Artificial Intelligence (AI) simplified Source: Dealroom.co 8 Artificial Intelligence Expert systems Planning Robotics Machine learning Natural language processing Vision Speech Deep learning Supervised Un-supervised Content extraction Classification Machine translation Question answering Text generation Image recognition Machine vision Speech to text Text to speech
  9. 9. AI Stack built on data and insights AI infrastructure Cloud Mobile Big Data Internet of Things AI Applications Intelligent Automation Cognitive Systems Deep Learning Machine Vision Robotics Social AI-Enabled Industries Advertising Aerospace Agriculture Automotive Education Energy Finance Transpor- tation Technology Retail Manufactur- ing Media Legal Data Insights Data Insights Health care 9Source: PWC
  10. 10. Different forms of AI, varied applications • Speech recognition • Handwriting recognition • Optical character recognition • Image and video recognition • Facial recognition • Speech synthesis • Natural language generation • Robotic process automation • Control of other systems through APIs • Case-based reasoning • Expert systems • Recommender systems • Data mining • Deep learning • Reinforcement learning • Unsupervised learning • Supervised learning • Natural language understanding • Machine translation • Sentiment analysis Source: BCG analysis 10
  11. 11. Recent AI predictions The artificial intelligence market will surpass $40 billion by 2020 – Constellation 100% of IoT initiatives will be supported by AI capabilities by 2019 – IDC AI will drive 95% of customer interactions by 2025 – Servion 30% of companies will employ AI to increase at least one primary sales processes by 2020 – Gartner 75% of developers will include AI functionality in business applications or services by 2018 – IDC Algorithms will positively alter the behaviour of billions of workers globally by 2020 – Gartner 11Source: PWC
  12. 12. Top use cases by function Customer Service SalesMarketing • Retargeting • Recommendation personalization • Social analytics & automation • Predictive sales • Sales data input automation • Sales forecasting • Customer service chatbot (e2e solution) • Intelligent call routing • Call analytics • Analytics platform • Natural language processing library/SDK/API • Analytics & predictive intelligence for security Healthtech Fintech HR IT Operations • Patient data analytics • Personalized medications and care • Drug discovery • Fraud detection • Financial analytics platform • Credit lending / scoring • Hiring • Performance management • HR analytics • Robotic Process Automation (RPA) • Predictive maintenance • Manufacturing analytics Source: Appliedai.com 12
  13. 13. 13 Top 50 AI Game Changer Solutions
  14. 14. Top 50 AI Game Changer Solutions (1/4) 14 Advanced Analytics Insights from unstructured data Translate data into meaningful insights Extract unstructured data for informed decisionsAI NLP for intelligent sales & marketing Brand exposure analysis in broadcast & streaming content Nia-Intelligent contract analysis EXACTO-Automatically extract information from a variety of sources Smart Insights- analytics platform for connected vehicles Conversational Chatbot Conversational Bots Conversation UX Bilingual voice BOT for intelligent conversations Conversational AI platforms Call centre analytics using conversational AI Intelligent online chat platform
  15. 15. Top 50 AI Game Changer Solutions (2/4) 15 Financial services Checking corporate governance standards and ethics of firms Payments transaction visibility Financial crime management and risk governance Cardiac care platform Prioritizing head CT scans Remote ECG diagnosisCuff-less blood pressure monitoring Virtual hospital assistant Healthcare InsuranceClaims processing Property damage estimation Real-time flight delay compensation Analyzing car images & automate insurance claim process Identification of rooftop damages using drone images
  16. 16. Top 50 AI Game Changer Solutions (3/4) 16 Manufacturing BOLTTM – Enhancing last mile productivity for field engineers Cerebra- Dynamic quality control of industrial finished goods Manufacturing-Plant floor data into insights Sound analytics for real-time quality monitoring Proactive sensing of quality issues in automobiles Real-time behavior detection monitoring suspicious activityCommodity grading & quality checking Automated and standardized grading inspection system for agri-products Quality & Security Retail Product attribute extraction from images Product discovery and visual search for apparels ignio™ -tech infrastructure support during peak holiday season Counterfeit products detection
  17. 17. Top 50 AI Game Changer Solutions (4/4) Social ImpactSmart policing Citizen Engagement Solution Automatic Number Plate Recognition 17 Transportation & logistics Truck freight price prediction Rail track fault detectionFleet and driver safety platformLogistics optimizations Miscellaneous Learning videosAutomated Speech Recognition Low cost embedded devices with high end computational capabilities Predictive analytics for telecom network IKON-A cognitive engine for incident management
  18. 18. 18 Advanced Analytics
  19. 19. IMPACT PROBLEM ROI tracking on advertisements displayed on LEDs in the sports arenas  The audience is in millions for broadcast media compared to thousand in-stadium fans.  The sporting action has several cameras with final feed on the TV being for a fraction of seconds.  Hard to manually count and measure brand exposure of logos when seen on TV broadcast also by the type of asset on which they are displayed  Calculating the pricing for placing ads depending on visibility and net viewership. SOLUTION Applying AI and computer vision to track the brand logos appearing dynamically on the TV broadcast  Brand exposures are accurately tracked with their visible time, when and where they appeared and their size.  The raw metrics are translated to dollar values by weighting them with TV viewership numbers and their demographics.  Available via online dashboards to sponsors, asset inventory owners to enable them to price the ads using these metrics. Developing trackable models processed on the cloud  Trackable catalog of trained brand logos under various sizes and for asset types (t-shirts, LED, ground, bat etc.).  Develop deep learning models for identifying brand logos from complex, fast moving action and mining various statistics.  Provides detailed drill-down dashboards with analytics and insights.  Ingesting and processing the broadcast video and in the cloud for fast response times 19 Brand exposure analysis and RoI tracking in broadcast and streaming content We trialled out broadcast and social media monitoring services across our IPTL event for the leg held in Gachibowli, Hyderabad, 2016. A major benefit to working with this ROI Tracking solution was having access to their analytics and insights dashboard which really helps find exactly what you are looking for as well as discovering information you didn’t know existed. Most importantly, the Drive Analytics team in partnership with Global Sports Commerce, serviced our needs quickly and professionally, they can always help and provide valuable guidance on best practice for media monitoring - Vaibhav R, Head of Marketing and Sponsorship for IPTL
  20. 20. EXACTO- Information extraction tool for handwritten and image based documents SOLUTION IMPACT PROBLEM Today’s organizations are expected to deliver seamless consumer experiences to compete in the ever-changing digital business landscape. EXACTO enables dynamic requirements of businesses for our clients by giving 98% accuracy and partnering with clients in the areas like trade processing, medical document triage, contract processing, invoice & check processing and KYC. – Anoop Tiwari, Corporate Vice President and Global Head – Business Services, HCL Technologies. Extracting language based objects from unstructured data  Extracting handwritten or typed data which may exist in isolation or embedded within an image, that are unstructured and diverse in nature  Average quality scanned or faxed images are processed manually with some degree of automation leveraging traditional OCR system but doesn’t yield high efficiency EXACTO, an AI/ML based scalable extraction solution with active learning capability  Information extraction tool for classifying and reading handwritten and typed fax/image based documents captured by standard scanner or mobile devices  Domain expertise in areas like trade processing, and medical document triage  Computer vision for image processing, deep learning for digitization of content and NLP for semantic data points extraction from given sample  Improves the input document quality by removal of noise and sharpening the document. High accuracy and reduced manual effort  Automatic document classification & text extraction for comparing the trade between buyer & seller with over 99% accuracy.  Automated data entry and validation of invoices to improve customers service and vendor partners agility.  Automate handling of medical prescriptions with payers and providers in Healthcare. 20
  21. 21. IMPACT SOLUTION PROBLEM Verify high volume of contracts and policy documents in stipulated time  Need to verify high volume of contracts.  Turn around time expected around 3-4 days  Ensure exhaustiveness and zero tolerance to any inaccuracy Nia Contracts Analysis, uses natural language to read contractual documents  Uses machine learning architecture to enable and read contractual documents the way humans would.  Converts natural language into a computable format to maintain semantics and context.  Uses pre-trained models to help expedite its usage in real-life scenarios. Benefits of compliance, agility, visibility and accuracy  Automatic extraction of contractual information saving over 30,000 person hours a year.  Contract interpretations are standardized and helps in early identification of risks. 21 Nia: Automated and ‘intelligent’ contracts analysis solution Infosys has done a very good job in taking a concept, vision for labour agreements, that we had a very vague idea about and achieving current state where the system is ready to be used by expert users in 4-5 weeks. We are no longer terrified about Artificial intelligence. – Client: Pharmaceutical MNC, France
  22. 22. IMPACT PROBLEM SOLUTION Digitalizing legacy documents from unstructured scanned documents  Daunting tasks of digitalizing legacy data required to improve operations.  Efficient use of existing inventory. AI solution which assists the manual process in meta data extraction  Cognitive solution which extracts metadata from scanned documents.  Different type of documents in pdf format provided by client like well logs and seismic logs.  A self-learning system that autocorrects and draws rules from human feedback.  Customized models for extracting text attributed to extracting data using natural language processing.  Based on AI trained models the words are spell-checked, fields are extracted and de-duplication of text takes place Increase in accuracy and reduced extraction time  Reduction in costs due to automation of manual tasks by 15-20%.  Multifold increase in accuracy and reduced extraction time helpful to make informed decisions.  Detection of unique sections across documents for better retrieval and easier management. 22 Digitalize legacy data and extract unstructured data for informed decisions
  23. 23. PROBLEM Smart Insights- An analytics platform for connected vehicles Optimizing automated machine learning model  Costs incurred due to warranty claims had a high negative impact on the bottom-line.  Minimize warranty claims by correlating vehicle usage/driving styles with expensive and severe claims.  Major challenge to derive relevant insights by merging complex data across different dimensions. SOLUTION SMART INSIGHTS, a scalable and self-service code-free platform for analytics on connected vehicles.  Scalable and self-service code-free platform for analytics on connected vehicles.  Platform empowers SMEs/ business users to identify & characterize different driving styles, test product hypothesis & correlate them with warranty claims  Built on IoT sensor data from cars, warranty claims & other vehicle information. IMPACT Analytical insights like  Significant reduction of warranty claims due to proactive drive-right messaging and preventive maintenance.  20% of total cars exhibited a short trip & long pause driving behavior indicating 40% higher risk of engine related defects.  Vehicles that spend 100% more on pedal position are at a 250% risk of engine related defects. 23
  24. 24. PROBLEM Marlabs’ platform based approach has helped us to be at the forefront of AI Innovation and has helped our customers transform their business and realize exponential gains. – Siby Vadakekkara, CEO, Marlabs Inc mAdvisor: Uncovering hidden stock investment insights from unstructured data Difficulties in identifying stocks investment opportunities to produce high returns  Lack of time and effort from research analysts.  Lack of highly skilled equity research analysts.  Consistency and adherence to quality of research and analysis. SOLUTION mAdvisor, an NLP-based research analytics solution, automates the traditional process of equity research analysis, analyzes a multitude of data sources to determine the likelihood of delivering high returns  Comprehensive analysis to determine probability of the stock becoming a winning investment.  Deep rooted analysis on each quantitative and qualitative attribute that impacts the overall investment return. IMPACT Validates and identifies stock projections  Ability to validate rigor of research and compliance of assessment with a 6-criteria investment philosophy.  Ability to identify over-ambitious and too aggressive forward projections instances.  Reduced time of over 40% taken by equity research analysts and portfolio management teams to build an investment case. 24
  25. 25. IMPACT SOLUTION PROBLEM Concerns faced by enterprises in the markets they operate in:  Dedicated market analysts needed to procure information on prospects, clients, competitors, industry trends and more  Process of data assimilation needs to be backed by verification, sorting and tagging  Process resource dependent, inefficient, & not easily scalable to support new initiatives by marketing team Marketing Assist, the enterprise AI assistant helps with relevant information to support marketing and sales activities  Works with structured and unstructured data sources to return consumable information based on natural language queries.  Integrates with internal data repositories and subscribed data sources to fetch information in real time across company, people, industry and other categories  Self learning & customised to give proactive recommendations to support specific sales/marketing activities targeted by account manager or user  Reduction in time taken by analysts to build custom reports on companies and product markets  Made the process of consumption of custom information by marketing more intuitive and efficient  Save time and money  Helps scale marketing strategies easily with real time, relevant insights Powerful NLP algorithms backed with Neural networks are the key to different stake holders having meaningful conversations with enterprise structured and unstructured data and we are right in the midst of it – Sanjeev Menon, CEO. Light Information Systems 25 Marketing Assist: NLP for intelligent sales & marketing
  26. 26. IMPACT PROBLEM Global Top 50 Consumer Goods Company with portfolio of health and hygiene consumer brands • Received millions of customer feedback from multiple comments/reviews/posts across sources which contain rich actionable insights. • Due to the unstructured nature of huge volume of text, difficulty in extracting valuable actionable insights related to product/service innovation, marketing optimization and strengthening the competitive differentiation Custom taxonomies and high-quality training data resulted in accurate and actionable insights • Decision clarity regarding strategic brand positioning  Influence both short-term and long-term adjustments in R&D and new product development  Drive tactical change including product innovation, packaging and user guides  Identify sources of competitive differentiation, unmet needs of target customers and white space in the industry From all the companies screened on this field, we selected SetuServ. They have developed a specific focus in this field, and despite being a start-up, they have most advanced technology for this specific task. – Customer 26 SetuServ applied its proprietary human plus artificial intelligence solution analyzing the data  Gathered over 500k comments/reviews/posts across sources  Created a custom taxonomy of 200 topics to handle full corpus of data for each brand;  Trained separate multi-level AI models for each source SOLUTION Actionable insights derived from unstructured multiple data channels
  27. 27. Conversational Bots 27
  28. 28. Conversational AI for call centre analytics and improved CX SOLUTION IMPACT PROBLEM The client, a major US property & casualty insurer aims to improve customer experience during call center interactions  The visible problems being high call handling wait times.  Need for advanced self-service features avoiding customers to call for simple status updates.  Lack of proper call transcription and offline review with very less calls reviewed for feedback.  Hidden problems include lower satisfaction and high attrition.  Scale as well as quality suffering at important moments of engagement AI solution using call center analytics, process redesign, and self-service, guided by a human- centered understanding  Analyzes historical call records and classifies historical patterns to train AI to improve real-time call transcription.  Recognizes caller and center agent within 30 seconds with customized emotion-sentiment score to aid center agent to determine best course of action.  Guides the center agent to assess checklist with high quality using parsing of real-time call transcript.  Modularized the design to be ported to other client call center operations after the initial proof of value. Automated assessment of every call providing improved customer visibility  The cost savings exceeded $2 Million per year with improved customer satisfaction and automated assessment of every call  Reduced call length by 30%, reduced total labor costs by 15%, and converted 10% of the formerly negative ratings into positive sentiment  Improved visibility into customer needs and trends. AI added intelligence to existing business processes while creating opportunities for warmer, relevant, and satisfying customer experiences. All parties, from the customer to the call center agent, benefitted from this integration of AI to enrich the call experience. - Karthick Krishnamurthy, Head Digital Business, Cognizant 28
  29. 29. PROBLEM Utility product in a conversational format  Industries looking out for engaging product that can provide utility in a conversational format.  Boost engagement on existing mobile platforms with an all-in-one service  Make use of messaging as a communication tool aiding increase in retention rates SOLUTION A hybrid model chatbot with multiple utility features  An SDK that contains multiple chatbots instantly embedded into any app or web client with a memory footprint under 1 MB.  The entire roster of chatbots includes over 40+ bots that offers everything from reminders to flight/cab bookings to bill payments to jokes.  Chatbot NER (Named Entity Recognition), a heuristic based subtask of information extraction that uses several NLP techniques to extract necessary entities from chat interface. IMPACT Personal assistant embedded in an app increased retention and engagement rates  Upto 60% higher retention  Increase in impressions is higher by 35.4%.  increase in engagement is higher by 31.6%  Higher automation in terms of chat response upto 50% to 95% 29 High user engagement using high-utility AI-powered bots The Personal Assistant is one of the key integrations we’ve done on the app. With early results showing 60% increase in retention rates for Assistant users, they’ve definitely taken a liking to the chat based virtual assistant powered by Haptik. - Product Lead, Mobile Apps for the Client
  30. 30. 30 EVA: AI/ML powered intelligent virtual assistant PROBLEM SOLUTION IMPACT Need to enhance customer assistance  Customer required to navigate multiple pages on the website or call phone banking for any product related queries  Huge cost incurred for answering routine queries EVA, an automated customer engagement online chat platform was created  EVA to be first point of contact for all customer queries.  Answers routine customer queries in conversational manner  AI & NLP was used for the first time within the bank  EVA skills were extended to Amazon Alexa, Google Assistant, Humanoid Robot Enhanced user experience and customer delight  EVA to be first point of contact for all customer queries.  EVA answering 0.5 million queries monthly with 89% accuracy level  Generic queries from other channels reduced  Enhanced user experience
  31. 31. IMPACT PROBLEM SOLUTION Making IVR relevant and reach the masses  Need to reach out to semi urban, rural and semi literate callers.  Reduce customer’s time spent on the IVR.  Easy navigation of options.  Method to reduce lengthy phone menus.  A quick & easy self navigation tool for queries/request/transactions on IVR. Deployed an AI-led voice bot to provide enhanced customer experience to customers  Shortened call time by routing callers faster.  Reduced misroutes to minimize incremental costs.  Improved automation rates by limited hang ups.  Adapted self-service applications, identified new ones. AI-led voice bot scored better across relevant parameters  Covered 65 use cases and 40% of total calls.  83% customer rated positively to KEYA’s ability to steer them correctly.  Reduction in time spent on IVR by 60 to 120 seconds per use case.  KEYA recognizes 80% intents accurately.  Self Service on the IVR has improved by 10% over 2 months. Keya has redefined customer experience in the banking industry. Despite alternative customer service channels, voice continues to be the preferred medium of customer communication and Keya’s bilingual and personal approach helps understand the customer’s intent, accent and helps them navigate to their desired output. Customers no longer have to go through the hassle of inputting feed into their dial pads and saves time because Keya gets issues resolved in a single interaction through intelligent conversations. - Puneet Kapoor, Senior Executive Vice President, Kotak Mahindra Bank 31 Keya: Bilingual voice BOT redefining customers’ phone-banking experience
  32. 32. Customer support chatbot for India's largest private sector bank 32 IMPACT Chatbot handles 25,000 queries everyday from 10,000 unique users, with instantaneous response times and saving the bank 350-700 customer service resources.  Reduction in operational cost and improved Customer Experience.  Efficiency: 30%; Number of User Queries Resolved: 25k everyday ,  4+ Million till date Accuracy: 86%  Uptime of the Bot: 99.9% PROBLEM Difficulties faced by bank’s customer support staff  Customers exceeding over 30 million and adding approximately 100K new cards every month.  Approximately 350-400 new customer service agents required to handle growing customer base. SOLUTION Proposed AI solution  A humanlike conversation platform powered by AI which can address queries, resolve issues, perform tasks  Drives bot platform for taking up all customer queries on the website and other touch points.
  33. 33. 50,000 questions answered with positive feedback has helped in saving up to 5 mins of exploration per question.!! In this entire process, the system should not expose data outside the IT network of the bank as it may have sensitive information. – Bank customer MAX – Conversation UX interpreting intent and natural language IMPACT SOLUTION  MAX is a conversational agent that interacts with human actors in natural language either through text or speech and help to fulfill their objective.  The end user needs to express itself in its natural language and the systems interprets this expression and provides a suitable response.  Uses deep learning algorithms, Max interprets intent of the customers, extracts relevant information from expressions and helps in completing tasks by connecting to bank applications.  Reduced time by 30 mins to originate a new deal  Improved engagement with employees for the organization policies.  Enable front officer’s to perform daily activities with increased productivity.  Reduced the backlog of calls and emails to HR business partners drastically.  Automated response to instantaneously help employees’ HR related queries PROBLEM  An intelligent conversational agent to be developed that can interact with human actors in natural language either through text or speech and help the actors to fulfill their objective. In this entire process, the system should not expose data outside the IT network of the bank as it may have sensitive information.  Improve customer User Experience (UX) 33
  34. 34. 34 Financial Services
  35. 35. Financial crime management and risk governance SOLUTION IMPACT PROBLEM Ways to control financial crime management and effective risk governance  A robust infrastructure for automated fraud case management  Fraud risk governance to timely and accurately control fraud risks  Standard storage of news & retrieval system for future references & analysis. AI solution implemented using NLP, similarity analysis, named entity recognition  Capturing secondary information in the form of unstructured data (news), pertaining to financial crime, AML & correspondent banking to compliment the current STR (Suspicious Transaction Reporting) filing process and disseminating as threat Alerts to Business Units  Specific targeted threat alerts with minimal spams (Spam ratio - 0.4%)  Standard storage of news & retrieval system for future references & analysis Acts as a ready reckoner for regulatory submissions  Increment in trigger reviews of upto 50% with critical nature of AML violations recorded in Q4 FY 2017-18.  Robust Infrastructure for storage & retrieval leads to better analysis & due diligence.  Automatic quality alerts are generated which helped FCMD-CB. 35 The exercise by your team is helping us with a repository of all the necessary inputs, in a most comprehensive manner. This has allowed us to be in a position of no-fish-skips-the net. – VP, Financial Crime Management
  36. 36. SOLUTION PROBLEM This artificial intelligence solution helps address the classic ‘Breadth vs. Depth Dilemma’. AI analyses petabytes of data and identifies right patterns/ information that might be obscure to the human brain. This would free up the analyst’s time to investigate key corporate governance issues which matter for investment decisions. – Praveen Sangana, Asset Management Business Need for accurate and timely corporate governance checks for making investment decisions  Select high-quality companies for investment very important.  Accurate and timely corporate governance checks for investment decision. Solution to select timely and accurate investment opportunities having highest level of ethics and corporate governance standards  Created an automatically updateable and searchable knowledge graph using named-entity recognition to extracting relationships among entities otherwise hidden in news articles.  Use of recurrent neural network trained to use semi-supervised approach via data generated using clever heuristic model.  Innovative and unique use of topic modelling, text summarization and sentiment analysis to slice and dice information and ease cognitive burden. IMPACT Ability to focus on important and personalized information  Focuses on high–value, personalized and specific information.  Manages and discovers relationships among people and companies.  Ability to ascertain key semantic and syntactic difference between documents Automated checking of corporate governance standards and ethics of firms 36
  37. 37. IMPACT PROBLEM SOLUTION BFSI client operating one of the largest retail payment applications like Aadhar Enabled Payment Systems(AEPS) and RuPay card transactions in the country.  Faced severe performance issues, transaction failures/losses  Application availability and performance with strict SLAs  Multi tier architecture of applications with complex and high transaction volumes  A proactive and continuous intelligence system to improve service levels and user experience Integrated vuSmartMapsTM, a big data and ML based platform, powered by an innovative engine vuSmartMapsTM to client’s application environment  Platform uses a combination or vector of multiple algorithms best suited for a single issue  Involves a variety of unsupervised ML techniques for anomaly detection, with correlation based on temporal, topology, transaction id and meta data tagging.  Innovative compound alerting framework which uses temporal correlation in identifying anomalies across an application service dependency map  Uses an innovative English like business rule framework built on noSql database.  100 % unified coverage cutting across business transactions, application performance & infrastructure metrics  Cost optimizations by more than 50%, 33% improvements in productivity, 70% faster troubleshooting  Reduction in alerts and faster MTTD (Mean time to detection) “We were extremely impressed with the end to end business transaction visibility in real time and correlation across transaction legs for our Aadhar Enabled Payment Systems and RuPay Systems. It has helped reduce our incidents by more than 30% and has helped us give a better end user experience, which is a big differentiator for us in this digital world” – VP, Applications, Retail Payments. 37 Enhanced user experience, end to end business transaction visibility
  38. 38. Healthcare 38
  39. 39. We have found diagnostics capability (of Cardiotrack) reliable and stable; AI interpretation are quick and precise; portability lending ease of use in tough/ remote environments – Dr. Dina Shah, Additional Director, Emergency Department, Fortis Hospital Noida. Early and accurate diagnosis of cardiac health conditions IMPACT PROBLEM Access to quality care for cardio vascular diseases (CVD) among the non-urban population • There are 60 million people suffering from cardiovascular disease in India, only 10 thousand cardiologists to attend to them. • Early and accurate diagnosis key to prevent death  Lack of proper diagnostics capability outside urban centers.  Specialist cardiologists available only in top 25 cities in the country. SOLUTION Cardiotrack, a cloud based IoT and AI based solution aids in interpreting the ECG scan and sends it to the primary care physician in less than 5 minutes  Perform complete heart health check-up at any primary healthcare clinic by a nurse.  The results of AI interpretation delivered to primary care physician in less than 2 minutes.  Compares patient’s ECG scan record with a database of 500 thousand ECG scans reviewed and annotated by cardiologists.  The neural network AI engine performs comparison and can identify 200 different heart anomalies.  This information is received by primary care physician to address and guide the patient through next steps. Accurate and early detection of critical heart health condition in non-metros  It has performed more than 50,000 ECG scans since Sept 2015.  Has identified more than 1,000 patients saving many a lives with early diagnosis  Its capability expands to tier-2 or tier-3 cities  Has diagnosed more than 10 thousand patients with non-critical heart health problems. 39 .
  40. 40. PROBLEM Healthcare has been depending on analytics for long, we at Praktice.ai are bridging the gap between this analysed data and actual action by real-time automation of hospital operations. Our vision is to automate all the pre and post consultation interactions between patients and hospital using AI and ML. – Srinath Akula, CEO Virtual hospital assistant for enhanced patient engagement  Huge operational cost: 20% increase year on year in the billions of dollars spent on hospital operations staff like call agents, chat agents, patient coordinators, etc  Revenue Loss: Due to lack of medical context & medical understanding, staff are only able to capture data related to 4% of the patients engaged, guide pre and post consultation, thus missing leads IMPACT  7x growth in patient engagement: from 2% engagement rate by patient support staff to 14% by AI assistant in just 1 month  Saved cost of 12 medically trained patient support staff  15k man hours saved so far SOLUTION For Hospitals like Apollo Hospitals, Parkway Pantai, Singhealth above problems are resolved by:  AI hospital assistant which autonomously performs patient interactions and transactions driven by medical triaging and medical natural language understanding 40
  41. 41. IMPACT  Time to diagnosis decreased significantly  Better volumetric measurement of lesions  Second opinion in case of trainee radiologists 41 qER: Prioritizing head CT scans by detecting emergency findings This is important new technology, the strong results of the deep learning system support the feasibility for use of automated head CT scan interpretation as an adjunct to medical care. This improves the quality and consistency of radiologic interpretation. – Dr. Campeau, M.D., Sr. Neuro‐radiologist, Mayo Clinic's Department of Radiology PROBLEM  Head CT scans of patients with brain hemorrhage need to be evaluated immediately  However, radiologists evaluate head CT scans on first-come-first-serve basis  Productivity of radiologists is hampered since there’s no way of automated prioritization  For critical cases relying only on the readings of trainee radiologists could potentially lead to adverse outcomes SOLUTION qER, head CT scan interpretation software, identifies critical abnormalities, localizes them to aid diagnosis, prioritizes scans that need immediate action, and facilitates decision‐making in remote locations without an immediate radiologist availability  Deep Learning to detect scans with emergency findings  Streamlining the radiologist workflow by prioritizing these scans
  42. 42. IMPACT SOLUTION PROBLEM Continuous monitoring of BP can result in prevention of fatal cardiac events however, this is often not possible using a cuff based BP equipment. As the focus shifts from hospital-centric healthcare approach towards patient-centric one, smartphone based (HRM sensor) BP estimation approach will be highly useful.  Data acquisition from HRM sensor  Pre-processing of raw signals  Feature extraction : Physiologically relevant to cardiac cycle having information about systolic and diastolic BP  Multiple BP predictions using a machine learning approach (ANN/DNN).  Robust outlier elimination method.  Deployed in Google Play store as an Android application named InstaBP  Compatible with smartphones having HRM sensor Non-invasive monitoring of BP is a much-needed requirement today for efficient management of cardiac health.  Uses existing HRM sensor already available in smartphones  Monitor Blood Pressure on the go/on-demand, without a need to go to clinic.  Easy tracking of BP trends over a long period of time 42 The application has been uploaded on Google Play Store, and has a rating of 4.2 (as of 14 May 2018). There have been positive reviews from the users. Also, when the app was tested on volunteers, most of them felt that the predicted BP was close to their actual BP, which was then confirmed by a cuff-based device (which is still a ‘gold- standard’, de-spite its portability issues) InstaBP : Cuff-less, non-invasive blood pressure monitoring using smartphone
  43. 43. PROBLEM Electrocardiograms (ECGs) are the primary means of diagnosing serious heart conditions like heart attacks. Reading ECGs require the skills of a cardiologist or an experienced physician  Misdiagnosis and delayed diagnosis is rampant across India & the developing world  Lack of trained expertise for early diagnosis of the disease remains a key unsolved problem  An inexpensive way of delivering accurate ECG diagnosis to all areas, including remote places Tricog is on a mission to save a million lives, by combining the best of medicine and AI. Through this journey, we are creating the largest digital database of 12-lead ECGs and world’s best ECG diagnosis platform – Dr. Charit Bhograj, CEO 43 Instantaneous remote ECG diagnosis SOLUTION Developed an inexpensive system of delivering accurate and instantaneous remote ECG diagnosis  Tricog Cloud where proprietary algorithms first analyze the ECGs transmitted from cloud connected ECG machines placed at remote centers  Provide the preliminary interpretation to the in-house team of cardiac specialists who are present 24/7/365 at Tricog’s centralized ECG Analysis Hub  Physician verifies the diagnosis from the algorithm and creates a final report, which is returned to the remote center within minutes.  Upon detection of a critical condition, the medical team alerts the remote center and, if required, facilitates transfer to a neighboring partner hospital IMPACT Since 2015, Tricog has analyzed over a million ECGs with 45% being abnormal and 4% being critical cases  Average ECG analysis time being reduced by over 20x over this period  Monthly ECG load has increased by over 400%  Company monthly revenue has grown by 300%  Limiting the medical team growth to less than 25%.
  44. 44. Insurance 44
  45. 45. Deep Learning is one of the major break through in recent times. It can transform the current software 1.0 as we see in key industries like Banking, Insurance, Lending, IoT etc. We wanted to be a major part in making our customers to be ‘AI’ first in production using our easy to use workbench VEGA and stand- along modules like Automated Claims Processing. Able to process a claims in less than a second will change how Insurers are operating today. We are glad to make deep learning as a core in re-imagining the financial services ecosystem. – Vinay Kumar, CEO & Founder IMPACT Problems with health insurers claims  Doctors employed in processing claims resulting in high operational costs, processing time and loss in value through frauds.  15 to 20 cents spent on every premium dollar in operations like claims processing  Manual processes lead to 6% to 12% claims leakage and over $120bn loss through frauds globally  Health insurers looking for advanced technology to optimize claims processing by automating processes and enhancing efficiencies PROBLEM SOLUTION Vega an end-to-end deep learning platform to automate complex claims  An ‘Automate Claims Module’ using Arya’s platform to automate the complex claims process built on ‘Vega 'an end-to-end deep learning workbench.  Built on neural network and dynamic DNN, does not require manual feature engineering or rules to be incorporated  Offers hybrid cloud environments provisioning insurer to train on-cloud and scale on-premise  Module can deduce the reasons, used primarily when a claims needs to be rejected. Drastic reduction in claims processing time  Time to process the claims is reduced from 48 hours to less a second.  More than 92% of claims are automated using 'Straight Through Process'.  Reduction in claims operational costs by more than 30% within first quarter.  Enhanced risk scouting with Recall improved by 40%. 45 Vega: Claims processing time reduced to less than a second
  46. 46. 46 Automated identification of rooftop damages to settle insurance claims Using Drone images to review and settle claims  A web application takes drone images as input, connect with historical data and interacts to help stakeholders to review claims and settlement them.  The raw images captured by drones are pre-processed and used for deep learning algorithms and core image processing techniques trainings.  The method involves breaking the image into tiles using classification technique with core image processing methods to localize the defects.  The defect size and count is measured in the whole image and displayed in PNG/JPEG/JSON format.  The results are written to the database with a unique identifier SOLUTION The client, a leading property/casualty insurance in the United States is incurring losses due to existing and future damages to rooftops  Damage caused to rooftops by weather events or pre-existing damage that allow water intrusion, resulting in damage to a property’s interior  Manual inspection of roof-tops to assess damages for insurance claims are cost-prohibitive  Use of drones to capture rooftop images to allow off-site inspection by adjusters and underwriters.  Need to automate identification of damage and quantification of costs from the images captured by drones. PROBLEM IMPACT Algorithms with high accuracy measure  The present implemented algorithms is trained with external web data with an accuracy measuring more than 95%.  With more than thousand images per class it is expected to improvise all the applied metrics.  The feedback from human users help to increase system accuracy, classifying damages, estimate repair costs and suggest parameters for claim renewal.
  47. 47. Analytic tool for detecting car-damages and automate insurance claim processes 47 PROBLEM SOLUTION IMPACT Automating the insurance claim process for automobiles • Analyze image data to quantify the damage on vehicles from images of damaged vehicles.  Automate car insurance claim process by leverage car-damage images from various customers to help build fast claim settlement process. Deep-learning solution to analyze car damage images and predict the quantum of damage  A deep-learning solution which identifies the car, the various segments/parts of the car analyses car-damage images and predict quantum of damage.  An advanced analytical model that uses historical claims data to estimate claim amount. Streamline the auto-insurance claim adjustment process accurately  A scalable, accurate, automated and streamlined auto-insurance claim adjustment process.  Advanced model to continually help increase accuracy and enable newer business use-cases.  Automated tool for detecting car-damage
  48. 48. Being able to assess claims automatically in real time, without any action on the side of the customer - such as obtaining proof of the incident - and then pay the claim directly to their PayPal or nominated bank account, debit or credit card, we think will be an appealing feature and provide the level of service we all now expect in our ever-increasing online, digital lives. – Alex Blake, Global head of travel insurance, Chubb Automated, real-time flight delay compensation product SOLUTION • In collaboration FlightStats, created a patented dynamic machine learning algorithm which analyses historical, real-time and forward-looking information providing highly accurate flight delay probabilities. • The dynamic pricing is unique for different combinations of flight carrier, departure/arrival locations and timings of the flight. • The entire product is white-labelled, integrated as a plug-and-play application and run on proprietary parametric platform, making it a seamless process for the end-consumer. IMPACT  Product is fully automated - It pays a predetermined amount of money if a delay trigger is breached for any passenger.  Eliminates all hassles for a customer in claiming insurance benefits  Product can be used on top of any other coverage from airlines and/or credit card companies, with hardly any exclusion.  Benefit trigger in this product is as low as 30 minutes to up to 180 minutes of delay  Competitively priced, lean and very flexible  Easily adjusted and targeted specific to the distribution partners’ consumer needs. PROBLEM Challenges faced by existing flight delay products  No automated real-time claims process in place – A customer has to manually file for claims to initiate it.  High delay triggers – A customer qualifies for the compensation only if there is a long delay of 6 hours.  Available products are complex and difficult to interpret, with numerous exclusions.  Absence of end-to-end digital solutions.  Unscientific pricing that is also flatly applied to all customers 48
  49. 49. PROBLEM Client, who is a leading global property and casualty insurer wanted to automate property damage estimation process  Reduce heavy losses on claims for rooftop damages caused by weather events or pre- existing damage to rooftops that allow water intrusions resulting damage to property’s interior  Unknown risks and claims which are difficult to verify as manual inspection of rooftops is often cost-prohibitive. IMPACT  Operational efficiency – Significant reduction of manual intervention and need for manual inspection by surveyors and also helped them prioritize.  Customer service – Time to take action decreased significantly helping improve customer service.  Improve ROI - Prediction accuracy of 95% leading to elimination of manual efforts and time spent in segregating the images leading to potential saving of millions of dollars Cost optimisation and automated property damage estimation process 49 SOLUTION  AI/ML led algorithm that classifies damaged vs non-damaged roof tops based on over 500,000 drone captured images using classical approach (SVM)/ Deep learning Algorithm (Faster RNN) methodology  Image processing where various techniques such as brightness normalization, image thresholding and contour/edge detection are used to clean the images which also helped identify the extent of damages thus aiding better loss estimation  Prioritization of property inspection based on historical claims data and image analytics
  50. 50. Manufacturing 50
  51. 51. Equilips 4.0: Sound analytics for real-time quality monitoring of manufacturing processes Machines have always been talking to us, through their sounds. We did not know how to understand their language.. Sound analytics was too complex, at least in noisy industrial setting, to do in real-time. Until now. We at Asquared IoT have developed real-time sound analytics technology, using Deep Learning, and we are revolutionizing real-time monitoring of machines by listening to their sounds. – Dr. Anand Deshpande CEO, Asquared IoT PROBLEM AI solutions for manufacturing plants  SMEs face various network complexities to convert to smart manufacturing and become a Industry 4.0 compliant factory.  Most of the available solutions are not easy to retrofit with old manufacturing plants.  Real-time quality monitoring for “special processes” such as welding is extremely important to detect defects and easily fix them compared to fixing them at the end application.  Destructive testing is the only known method to check the quality of welded joints, which is not only expensive but cannot be applied on 100% parts. SOLUTION Equilips 4.0, provides real-time quality monitoring of the welding process, requires no internet connection and other external connections • Uses industrial sounds (sounds of machines) as the input/data and microphone as the sensor  Developed machine learning (including deep learning) algorithms for Real-Time Sound Analytics that is embedded in the solution. 51 Sound analytics to deduce real-time information from manufacturing processes • Non-intrusive, non-touch, easy to retrofit feature available on edge computing • Huge savings from minimizing quality issues in the end application • Visibility into the operations and quality from remote locations. IMPACT
  52. 52. Proactive data sensing of quality issues for automobiles IMPACT PROBLEM AI (Machines & Platforms with intelligence) has become a part of mainstream business decision making, providing unbiased expertise. This AI application is proving to be a unique differentiator for our clients in the automobile industry by significantly improving the quality and safety for their customers – Romal Shetty, President, Deloitte India For a leading global auto manufacturer, expediting quality & service issues  Access to only sample data to investigate & identify aftermarket quality and service issues.  Insights from data after the quality issues have occurred was retroactive  Leverages potential data signals previous unexplored.  Multiple stakeholders to identify & investigate issues making the process complex and prolonged. SOLUTION Driving greater business value through predictive AI and data sensing  Early detection and streamlined the issue identification process leading to vehicle up-time  View prioritized quality issues based on projected warranty cost  Helped provide a single broad-based view to the higher management Issue identification and investigating root cause analysis  Proactively identified quality issues at least by an year in advance  $8M Annualized benefits per year observed in the first year after deployment  65% of IT workforce transformed to be more innovative 52
  53. 53. Cerebra Quality Solution- Reliable Industrial Intelligence PROBLEM Need for stringent quality control  The adhesives for critical mission usage needs stringent quality requirements from customers.  Standard operating procedures not giving room for real-time interventions and quality control.  Leading to over- production, rejections and customer complaints. SOLUTION Cerebra Quality solution a dynamic operating procedure using IoT Analytics  IoT Analytics applied helped control quality of the finished goods not possible with standard operation procedure.  Leverages technologies such as GPS, GIS, GSM, etc.  Provides performance benchmarking and quality prediction through AI Apps.  Conducts quality diagnostics using causal factor analysis. IMPACT High predictive accuracy and scale  Achieved 95% accuracy in prediction of quality of finished goods  Annual cost saving of USD 15-20 million across 10+ plants.  Reduction of 12% in customer complaints  Reduction of 60% in root cause analysis time  10% Off-spec reduction 53
  54. 54. BOLTTM – A Digital Service Engineer, enhancing user experience & last mile productivity for field engineers BOLTTM will bring agility in the way a service engineer can resolve the ER cases or service requests for GE. I am thrilled that our digital team has been able to leverage the power of AI, which will improve operational efficiency of Industrial assets for our customers. - Asha Poulose, VP & Hub Leader, GE Digital Hub PROBLEM Prolonged turnaround time in resolving an engineering (ER) case by field engineers  Activities involving field inspection and analysis of equipments take up to one week of turnaround time.  Providing recommendations to field engineers are extremely human centric & manual processes.  Increase in down time of ‘in service’ assets SOLUTION BOLTTM, a platform which acts as a digital coworker to the engineering team resolving repetitive ER cases  Converging digital with physical to improve industrial assets productivity  Machine learning and data science models for exploratory, descriptive, predictive & prescriptive analysis aiding problem diagnosis and providing recommendations to engineering team  Intelligent BOTS integrated with AI engine performs the resolution actions in tandem with engineering team  Deep learning techniques and framework for image analysis and semantic understanding of words. IMPACT Reduce equipment down time leading to improved power output  Reduces plant equipment down time to help improve power output generation, revenue and operating margins.  Drives operational efficiency by reducing TAT time by 95%.  Improvement in workforce productivity by 20%. 54
  55. 55. PROBLEM IMPACT The key distinction of this solution platform which focuses on improving OEE in manufacturing context has broad applicability across companies adopting Industry 4.0 Driving insights on automotive shop floor SOLUTION Automotive OEM issues on manufacturing line  Performance, quality & availability of high end robots used in welding, painting, assembly and other operations.  Impacting the production, raw material wastage, production delays and revenue. End to end solution turning plant floor data into insights  Resolve the issues of downtime of robots by predicting major faults in advance.  Early detection of quality issues to prevent material wastage and operation re-run.  Performance benchmark and early detection on deviation of assets performance (robots driven by PLC). Reducing operational cost  Informed decision making on maintenance and reduction in robots down time.  Predict and pinpoint potential OEE losses at machine level and prescribe optimized recommendations. 55
  56. 56. Quality & Security 56
  57. 57. Most of the cutting-edge product solution, which we have developed and deployed, have been co-created with our esteemed clients. ITC’s online agriculture products inspection system automation is one of the most challenging business problems we encountered. And, solution of this project won’t have been possible without the clarity of inputs, briefs, detailing and support, we received from our client ITC Agri-Business Division team. - Amit Singh, Chief Responsible Officer Automated and standardized grading inspection system for agri-products IMPACT PROBLEM For the client, ITC Agri-Business Division (ABD) Limited, standardizing and automating the procurement and processing of leaf tobacco  All ABD customers have specific leaf tobacco requirement, achieved through blending different kinds of leaves together  Customers expect a consistent product grade making blending process critical.  Every tobacco grade expected to comply with customers requirement of different color, ripeness etc.  Tobaccos grades are processed manually and is highly subjective Automatic and efficient tobacco leaves inspection process.  Reduces cost by decreasing human subjectivity in the daily inspection process.  Enhances efficiency by grading to 100% compared to 10% during manual inspection  Maintains product quality standards by minimizing manual involvement.  Real time inspection of all 100% tobacco cases 57 SOLUTION AI based Packaged Tobacco Inspection System implemented  AI application module for grading application known as “CAI’s Core module”  User interface known as “CAI-UI’s Software module”.  HD Industrial-Built camera custom-designed and mounted with a pneumatic arm operates in-sync with tobacco-case production & inspection cycle.  CAI-Reporting System available through web-UI which generates inspection output and other predictive results.
  58. 58. PROBLEM The client, Kerala Cardamom Processing and Marketing Company (KCPMC), the largest aggregator and exporter of Cardamom in India wanted to  Assess the quality of high volumes of incoming cardamom accurately and instantly to  Manage timely trade to bring faster & fairer gains to the farmers. SOLUTION A fast, objective and scalable digitization cardamom quality checking solution with no manual intervention making subjectivity and results standardized  The manual inspection and sorting is replaced by a image based digitized process using AI.  An image of the sample taken by computer vision and deep learning for the algorithm to further classify each pod by size, color and health.  The aggregated results of all the pods are then taken to calculate the final quality.  The AI solution works on cloud architecture by using images clicked via mobile phone using a simple app.  Provides an auditable trail of the actual assessments. IMPACT Reduces quality checking time and increases accuracy  The time per sample reduced from 25 minutes on an average to 55 seconds  The accuracy level of solution is 90% as compared to 70% accuracy of manual results. 58 Image based digitized, automated commodity grading & quality checking solution The difference of 0.5 mm in girth of cardamom can impact its price and hence the margin of error in grading is very small. Our proprietary solution brings down the average error to 0.03 mm thus providing high level of accuracy in grading and saving substantial costs in cardamom procurement. – Milan Sharma, CEO Intello labs
  59. 59. IMPACT PROBLEM SOLUTION Traditional Anti-Virus is not scaling to protect customers in the rapidly evolving cyber threat landscape.  Rapid explosion of Malwares at the rate of 400+ threats per minute and increased security breaches  Require technology that provides the best protection from advanced “Zero-Day” threats and security breaches leading to loss of revenue and increased operational costs A highly scalable, real-time behavior detection technology that monitors suspicious activity at endpoint, leverages machine learning, automated, behavioral-based classification in the cloud to detect advanced zero-day malwares.  Applies AI / ML techniques to identify malicious code and peels away the latest obfuscation techniques to unmask hidden threats to discover zero-day malware  Combines pre and post-execution behavioral analysis to detect malwares  Helped McAfee to grow and establish its endpoint business in enterprise, consumer and defense market segments.  Enhanced customer value, reduced infrastructure downtime and higher productivity  Faster response & reduced need for human analysis  Reduced endpoint administrator pains, operational cost optimization. It is the security industry’s first large scale AI based protection platform that has disrupted the classical signature based antivirus technologies by providing true predictive protection to users through the application of artificial intelligence algorithms in security. – Prabhat Singh, VP, Future Threat Defense Technology Group, Office of the CTO, McAfee LLC. 59 McAfee Real Protect: real time behaviour detection to monitor suspicious activity at endpoint
  60. 60. Retail 60
  61. 61. SOLUTION IMPACT PROBLEM AI has progressed rapidly in the space of Computer Vision, which is typically used in retail for object identification, finding similar products, tagging product attributes, etc. However, solving these problems today requires labelled training data. So far, deep learning has been successful primarily for such supervised learning tasks. Now, there is great potential in unsupervised representation learning, which does not require labelled training data sets. We are focusing our efforts in these two active and exciting areas of research for AI technologists to branch further into. – CEO-Dataweave Third-party sellers list counterfeit products on ecommerce websites, which affects the client’s brand image and leads to consumer dissonance.  The client , manufacturer of textile products for outdoor gears, relies heavily on ecommerce websites to drive sales.  Third-party seller's counterfeit products on websites affects brand’s image leading to consumer dissonance.  Seller compliance required to mention brands, track and report counterfeits. Image processing techniques to identify counterfeits and improve the accuracy of output  Single Shot Multi Box Detector (SSD) for object detection.  Pre-trained Convolutional Neural Network (CNN) based models to take advantage of transfer learning.  Siamese Networks trained on internal data focusing on fine grained image features.  image processing and image matching techniques based on key points and descriptors to improve the accuracy of output. Successful tracking of unauthorized white-labels in retail  More than 55% of 500 original products tested across 8 websites had at least one counterfeit product. Counterfeit products detection for consumer brands 61
  62. 62. PROBLEM The retail industry is becoming increasingly visual and images play a far bigger role in purchase decisions than ever. When we're dealing with datasets in petabytes, it's important to devise smarter ways of extracting reliable, real-time data. Innovations in AI-led solutions ensure faster, more holistic insights on a much larger scale. – Sanjeev Sularia, CEO Delivering catalog curation and product availability insights Retail client’s need for intellectual infrastructure solution to extract rich data from product images  Need for a solution to extract data from product images to support textual data to deliver better catalog segmentation and analysis.  Existing solutions requires manual intervention and was not scalable or real-time. SOLUTION Neural network capable of reading multiple image files and enable product attributes extraction  A neural network developed to transfer and read positive image files for various attributes like color, dress type etc.  This neural networks making way for automatic feature learning that can be extended to other applications.  Currently delivered as a micro service and embedded in the product suite IMPACT Deliver better catalog curation and product availability.  Increase in operational efficiencies of 20%.  Increase in data 'completeness' by 40%.  Ability to generate reliable framework to obtain benchmark data. 62
  63. 63. PROBLEM Our goal is to use AI to make it effortlessly easy for you to make better choices. The internet has made it easy to access information, but there is too much of it. And it can get confusing. We want to help drown out the noise and focus on choices that are relevant to us. Our computers and smart phones need not be passive channels for consumption. AI and deep learning in particular has now made it possible to take piles of seemingly confusing, unstructured data and tease out insights. Dittory is one great example of what's possible. And we are working on more. – Sai Gaddam, CEO-Kernel Insights Dittory: a product discovery and visual search platform for online apparels IMPACT How to discover an identical or near-identical piece of clothing elsewhere online  Searching apparel products is difficult as they do not come with standardized names.  The visual semantics of apparel hard to translate to text making narrowing down on desired products difficult.  The text labels offered with apparel catalog imagesonly capture the broad category, cannot translate fine grained individuals style preferences. SOLUTION Dittory a product discovery and visual search for clothing, enabling real-time suggestions of matching apparel  In-stock product database covering 50 ecommerce sites and 60 million products.  Fast visual search in less than 500 milliseconds to retrieve identical and similar products across 30 million products.  Deep-learning techniques to generate meaningful vectors representing each image and make similar images have similar vector representations. Real time impact on end-user experience  The search allows users in real-time to compare prices and look for similar/identical products on other ecommerce/stores when shopping for apparel  The solution works for more than 60 million products with number of products increasing on a daily basis. 63
  64. 64. “Ignio has played a very crucial role in our peak season and reduced P3 and improved operations, some of very critical Applications have reduced P3 incidents. Thanks for putting the right people to implement and configure” – Fritz Debrine, TCS-VP, Infrastructure and Operations ignio™: Automation powered tech infrastructure support during peak holiday season SOLUTION Deployed AI powered Cognitive platform ignio™ to automate most of the data center operations.  ignio SMART TRIGGER and PCM (Performance & Capacity Management) module used historic performance data from monitoring tools for providing recommendations for server configurations and normal behavior profiling for dynamic threshold to suppress noise in the system.  ignio HEALTH CHECK module was configured to do periodic health check of critical parameters and remediation Retailer client needed the systems to be available during the peak holiday season which accounted for nearly 40% of the annual sales.  Customer did not have reliable insights of the capacity planning that is required for their infrastructure based on historic performances the infrastructure  Incidents occurring during peak time went to respective infrastructure team for manual fix and was usually delayed  Proactive health check of critical application infrastructure required dedicated team working 24X7 PROBLEM 64 IMPACT Customer acquisition and growth in sales  55% new customer addition compared to the previous year  25 to 29% increases in their dotcom sales across their three different sites compared to the previous year  100% availability of POS across all the stores which is a record achievement in their business  MTTR reduction by 75% due to automation
  65. 65. 65 Social Impact
  66. 66. IMPACT PROBLEM SOLUTION Security agencies and police forces face several challenges on ground zero level  Identifying the suspects in real time while routine checking  Examining CCTV footages as there is no unified technology platform to connect unstructured and heterogenous data points of criminals  Policing reactive rather than proactive  Adopted Artificial Intelligence & Deep Learning technology in an un-conventional approach to process real-world datasets by decoupling them to their constituent elements like text, speech and images  Perform selective amalgamation of data points to feed into advanced hybrid deep neural network models.  Enable extraction of information impossible to achieve with a single domain (image or speech or text) neural network models.  For the end user, the solution is in the form of a web-panel and a mobile application.  Hybrid deep neural network model to analyze multiple data categories simultaneously  Replaced traditional practices of tackling each dataset in silos; thereby extracting larger information compared to other entities  400+ gangsters apprehended  21 foreign handlers identified  8 terrorist modules busted 66 ABHED: Predictive and smart policing; real time analysis
  67. 67. PROBLEM A need for a quick to deploy omni-channel engagement and insights system for government, both local and national, to understand key citizen concerns and engage with them to provide resolutions  No good e-governance focused solutions  No solution that include social and public domain data with automated sentiment analytics including regional languages SOLUTION Citizen Engagement Solution enables to: • Listen to citizens across mobile, web, SMS, email, instant messenger and social media • Citizen Voice: Get automated Voice of Citizen dashboards that are role based dashboards in over 50+ languages • Workflow Route comments into workflows with auto prioritisation, SLA management to ensure the relevant people have access to response queues to respond efficiently and effectively • Automated assistants and embedded AI automates processes and optimize response time • End-to-End Customer Grievance Resolution for all the departments of a major city, including utilities, transportation, and urban planning 67 IMPACT Understand customer and competition sentiments at a granular level, improve customer loyalty, create awareness & influence purchase  20-30% improvement in response times  100% coverage of citizen voice with automated sentiment analytics  10-15% improvement in response times via social AI SOCIO - Citizen Engagement Solution
  68. 68. Automatic Number Plate Recognition (ANPR) solution for improved traffic management, vehicle analytics & security PROBLEM Highest accuracy ANPR system seen in India. Amazing! – Client testimonial The non-standardization of vehicular number plates made the accuracy of detection and reading very poor thus affecting traffic management, vehicle analytics & security  Automatic number plate recognition (ANPR) aided in fastening the toll lanes, provided data for parking automation, assisted in tracking vehicle & crime analysis  Automation of number plates helped in improving detection accuracy as well as helped in automating/optimizing various functions. SOLUTION Uncanny’s AI algorithms, has achieved >98% accuracy for detection and >90% for recognition.  Combination of neural network was used for detection as well as recognition of the number plates  In every toll lane, there is an Uncanny Vision ANPR camera and it is connected over an ethernet network to a processing system running Uncanny ANPR  Uncanny ANPR detects vehicle number plates and for every new vehicle, sends one number plate info over a secure network interface to the toll management software IMPACT Significantly improved ability to monitor and audit flow of vehicles  Significant cost saving potential of INR 500+ crores per year is possible once system is operationalized in all toll plazas  Faster flow of traffic – expected to cut toll plaza time by 50% 68
  69. 69. Transportation & Logistics 69
  70. 70. "Using Locus, we are able to simulate multiple what-if scenarios and then take larger business calls. For example, we send carpenters on all our delivery vehicles and at one point we questioned ourselves on why we need to send carpenters on all our delivery vehicles. Is there a better way to do? We actually simulated this on Locus. Without the tool, it would have been hypothesis." – Kaustabh Chakraborty, SVP (operations and supply chain) Urban Ladder Logistics optimization platform using deep learning proprietary algorithms PROBLEM Manual shipment processing significantly increases error rate, higher processing time, human resource cost and additional overhead costs, • Lack of accurate checks for mis-routes; impacting delivery efficiency • Compromise on the agreed service levels with the end customers SOLUTION Locus’ AI enabled solutions includes  The most advanced route optimization solution for material dispatching  Automatic shipment sorting and rider allocation.  Intuitive and dynamic automated packing plans  Automated beat planning leading to higher sales productivity  Real-time tracking, insights & analytics  A portable device for cost-effective measurement of packages. IMPACT The AI solution provides  Increase in serviceability ratio by 12%  Increase in SLA adherence by 15%.  Decrease in operational costs by 30%.  Reduction in freight costs by 15%.  Decrease in shipment processing time by 65%.  Man-hours of 53.3 saved every day. 70
  71. 71. We selected Driveri™ because it provided us the greatest view of our fleet—delivering meaningful data within minutes and empowering us to recognize our drivers based on their actual driving.” – Keith Warren, VP Transportation, LeSaint Logistics Driveri: Versatile fleet & driver safety platform PROBLEM Road and Driver Safety is a major concern across the world. Road accidents in the US cost around 800 billion dollars a year, and in India it’s estimated to be around 60 billion dollars a year.  Creating HD maps for autonomous driving is a very costly, time consuming, bandwidth inefficient process.  Contributes to significant economic, social and emotional loss to the society. SOLUTION Netradyne’s product Driveri, crowd sources data to generate real time, dynamic “high definition” HD maps for autonomous driving using edge computing and crowd sourced SLAM based approaches.  Uses the sensory stack of Autonomous Driving Technology using computer vision to identify at risk driving situations in the complex driving scenarios and notifies the driver in real time using audio messages  Communication acts as a real time coach and third eye for the driver resulting in increased safety and improved bottom-line for the fleets. IMPACT Driveri devices have covered over 5 million miles per month with 15 million miles; expecting to cover 100 million miles by the end of this fiscal year. Client - LeSaint Logistics has seen significant improvements in driver behavior:  50% reduction in hard-braking alerts  38% reduction in risky following distance  45% reduction in seatbelt non-compliance  26% reduction in speeding  27% improvement in average fleet following distance 71
  72. 72. Om Logistics: ‘I don't have to call 10 people on each route to get lowest rates. With Rivigo Vyom I get best prices on any route immediately.’ – Client Testimonial Vyom: Dynamic and accurate prediction of trucking freight prices IMPACT Pricing engine makes the process of price discovery hassle-free and transparent.  > 10x improvement in the accuracy of the pricing engine by measuring pricing offset per trip (percentage difference in the sourcing price vs predicted price). 72 Predict the prices for all possible Origin-Destination-Vehicle Type with highest accuracy in real time  Zero price transparency in the truck freight market due to tedious process of price discovery and finalization.  No scalable solution available. PROBLEM SOLUTION ‘RIVIGO Vyom’ a data and technology driven freight marketplace, implemented for predicting freight prices  Price Clusters formation  Price Prediction based on historical price points  Price points extraction from unstructured & broken English sentences  User reliability calculation for quotes via Vyom rate exchange  Reverse lanes price prediction  Prediction of supply and demand in each region
  73. 73. The latest developments in deep learning and robotics are enabling AI practitioners to mimic seemingly complex human tasks – both cognitive and physical. This is opening up a lot of opportunities for AI to make an impact in industries which are traditionally less digital. – Pradeep Gulipalli, Co-founder, Tiger Analytics Cost effective rail track fault detection PROBLEM  Traditional railway operational activities involve railroad & regular maintenance by personnel manually inspecting and detecting the rail track for faults.  The client, a leading railroad company, records high-definition videos of rail tracks as the train moves along the tracks. This video would then be evaluated by trained professionals to detect any issues with the track. This process would be conducted on a periodic basis. The client was looking for a cost-effective solution to this process, using AI. SOLUTION Developed a custom AI solution that examines the video captured at real speeds, to detect a wide range of faults in the rail tracks.  Developed custom de-noising nets to correct for dust/fog/light and performed pre-processing activities to account for features specific to rail track videos (e.g. angle, aspect ratio, focus).  We trained custom deep nets to detect and annotate specific parts of the video with fault-tags and geo-tags. The AI would then appropriately alert the maintenance department. IMPACT  Considerably reduce the degree of manual effort in analyzing the videos  Significantly more accurate in detecting faults – both early-stage and late-stage.  Estimate the eventual impact of the solution to be of the order of $10-12 million annually. 73
  74. 74. Miscellaneous 74
  75. 75. IKON- A cognitive engine for incident management SOLUTION IMPACT PROBLEM Solving incidents and service requests using latest technologies  Capgemini's clients consider application and IT support as a cost of failure.  Clients expect close to 100% system availability with quick turn-around time during service requests.  Incidents cause system outages resulting in customer dissatisfaction and loss of market. Implemented IKON (Incident Knowledge Object-based Nanobot), a cognitive engine using deep learning • When incident is reported by the user it flows to IKON within minutes and solved using two incident parameters – KO Relevancy and KO Usage.  The analyst confirms the knowledge article relevant to the incident and performs the required steps  Predicts incidents on pattern analysis using past data and provides correct knowledge article to avoid incidents.  Provides a feature to execute robots and performs the steps according to knowledge article. Continuous improvement through problem management and automation. • Reduction in incident cycle time (TAT) by ~60%, helps to improve turnaround time resulting in higher client satisfaction.  Reduction of Average Effort per Ticket (AET) by ~25%, provides for continuous improvement by problem management and automation.  Productivity improvement reduces labor by about 20% yearly.  Visible change in consistency of deliverables with high quality We use IKON to drive business results. IKON and ROOCA tools are used to reduce the tickets as well as MTTR. IKON is the knowledge management database, where analysts types in the keyword and resolves the repetitive incident faster. These knowledge articles IDs then used by the ROOCA tool for automated failure mode analysis to identify root causes and eliminate incidents – A large agrochemical and agricultural biotechnology company 75
  76. 76. Solution Framework built on Nokia AVA platform.  Analyses data across multiple sources over a longer period 9-12 months  Uses machine learning based dimensionality reduction and decision tree based methods to extract hidden interesting insights from customer data.  Predict network KPI degradation/non-degradation behavior at cell level in 7 days advance. Predictive Operations Analytics is enabling telecom service providers  Increase network availability by 10%  Reduce operation cost by 20%  Reduce customer complaints by 10%  Reduce churn reduction by 10%.  Improve subscriber experience and loyalty 76 PROBLEM Need for predictive operations analytics solution which can analyze KPI, Counters, Alarms, & Weather data of thousands of sources over a longer period 9-12 months and extract hidden interesting insights to predict network KPI behavior at every source and proactively assist operation team to deliver the best network services with remarkable customer experience  In existing telecom network, operation team monitors network KPI, Alarm data, and takes corrective actions after interruption of network services. This results into service unavailability, poor service quality, & customer dissatisfaction Predictive operations analytics to deliver the best telecom network services and CX IMPACTSOLUTION
  77. 77. IMPACT PROBLEM In order to unleash the true potential of Artificial Intelligence (AI) on small devices, there was a growing need for embedded devices to carry out complex computations involved in AI based algorithms. The solution has to accommodate high computations on smaller form factor devices. Tata Elxsi made this possible by creating a solution that can successfully run AI algorithm like neural networks, on conventional low cost devices. Generated higher returns with minimum investment and ensured a continuous flow of opportunities for a long time. Considering the fact that the next frontier for AI technology is moving to the edge( in devices) as some of the computations can be offloaded to the device, not all computing need to happen in the cloud leading to cost saving The AI team has created a niche for itself in this domain. With the right combination of experts, we have become one of the leaders in enabling the AI evolution – Quote from Head 77 Edge AI solution bridges the gap between AI algorithms and embedded devices by facilitating the execution of AI algorithms on low memory and low processing power-embedded devices SOLUTION Execute complex AI based computations in conventional embedded devices
  78. 78. IMPACT PROBLEM SOLUTION An Automated Speech Recognition (ASR) engine which converts speech to text is required for both call analytics and conversational voice assistant products.  Hidden intelligence is contained between recordings of spoken conversations between call center agents and customers  Spoken data can be made amenable to text-based analytics. ASR engine incorporates data from diverse sources such as telephone call recordings, custom-collected spoken recordings, commercially available speech databases, transcribed texts of speech recordings, among others.  Utilized AI techniques like classification algorithms, clustering, Hidden Markov Models, DNNs, SLTMs and so forth  Leveraged Windows, Linux, shell scripting, Python and its NLP and machine learning libraries, Java, Weka, GPU servers (for acoustic model training), and numerous tools custom-developed in house, among others.  Enhanced accuracy rates compared to competitive ASR engines, 50-80% reduction in error reporting  Significantly more accurate & relevant business insights or intent recognition  Drive key business metrics and outcomes such as reduction of customer churn, improvement of sales conversion/collections, improvement of customer satisfaction/NPS 78 Unlocking intelligence through Automated Speech Recognition (ASR) engine
  79. 79. Transforming the way videos are watched PROBLEM Highly underutilized video content  Videos represent a powerful medium of communication. Enterprises produce marketing & product training videos for customers/partners, learning & internal communication videos for employees.  However, these videos are highly underutilized, as viewers are unable to peer inside videos and most viewers rarely watch past the first few minutes. SOLUTION VideoKen’s AI-based platform makes informational videos much richer and more consumable.  Uses AI techniques to automatically index videos, creating table-of-contents and phrase cloud, to summarize key topics in a video  Embedded video player provides unique navigational capabilities to jump within a video to points of interest to the viewer  Provides insights on which topics within a video received more views, and where the viewers left the video. IMPACT  Richer video watching experience by topic discovery and search within videos  >2.5x higher user engagement for informational videos  Rollout video-based learning programs 3x faster than before No other platform provides such rich indexing currently. 4 granted US patents and more pending patent applications. 79
  80. 80. Glossary 80
  81. 81. Glossary (1/3) 81 Algorithm: A formula or set of rules for performing a task. In AI, the algorithm tells the machine how to go about finding answers to a question or solutions to a problem on its own; classification, clustering, recommendation, and regression are four of the most popular types. Analogical reasoning: Solving problems by using analogies, by comparing to past experiences. Artificial Intelligence (AI): A machine’s ability to make decisions and perform tasks that simulate human intelligence and behaviour. Artificial Neural Networks (ANN): A learning model created to act like a human brain that solves tasks that are too difficult for traditional computer systems to solve. Autonomous: Autonomy is the ability to act independently of a ruling body. In AI, a machine or vehicle is referred to as autonomous if it doesn’t require input from a human operator to function properly. Bayesian network: A type of probabilistic graphical models built from data and/or expert opinion. They are graphs explaining the chances of one thing happening depend on the chances that another thing happened. They can be used for a wide range of tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction and decision making under uncertainty Chatbots: A chat robot (chatbot for short) that is designed to simulate a conversation with human users by communicating through text chats, voice commands, or both. They are a commonly used interface for computer programs that include AI capabilities. Classification: Classification algorithms let machines assign a category to a data point based on training data. Cluster analysis: A type of unsupervised learning used for exploratory data analysis to find hidden patterns or grouping in data; clusters are modelled with a measure of similarity defined by metrics such as Euclidean or probabilistic distance. Clustering: Clustering algorithms let machines group data points or items into groups with similar characteristics. Cognitive computing: A computerized model that mimics the way the human brain thinks. It involves self-learning through the use of data mining, natural language processing, and pattern recognition. Computer vision: The field of A.I. and image processing that train machines how to interpret the visual world Convolutional Neural Network (CNN): A type of neural networks that identifies and makes sense of images.
  82. 82. Glossary (2/3) 82 Data mining: The process by which patterns are discovered within large sets of data with the goal of extracting useful information from it. Data science: An interdisciplinary field that combines scientific methods, systems, and processes from statistics, information science, and computer science to provide insight into phenomenon via either structured or unstructured data. Decision tree: A tree and branch-based model used to map decisions and their possible consequences, similar to a flow chart. Deep learning: The ability for machines to autonomously mimic human thought patterns through artificial neural networks composed of cascading layers of information Facial recognition: The recognition of faces and emotional states in images or video signals. This is commonly done through point annotations called landmarks Heuristics: These are rules drawn from experience used to solve a problem more quickly than traditional problem-solving methods in AI. While faster, a heuristic approach typically is less optimal than the classic methods it replaces. Image recognition: Recognizing the specific types of objects in given image or video datasets Machine intelligence: An umbrella term that encompasses machine learning, deep learning, and classical learning algorithms. Machine Learning (ML): A field of AI focused on getting machines to act without being programmed to do so. Machines “learn” from patterns they recognize and adjust their behavior accordingly. Natural Language Processing (NLP): The ability of computers to understand, or process natural human languages and derive meaning from them. NLP typically involves machine interpretation of text or speech recognition. Optical Character Recognition (OCR): A system that detects images of handwritten or printed text and converts them into machine-readable text Recurrent Neural Network (RNN): A type of neural network that makes sense of sequential information and recognizes patterns, and creates outputs based on those calculations. Reinforcement learning: A process where machines learn to do a new task like humans do — through a system of rewards and punishments — starting as a novice and improving with practice and feedback.
  83. 83. Glossary (3/3) Source: Compiled from secondary sources 83 Speech recognition: The recognition of words and/or emotional state in an audio signal Supervised learning: A technique that teaches a machine-learning algorithm to solve a specific task using data that has been carefully labeled by a human. Everyday examples include most weather prediction and spam detection. Training data: In machine learning, the training data set is the data given to the machine during the initial “learning” or “training” phase. From this data set, the machine is meant to gain some insight into options for the efficient completion of its assigned task through identifying relationships between the data Transfer learning: This method tries to take training data used for one thing and reused it for a new set of tasks, without having to retrain the system from scratch. Unsupervised learning: A type of machine learning in which human input and supervision are extremely limited, or absent altogether, throughout the process. In unsupervised learning, the machine is left to identify patterns and draw its own conclusions from the data sets it is given. The most common unsupervised learning method is cluster analysis.
  84. 84. NASSCOM is the industry association for the IT-BPM sector in India. A not-for-profit organization funded by the industry, its objective is to build a growth led and sustainable technology and business services sector in the country. Established in 1988, NASSCOM’s membership has grown over the years and currently stands at over 2,500. These companies represent 95 percent of industry revenues and have enabled the association to spearhead initiatives and programs to build the sector in the country and globally. NASSCOM members are active participants in the new global economy and are admired for their innovative business practices, social initiatives, and thrust on emerging opportunities. Disclaimer The information contained herein has been obtained from sources believed to be reliable. NASSCOM disclaims all warranties as to the accuracy, completeness or adequacy of such information. NASSCOM shall have no liability for errors, omissions or inadequacies in the information contained herein, or for interpretations thereof. The material in this publications is copyrighted. No part of this report can be reproduced either on paper or electronic media without permission in writing from NASSCOM. Request for permission to reproduce any part of the report may be sent to NASSCOM. Usage of Information Forwarding/copy/using in publications without approval from NASSCOM will be considered as infringement of intellectual property rights. About NASSCOM 84
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