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With the U.S. economy grappling with massive internal debt, ideas that canMortgage Fraud Report - CoreLogic

reduce even a fraction of this would be of great advantage. Financial institutions as well as startups have already begun looking for ways to innovate, and make banking systems more efficient which allows financial institutions to the lending problem effectively. Interestingly, lending can be seen as a big data problem making it naturally suited for machine learning. As part of their strategy to become efficient, profitable new age digital lenders, financial institutions can optimize their lending processes to support proactive loan decisions by leveraging AI-driven advanced analytics. 

Traditional loan underwriting methods depend heavily on scores provided by credit bureaux. While credit score verification may be the first step towards filtering out likely defaulters, they could fail to capture complex patterns in loan repayments. Banks are now adopting general AI-powered tools such as chatbots, customer relationship management tools, social media footprints and real-time analytics to gauge creditworthiness more accurately.

One banking application looks at a potential applicants’ entire digital footprint to determine their creditworthiness by having individuals download their app. It looks at over 12,000 variables including social media account use, internet browsing, geolocation data, and other smartphone information. The machine learning algorithm turns all this data into a credit score, which banks and other lenders can use.

The application does not share personal data with lenders to protect individuals’ privacy but only the final result of their analysis. This has allowed their partners to approve up to 50 percent more applications.
Cognitive AI applications which help students pay off their student loans faster uses machine learning to analyze the individual’s financial habits to determine if they can afford to pay back their loans faster, besides also automatically prompting how much more they should contribute.

The use of machine learning to analyze alternative data in loans and credit rating may raise some privacy, ethical, and legal concerns. People might not feel comfortable with a financial institution having access to all sensitive information about their life. Even if all these institutions behave ethically, the more data they hold the more that can be stolen by malicious hackers during a data breach.

Even with these concerns, the use of machine learning to process alternative data to determine creditworthiness will grow significantly. There are billions of people who are unbanked or a disorderly financial spending pattern to whom financial institutions may one day offer mortgages, payment plans for products, credit cards, or other loans. The financial appeal of these tools is obvious. It is reasonable to believe that the more information you gather about an individual, the more likely you would be able to predict their behavior, including how diligently they would pay back a loan.

Most loantech applications are new and have existed during a time of modest economic growth. It is easy to appear right about loan worthiness during good times – the real test is often how they perform during a slump. Even if machine learning can accurately use an individual’s digital footprint (purchase history, app use, search history, social media activity) to determine their creditworthiness, it isn’t necessary that machine learning systems will always yield better results than traditional credit measures, unless it works in a cross-channel, real-time fashion.

There’s another important dimension – fraud.

Banks work on thin margins because of extensive regulations and operational controls and interest fees on loan/credit products is their bread and butter. So, fraud perpetrated on these products hit them quite hard. Banks are continuously challenged by fraud, a lot of which are perpetrated by unscrupulous agents who use a variety of alternate delivery channels available today. Fraud by customers who work with accomplices (other customers and /or bank employees) take advantage of limited safeguards against fraud for loan/credit products.

Also, credit departments of banks tasked with identifying / establishing customer credit-worthiness before disbursing funds, must invest more efforts in identifying account organizing frauds in real-time to handle cases internally to take quick, preventive action if/when required.

Besides helping detect fraud syndicates, AI-based, real-time, cross-channel anti-fraud technology can help decline loans or keep an eye on the borrower and restrict them or favor them according to their credit risk and fraud risk ratings.

Intelligent real-time anti-fraud solutions have comprehensive rule-based fraud identification mechanisms that enable banks to instantly identify fraudulent applications/loans/customers in real-time at the loan origination stage itself. These solutions enable banks to accelerate the entire credit processing end-to-end workflow and can be configured to perform granular pattern-matching against a large set of pre-configured variables to automate potential loan fraud scenarios.

Digital lending helps reduce conflict in the borrowing ecosystem, reduces paperwork and moves customers to online and mobile interfaces. But what will change the game will be the accurate application of customer insights (using both internal and external data) and precise digital analytics using AI and machine learning to intervene in absolute real-time.

In the world of financial transactions, rule based heuristics are often employed to detect fraud, rather than to detect anomalies. The cat and mouse chase of financial institutions and fraudsters is an ongoing battle that has cost the global economy close to half a trillion dollars. Given the fundamental shortcomings of rule based heuristics, how does machine learning help in fraud detection?


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If chemical companies want to stay competitive and move forward in a changing world, they need to rapidly adopt innovative technologies. Incorporating IoT within these companies can provide important benefits. Combining IoT with machine learning can move this industry forward to work more efficiently and create better results.

How could IoT improve chemical production? How can chemical companies use IoT and machine learning with their processes?

Improve Chemical Production With IoT

While many industries are embracing IoT, it might not seem clear how it could relate to chemicals businesses. But Andy Chatha, the president of ARC, made it clear that IOT applies to the chemical industry too, in his keynote presentation for the ARC Advisory Group Industry Forum a few years ago. A review of this presentation was written for Chemical Processing by Paul Miller. Chatha explained that IoT can streamline many parts of industrial companies, including providing smart machines, offering better capacity for big data storage, and helping optimize systems and assets. The benefits of IoT within this industry are far-reaching. They include better productivity, improved asset utilization, and higher revenue.

Fostering Innovation

Significant opportunities exist in R&D to create higher value and higher margin products at a faster pace, particularly in specialty and crop protection chemicals. Advanced analytics and machine learning enable high-throughput optimization of molecules as well as simulation of lab test and experiments for systematic optimization of formulations for performance and costs (“from test tube to tablet”).

In addition, advanced analytics and machine learning can drive allocation of best available resources to research projects in line with portfolio priorities.

Also screening of internal knowledge and patent data bases becomes possible to maximize use of intellectual property and fill gaps therein.

Machine learning can also help chemical manufacturers to run simulations on sustainability and environmental impact across a products life cycle.

Changing the Game in Plant Operations

IOT builds the foundation for Machine Learning in Manufacturing and Asset management as it allows to capture real-time data on asset status and performance, process parameters, product quality, production costs, storage capacity and inventory (telemetry), inbound/outbound logistics, worker’s safety, pairing products with services etc.

With today’s advanced capabilities in capturing, storing, processing and analyzing data a vast amount of plant, asset, and operational data can now be used in conjunction with advanced algorithms to simulate, predict and prescribe maintenance needs for assets to, increase availability, optimize uptime, improve operational performance and extend the assets lifetime.

In this context, digital twins play a major role in managing asset performance and maintenance. Once plants and processes have been designed and engineered, digital twins can be used to train operators by simulating special plant and process conditions related to safety and/or performance (like flight simulators). Digital asset twins can be used in maintenance to predict the impact of certain process parameters on asset performance, asset lifecycle and maintenance needs. A Deloitte University Press document, “Industry 4.0 and the Chemicals Industry”, explains the concept of Digital Twins in such a way that “organizations create value from information via the movement from physical to digital, and back to physical.”

Completely new opportunities for the chemical industry arise from Distributed Manufacturing/3D-Printing in terms of developing innovative feedstock and driving new revenue streams. While more than 3,000 materials are used in conventional component manufacturing, only about 30 are available for 3D printing. To put this into perspective, the market for chemical powder materials is predicted to be more than $630 million annually by 2020.

Worker safety can be enhanced by the addition of smart tags on wearables which could help to alert workers on exposure to dangerous substances (like e.g. toxic gases),p to help locate employees and contractual workers in cases of emergency. Moreover, alerts could be triggered if employees work out of their designated or authorized working area (“connected worker”).

Taking your Supply Chain to another Level

In Supply Chain a lot of untapped potential exists for new technologies of IoT and Machine Learning. Just think about using advanced analytics to increase forecast accuracy leading into improvements along the entire Sales and Operations Planning Process and related KPIs.

Advanced analytics and machine learning could be used for mitigating risks of supply chain disruptions. For example, in case of natural disasters shipments could be automatically re-routed to meet on-time-delivery goals and customer commitments at minimum costs.

Another opportunity resides in optimizing the use of transportation assets and related costs. Moving chemicals in many cases means considering special equipment and complex compliance requirement so that empty backhauls are the norm rather than the exception, resulting in increased costs and suboptimal asset utilization. Here machine learning could help to better leverage transportation assets and drive waste out of the logistics function.

Innovate by getting closer to your Customer

Over the last years the chemical industry as an “asset intensive” industry was focusing its efforts towards optimizing plant and asset operations. However, there is huge untapped potential to develop innovative, customer centric business models and services. Here are a few examples how chemical companies could benefit from better leverage of IoT and Machine Learning at the customer front end:

Leverage sensors and telemetry to implement Vendor/Supplier Managed Inventory concepts and completely automate the replenishment process (“no” or “low touch” order to delivery).

Monitoring your customers’ manufacturing process parameters in real-time via sensor technology, leveraging advanced algorithms to correlate process parameters with quality of (semi-) finished products, start selling first pass quality as business outcome, instead of selling products. Opportunity to offer benchmark data as a service.

Use advanced algorithms to better understand buying behavior/patterns of customers, adjust product and service portfolio correspondingly, identify cross-selling opportunities to increase customer loyalty and share of wallet.

Get visibility into customer/market sentiment via capturing and processing unstructured data from social media, respond with appropriate marketing campaigns and innovative service offerings.

Moving Forward With IoT

By using IoT with machine learning, chemical companies can move forward and gain positive business results. How do chemical companies use IoT technology? Chatha said that industrial businesses already have or are just building the foundations for incorporating IoT and Machine Learning. Overall, IoT can act as a solution that helps the chemical industry keep up with changing times and better meet the needs of shareholders and customers. However, having clean and abundant data available to train algorithms and build high quality models which predict high quality results are pivotal to success. Another critical success factor are highly skilled data scientists. Lack of those could be a severe constraint for rapid adoption of IoT and Machine Learning in the chemical industry.

“Reprinted with permission, original blog was posted here”. You may also visit here for more such insights on the digital transformation of industry.

 About ARC Advisory Group ( Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.

For further information or to provide feedback on this article, please contact

About Your Guest Blogger

Stefan Guertzgen has worked for 8 years as Senior Director for Industry Solution Marketing Chemicals at SAP. In this function he is globally responsible for driving industry thought leadership, strategic portfolio decisions and overall positioning & messaging as well as executive messaging programs for key stakeholders along SAP’s entire Chemical solution portfolio.

Prior to this assignment he has worked for 11 years in the chemical Industry at Chemtura in various positions comprising R&D, Global Business Development, Business Process Management and Sales & Operations Planning. On top he has a 7 year experience in Pre Sales and Management Consulting for the process industry with focus on business operations, working for companies like AspenTech, AT Kearney and SAP Business Transformation Consulting.
He holds a PhD degree in Chemicals from the Max-Planck-Institute for Coal Research at Mülheim/Germany and has been granted a 1-year post-doctoral fellowship for the University of Berkeley/California from the Alexander von Humboldt Foundation.

Economic progress that in India was earlier propelled by capital investment and labor has been pushed into the sidelines as a result of a fresh factor of production – artificial intelligence (AI), something that’s opening up new sources of value and growth across the globe. Leaving aside the aspect that AI is simply another productivity promoter, policy makers and business leaders should appreciate it as a tool that can transform our thinking about how growth is created.

New technologies such as AI and machine learning (ML) have already ushered in an era of making networks intelligent and are changing business models. U.S., China, Russia and South Korea are already investing big bucks in developing their AI technologies and policy strategies. Apparently, in Germany they have already created an ethics committee to understand the man-and-machine relationship in case of automated driving.


India too is gradually picking up pace in advancing its AI technologies, the push chiefly coming from the Indian outsourcing industry and a move toward a digital economy. A recent global survey conducted by U.K. based professional body Chartered Institute of Management Accountants CIMA showed that India ranks amongst top 3 countries in the world implementing robotic automation in their core business processes.


Strategically, AI can drive growth in creating a new virtual workforce, complement and enhance the skills and ability of existing workforces and finally drive innovations. In healthcare, AI can deliver better health services, in education, edtech companies are using AI to deliver personalised learning experiences, in finance, we’ve already witnessed dramatic progress in digital payments and in agriculture, AI could deliver tailor-made advice to farmers to increase crop yields. With one of the world’s largest IT industry and a rapidly burgeoning digital population, we need to exploit innovations better.


With tech conglomerates in India already swiftly moving ahead in adopting AI and increasing number of colleges in India beginning to offer relevant courses, we need the capacity to harness the long-term prowess of AI. Some factors that propel this growth have been India’s start-up scene that has been quite lucrative getting the required boost from our government’s constant push for ‘Make in India’ and ‘Start-up India’.

Another important factor for India’s AI development has been big investments from marquee investors and business tycoons. Plus, I have a strong belief, because the magnitude of change AI will bring to a country like India will be far larger than its bigger counterparts, adoption will be much faster. There is a requirement for nearly 4,000 machine learning and AI programmers in Bangalore alone with our tech savvy PM acknowledging the fact that AI is soon going to dominate human lives and in the process create more jobs. With such rapid developments in the fields of IT and hardware, the world is at the threshold of a 4th industrial revolution driven by big data, high computing capacity, artificial intelligence and analytics.


But there are a few roadblocks that hinder AI’s swift progress in India. AI-based applications have largely been driven by the private sector. Here, what we need to realise is that both private and public sector investments are vital for the AI industry to grow. Despite several initiatives like Skill India and Make in India, government policymakers need to work towards making AI a critical component of our flagship Make in India, Skill India and Digital India programs. This can happen when we offer incentives to manufacturers, creating regional innovation clusters for manufacturing automation and robotics in partnership with universities and start-ups. Another crucial and I think the most important hindrance is internet penetration. We are struggling with internet penetration which stands at 15.1%, compared to China’s 48% and 84% in the USA. And finally, like I’d already mentioned above, India’s AI development is also paying the price owing to talent crunch. Technical skills have to evolve to stay ahead of the curve in today’s fast-paced digitally-driven world.


Our government must provide the necessary policy framework and incentives, including direct funding to select companies, start-ups and research institutions to ensure targeted capacity development. In order to not fall behind in the run-up to the AI revolution, we need to adopt aggressive policies to drive AI innovations and proliferation in sectors beyond merely consumer goods and information technology.

There are 20 billion connected IoT devices at present and the number is likely to rise to 75.4 billion in 2025, according to IHS Markit. With actionable insights from IOT data, all participants in an IoT ecosystem can not only improve decision making but also build new business models or revamp existing ones to increase profitability and growth.


For instance, in an IoT ecosystem involving an automobile seller, insurers, and coolant manufacturers, the automobile retailer is the primary participant, while the insurers and coolant producers are secondary participants. Although the customer who buys a car goes through only one purchase cycle with the primary participant – the retailer – she might have multiple purchase cycles with the insurer and coolant manufacturer. The primary participant in this case invests in the IoT infrastructure and collects data on the actions and choices of the buyer. The secondary participant merely uses the data from the primary participant’s IoT infrastructure. This brings us to a key question: is the IoT ecosystem a zero-sum game?


How you capitalize on IoT investments matters more than how much you invest

Worldwide spending on IoT is expected to grow from USD800 billion in 2017 to USD1.4 trillion in 2021, according to IDC. It is not the quantum of investment that will separate the IoT winners from the followers, but rather their choice of investment avenue and how they capitalize on it. So how can primary and secondary participants mutually drive value from an IoT ecosystem?


If you are a primary participant, build alliances and become a data custodian

Let us examine a few use cases to see how primary participants can drive value from the IoT ecosystem. The ability to forge alliances is a leading success factor. HP, through its Instant Ink campaign, has connected ‘ink level’ sensors to its printers. When the ink is close to depletion, HP uses the sensor data to automatically supply customers with cartridges through its channel partners –  the secondary participants. What is the benefit of such a syndicated approach? First, it serves a larger customer base and improves overall customer service. Second, the primary participant can share data with select secondary participants for a premium. The alliance ensures data and customer insights for secondary participants, giving them a competitive advantage.


You can also derive actionable customer insights from the collected IoT data sets to help secondary participants improve their offerings. This helps them offer exclusive services and move from a product-centric model to a customer-centric one. As an exclusive partnership limits the customers’ choice to buy products from secondary participants on other platforms, secondary participants must study the market to ensure the benefits of exclusiveness.

You can also collect data beyond the point of purchase and monetize the data by selling it to select stakeholders along the value chain.  But, you must protect sensitive data, ensure privacy, and assess security risks thoroughly. If a secondary participant has a strong brand, you can also share data with the secondary participant to leverage its brand equity.


If you are a secondary participant, fine tune marketing and sales strategies

Secondary participants can also build IoT-based business models to drive value from the ecosystem. You can buy data from primary participants to create customer-centric marketing and sales strategies. Consider an IoT ecosystem involving a refrigerator manufacturer and associated products and services providers. With data flowing from the connected refrigerator, grocery and power retailers can accurately study consumption patterns to fine tune marketing, sales, and procurement strategies.


In an IoT ecosystem, a secondary participant such as an energy retailer can enter into alliances with multiple primary participants such as thermostat, air conditioner, and television manufacturers to draw consumption data and offer better discounts and services while enhancing planning.


If you are entering a new market, you can ally your business with primary participants that have established brands in order to build your brand. For example, a new diagnostics services brand can collaborate with an established wearables manufacturer to offer services exclusively through the wearables brand, at a discount, to leverage its established market base.


Driving value from the IoT ecosystem is all about using data to increase customer satisfaction and loyalty

For participants, driving value from an IoT ecosystem is all about providing contextual and customized products and services to increase customer satisfaction and loyalty. To gain optimal benefit from the ecosystem and enhance efficiency and agility, it is important for both primary and secondary participants to collaborate with each other, and unlearn old processes while learning new ones

Welcome to the first Virtual Roundtable. This series will bring together five subject matter experts from five different verticals to talk about an advanced technology. The series will focus on use cases, applications and case studies in emerging technologies. The experts on this panel are in conversation with Om Routray, Lead, NASSCOM Community.  


Rama C Dash is a Healthcare thought leader with deep expertise on public health and AI aided health-tech systems. He has over 20 years of experience in software solution building, delivery and professional services for international markets and global clientele. Currently Ram leads the Watson Health business unit of India Software Lab, IBM. Additionally, Ram is also responsible for successful implementation and delivery of IBM Watson Health's human services solutions for customers in Asia Pacific, ANZ and Greater China Group.


Hari Balaji is the co-founder of Egregore Labs, a Fintech Startup. Hari is an IIT Madras and IIM Ahmedabad alumnus with a decade of experience as a quantitative strategist in front office Capital Markets roles across London, HK & Singapore primarily with Goldman Sachs. Hari ran a bespoke derivative solutions group for GS out of Singapore prior to moving back to India in Jun 2017 to start Egregore Labs.


Mayank Vatsa is currently an Associate Professor with the IIIT-Delhi, India and Adjunct Associate Professor at the West Virginia University, USA. He is also the Head for the Infosys Center on Artificial Intelligence at IIIT Delhi. His research has been funded by UIDAI and MeitY, Government of India. He has authored over 200 papers in refereed journals, book chapters, and conferences.



Ramakrishnan M is the Chief Marketing Officer of Intello Labs. An ex-Consultant (from Accenture), he is focused on building the market voice of Intello Labs and developing their Partnerships & Alliances ecosystem. He has 17+ years of B2B experience. He launched the first suite of Analytics products in Absolutdata. He is a Mechanical Engineer from DCE (Delhi College of Engineering) and MBA from MDI, Gurgaon.


Ankur Dinesh Garg is CEO, Hotify AI and committed to help Enterprises Adopt AI Successfully. Ankur has expertise in – Technology, Business and Marketing he has worked across Digital Publishing, Retail, e-Commerce, BFSI, Media and ITES sector. Ankur is a graduate and masters from IIT Bombay in Mechanical, Design and Automation.



  1.        We can’t really talk about AI without mentioning IBM Watson. So, Rama, let’s start with you. How is IBM Watson using artificial intelligence to improve the healthcare? Also, if you can give us a few other remarkable use cases from the health sector.


Number of ways – Let’s just talk about one specific and compelling scenario…

The accepted model in medicine has been: Controlled trials to guidelines to doctor recommendation. 


Now as you can readily see, health information is flowing in unimaginable volumes from different directions – for example via monitoring (IOT) devices (e.g. fit bit), health apps on mobile phones etc.  Patients are interacting with each other and doctors via technology and creating massive datasets, which typically forms the healthcare ‘big data’. Patients are increasingly contributing directly to the EHR through bodily parameters, words or biometrics from their devices.  Healthcare research is at its peak, which contributes to massive amount of data itself e.g. approximately 44000 oncology research papers alone were published in 2015-16. Healthcare data is exploding and by 2020, it is expected to double up in every couple of years. Healthcare big data is great, but what can we do with it to improve healthcare?


Big data is nothing new, but earlier the healthcare world did not have proven AI capabilities to ingest the unstructured and voluminous data, curate it and put it into a condition where patterns can be recognized and meaningful insights can be derived within a very short period. That is what Watson is able to do – so it is such a great example of what an AI platform can be.


Additionally, through such a platform, knowledge (be it medical or otherwise) which is typically centred in urban centres and in a few countries, is getting democratised. For example: Through Watson for Oncology, an Oncologist in any corner of India will have access to the same knowledge and insights which an Oncologist practising in say, Memorial Sloan Kettering, NY has.


It is this ability to quickly generate actionable insights which are also backed up by evidence is what is going to make Watson or similar platforms to make a huge impact in healthcare - be it cancer treatment, in clinical trials, in genome research or drug discovery.


Use Cases:


a. Life sciences researchers are using IBM Watson for Drug Discovery to make scientific breakthroughs and increase our knowledge of disease – faster than ever before. Watson can accelerate identification of novel drug candidates and novel drug targets by harnessing the potential of big data.


Barrow Neurological Institute applied cognitive computing with IBM’s Watson for Drug Discovery to identify 5 novel RNA binding proteins altered in ALS. From nearly 1,500 candidate proteins, Watson helped predict those most likely to be involved in the disease.


b. Watson For Oncology

Cancer is a complex and sometimes elusive disease, and each person’s unique cancer may present unexpected challenges. For this reason, physicians often seek opinions from other experts to either corroborate a treatment plan or consider a new approach based on the supporting evidence -- potentially boosting confidence in their treatment decisions.


In a double-blinded study, the doctors at Manipal Hospitals found that Watson was concordant with the tumour board recommendations in 90 percent of breast cancer cases. The study was presented at the San Antonio Breast Cancer Symposium.


Watson for Oncology demonstrated concordance rates of 96% for lung, 81% for colon and 93% for rectal cancer cases with a multi- disciplinary tumour board in India.


c. One of the largest health plans in UScreated a cognitive call centre using Watson to dramatically enhance the user experience for healthcare providers. By detecting relevant information in natural language, the solution interacts with callers in an easy-to-use format that mimics human conversation.



  1. Prashant, somewhere in your mission statement, there is a line ‘’we want to predict the future”. Ironically, Richard Thaler won the Nobel this year highlighting irrationality and behavioural economics. How are you using AI to counter that?


While individuals possess and exhibit their own unique irrationality, the future depends on their group behaviour. This is especially true in financial markets, where a collective action by a group can have a meaningful impact in the short-term. In a group, some biases tend to get mitigated while some others are accentuated. Understanding the opinions of a large enough, representative, sample of individuals allows us to predict group behaviour.


Collecting active and passive opinions of individuals at scale is possible today through the growth of social media and digitization. Likewise, advances in AI and big data allows us to manipulate, process and extract predictive insights from these large pools of data.  


  1. Many are optimistic that AI would enable Indian IT to move up from being the BPO hub. First, from the academic standpoint, Mayank, how prepared are we to grab this opportunity? Second, what sort of efforts are being made in research to ensure that we are innovators and not replicators in AI domain?


I would answer this question on each point separately:  


1. I would call India as land of opportunities for AI researchers. To establish my statement, I would quote a recent incident. I was at ICCV2017 (International Conference on Computer Vision - one of the big conferences in the area) and on one of the demo booth, I looked at self-driving truck demo video. I was impressed with the video and I asked the representative of that company what he thinks of their truck on Indian roads. This person, who himself was a researcher, said that the technology is not yet matured enough to drive on Indian roads and he recognized the challenges that the technology has to address. Similarly, I can quote a lot of other incidents where we have seen that off-the-shelf AI solutions may not solve India specific problems. So, certainly, there is good scope for Indian IT sector to move up in the ladder. 


2. It is interesting to note that many researchers (worldwide) in the area of machine learning, pattern recognition, and artificial intelligence are Indians. Therefore, I strongly believe that India has the talent pool to work on real world problems related to these areas but most of these researchers are not in India. If we talk about preparedness in India, then we may not be well prepared. In general, my observation is that we are still users of AI technology not the creators, barring some exceptions. A simple example is to look at the research papers in top conferences and journals in related areas - you would see majority from US, China and EU countries. While we contribute in 20% of world’s population but we do not contribute 20% in world’s top-tier research publications profile, particularly in AI technologies. I will add another pointer; if we look at the top US universities, most of them have focused Machine Learning and Artificial Intelligence programs to prepare next generation workforce. On the other hand, in India, we can hardly find such focused programs. To the best of my knowledge, we do not have a high quality focused MTech program in Machine Learning or Artificial Intelligence Program which can be compared with CMU’s MS-ML Program.  


3. In India, set of focused efforts from government, academics, and industry has to be initiated to push the envelope and take initiatives. The Government should recognize Machine Learning and Artificial Intelligence as next generation technologies and start large dedicated programs to support research and development. Academia should start focused programs which are thorough, rigorous, and industry ready to prepare the workforce. Industry should recognize the talent pool, support academic institutions in setting up strong programs and labs, and support research. Further, industry should setup research facilities in India to conduct ML and AI research on India specific problems. At IIIT-Delhi, we are starting a rigorous and industry focused MTech with specialization in AI in which we are first building breadth wise foundation of AI and ML and then depth wise expertise in sub-areas of AI. We are excited about the proposed program and hoping that other academic institutions will also start such a program.


4. At the end, I would like to emphasize that we have a large talent pool. A concentrated effort in the right direction can lead us among the top players.


  1. When we talk of advanced technologies, such as AI and blockchain in agritech, our biggest concerns are about feasibility and then scalability. How is Intello Labs using AI and how successful have you been in taking it to the farmers?


Great question, Om. As you are aware we do quality grading of commodities using images and AI.

I think we have clearly crossed the question of feasibility; we have trained our algorithms through thousands of images...Across different categories...Across locations.


We have enough evidence of successfully implementing our solutions across multiple client situations. So the concept-selling is becoming easier now. People see what we have already done, and then question about their own items or their unique challenges.


Coming to your second point...Fortunately for us, what our clients truly like about our solution is the scalability; one can easily install our application on a mobile phone. We work with food retailers and agri-traders who find our mobile-based solutions lot more reliable and scalable than the current manual practices and all the errors and malpractices involved therein.



  1. Ankur, you are doing something that can be seen as ahead of time. Hotify already offers AI as a product, service and also as a platform. Tell us more about your offerings. Who is your target clientele and when do you think SMEs will be able to afford such advanced technologies?


Yes, we do believe we are ahead of time, not much though. We offer unsupervised intelligence from enterprise data via a black box AI Engine which is like a self-assembling lego box of Deep Tech components. Depending on the data it organises itself into best suited intelligence pipeline and generates hands-free unsupervised intelligence on the other end.


Our targets are enterprises across sectors and markets, but our approach to them is via partner companies with extensive domain expertise, years of experience and strong client relationship but NO or LOW knowledge of AI.


Our offering becomes very valuable to them as it makes them much more competitive and ahead of the curve solutions at a time when their survival is being questioned by AI Technology based startups venturing into their solution domain. 


We are scheduled to launch our DIY platform by April 2018 and it shall enable practically anyone to afford sophisticated AI backing their solution and service offering. 


Click to read other virtual roundtable stories. Share your thoughts and suggestions about the series in the comments section. If you would like us to conduct a roundtable on any new technology, let us know at


Mobile shopping is getting easier thanks to apps and mobile wallets. Since 2010, mobile commerce has grown from 2% of digital spending to 20%. Shopper recognition, engagement, and personalization today is driven by the retailer’s specific apps and at least for now, those apps provide the singular method for creating a personalized experience in store, online, etc.


In addition to personalized shopping experiences, shoppers will increasingly be able to check-out using mobile payment methods at traditional cash wraps in store and at mobile, in-aisle stations as retailers add mobile check-out around store with associates. With the expansion of advanced bar-codes and imaging technologies for scanning products, faster alternative checkout methods will continue to grow across the retail sector.


For the retail associate, mobile applications that enable access to product information and inventory information in retail-time, while interacting with the customer through product and assistance requests will be an in-store mobile priority. This mobile capability will also facilitate friction less returns.




Internet of Things (IoT). IoT is a collection of smart, connected devices or products that, when pieced together well, can yield new functionality, reliability, utilization and capabilities that were previously not deemed possible. Globally, it is expected that 34 billion connected devices will be in market by 2020, 24 billion of which will be IoT devices with nearly $6 trillion being invested in IoT in the next five years. Why the growth? Several key factors including a decrease in sensor costs, a decrease in the cost of processing, a decrease in the cost of bandwidth, and finally, a decrease in the cost of storage. Incidentally, many of these same factors are what limited RFID’s growth in the mid-2000’s.


What about IoT’s application within retail? From a shopper perspective, shoppers may quickly scan a product and pull up product information, reviews and availability. Shoppers may also receive personalized location-based digital coupons in store. With IoT, retailers could have instant visibility regarding in-stock positions on shelves and in some stores, use robots for replenishment (e.g. grocery). Retailers may also track product freshness, aging, and history (e.g. track-and-trace). Also from a retailer’s perspective, IoT enables dynamic pricing and even auto-replenishment for consumers with smart pantries.


Retailers will continue to experiment with IoT in 2018 and the technology will become more pervasive to address specific desired business outcomes. Of course, with IoT, AI, ML, etc. comes the need for more dynamic, cloud-based, flexible systems and architecture which will fuel new technology investments in this space. IT spend will escalate in 2018.




What in the world is a Chatbot?! Chatbots are a blending of artificial intelligence, natural language processing, a data set, and a human interface. Chatbots allow for two-way communication between a “bot” and a retail shopper and have varying degrees of intelligence when communicating with a user (from scripted and programmed to moderately intelligent).


Engagement with a chatbot begins with a messaging platform. The shopper sends the chatbot a message, which is passed to a language service that decodes the text by identifying objects and actions. Once the message is decoded, the artificial intelligence  determines the response. The response can be predefined or complex using trained machine learning solutions.


Chatbots have been implemented by an assortment of retailers. In one case, the retailer’s chatbot gives shoppers a curated experience in finding holiday gift ideas. It asks compelling questions to gather information on the gift recipient, provides an almost natural discussion while conversing, and recommends a collection of gifts depending on the answers provided by the shopper. With another retailer, chatbots use directive questions to create a style profile and make fashion suggestions. Responses are predefined and work very similar to navigation on an e-commerce website and filter using gender, age, style, etc. to suggest outfits.


Bottom line: Chatbots will be used to create tailored shopping experiences and inform marketing campaigns utilizing previous shopper interactions with the chatbot to notify customers about current promotions or sales, and suggest recommendations to shoppers from previous orders. Bring on the bots!




AI for retail is not just about chatbots. It's defined as smart machines that extend human capabilities by sensing, comprehending, acting, and learning, allowing people to achieve much more. Consumers now routinely use AI-driven technology features such as digital voice assistants. An impressive 84% of 14-to-17 year olds currently use or are interested in using the voice-enabled digital assistant in their smartphones. And, interest is not limited to younger generations. About one-third of consumers in every age group are interested in these features.


There are many benefits to interacting with computer based applications rather than human advisors. They are available any time, demonstrate less bias, are faster to engage and provide service (limited wait time), and yes, even communicate more politely (tough for a computer to bring emotion into a digital convo)!


According to Fjord Trends 2017 report, “while AI has evolved exponentially, in 2017 we will see a shift in organizations’ approaches to developing products and services as emotional intelligence (EQ) becomes a critical AI differentiator." AI is central to understanding the needs and desires of different consumers, personalizing services, and driving demand.


From a retail personalization perspective, AI enables a curated shopping experience online. Think two different shoppers visiting an e-commerce site and receiving completely different and tailored experiences, from personalized product recommendations to personalized pricing and offers. Of course, this is only possible if the data exists on the shopper and their prior behavior on the site (hence data and analytics still an investment priority to support AI).


No doubt, AI will be front and center with most major retail technology providers in 2018 and on the list of “capabilities to investigate” for most retailers.




Bitcoin, blockchain, distributed ledgers, cryptocurrencies – the developments around these technologies are proceeding at a rapid pace and while they are not prevalent in retail today, they will be very soon. Time to get up-to-speed on the topics. Here are a few stats and facts on Blockchain from my good friend Nikki Baird, Managing Partner at Retail Systems Research.


High transaction costs constrain a market. That’s basic economics in the sense that a high price for a good or service depresses demand for the good or service. Compare mobile payments in China vs. the US: according to Hillhouse Capital, in partnership with Kleiner Perkins, a US credit card transaction costs over 200 basis points, vs. less than 50 for WeChat or AliPay. In 2016, the mobile payment market in China reached $5 trillion, vs. $112 billion in the US.


One of the reasons why Bitcoin and other crypto-currencies are getting such buzz is because they offer an opportunity to bypass “expensive” forms of payment for something much cheaper – if crypto-currencies can keep their transactions “cheap.” So there is the potential for a lot of demand for crypto-currencies from a consumer perspective, but right now it’s a pretty complex process to set up a digital wallet, gain access to a crypto-currency exchange, and start buying up coins. And the security of some of these crypto-currency markets is not assured.


What are the Blockchain implications to retail?


Product Track-and-Trace: The Blockchain framework allows all members of the value chain – supplier, manufacturer, retailer, consumer – to have visibility into products from source through production to store to consumer. Track food from source to plate. Ensure products were created in environments meeting specific regulations and/or requirements.


Counterfeit Goods: Consumer goods can be certified with Blockchain’s anti-counterfeiting solution (Block Verify) for pharma, luxury and electronics. Counterfeit goods continue to be an issue for both retailers and CPG manufacturers and Block Verify allows for detection and identification of counterfeit items.


Product Warranties: Many companies today are already using a product called Warranteer to move product warranty information into the cloud via Blockchain. This allows the warranty to be easily updated and transferrable. The consumer “owns” the warranty and can manage it, eliminating the need for retailers and CPG companies to get stuck in administrivia.


Smart Contracts: Blockchain is being proposed in terms private Blockchains that update automatically over time, recording all of the actions taken in regards to a contract, whether the buyer, the seller, or third parties acting on either party’s behalf. For retailers this will mean less paperwork, more digital exchanges of information, and smoother transactions across borders and across multiple parties – once it gets out of proof of concept phase.


Trade Promotion Management: Trade deals between retailers and consumer product suppliers today largely leverage spreadsheets, databases, e-mail exchanges and for some, more sophisticated trade promotion management or “deal” software. The process is cumbersome and largely inefficient, resulting in over- and under-payments and confusion over product promotions. The TPM space is ripe for transformation using Blockchain – and the solution will enable faster recoveries for retailers for deals executed on specific promoted products.

Every day, all of us are bombarded with marketing messages on hoardings, print media, SMS, mail, TV and phone calls that are of no relevance to us and are in fact a nuisance. Google ads make a lame attempt at personalization but are actually an equal nuisance. Personalized messaging based on customer preferences is the holy grail for marketing professionals. Modeling customer preferences and predicting customer behavior are therefore important use cases for AI and should be of great interest to marketing professionals.


Recently I tried working on the marketing use case published on the the IBM Watson blog  at with my preferred Machine Learning tool TFLearn ( ) and I got good results. I would like to encourage all marketing professionals to try cracking this use case on their own. This will motivate you to start thinking of Machine Learning use cases that can enable you to reach out to your customers in a more personalized manner


If you need a little help to get you stated on your journey to learn Machine Learning, I will be happy to conduct a workshop for your team. Please mail me at if you are interested.

This post is dedicated to the Human Resource Community that is wondering how to grapple with the coming Tsunami of Artificial Intelligence that is expected to sweep across the world soon. The workforce will need to be up skilled and job losses can be expected. On top of that, HR maybe called upon to deal with a mixed workforce comprising of humans and BOTs. The good news is that AI is slowly moving from the lab to the field and will soon be a part of every professional’s toolkit. Recenty I tried working on the HR use case published on the IBM Watson Blog ( ) with my favorite Machine Learning tool TFLearn ( TFLearn | TensorFlow Deep Learning Library  ) and I got good results. I would like to encourage all HR professionals to try cracking this use case on their own. This will convince you that there is no black magic and everyone in the company can learn Machine Learning and start thinking of use cases that can benefit the company.


If you need a little help to get you stated on your journey to learn Machine Learning, I will be happy to conduct a workshop for your team. Please mail me at if you are interested.

In 1998, we did a reconditioning of a 500 KVA stamford Alternator. After 10 months of it's operation the alternator broke down but we all (including the Factory maintenance team) had a great trouble in tracking the sequences of events that caused this failure. And the biggest question in every once mind was how do you avoid this for rest of the other Alternators.


After going through the maintenance logs of various sections, we found the root cause was due to an elongated duration of circulating current in the alternator while operating in parallel with the other Alternators. Since then I have seen many different instances where most of them could have been avoided, saving not just the cost of repair for you but also the loss that you incur when your core machine that runs your production line is down. 


Hunting for a solution to this problem, we started our journey to work on a solution based on IoT devices, smart sensors and high throughput platform. We came up with a design for the platform, hardware components that could be used and the sensors that would feed the digital output for us. I wish we had access to these kind of devices 20 yrs ago. We invested close to 4 months on development of Hardware and its Platform because that is the heart of the system and it needs to be perfected. Spent long time on continuous improvement of the Platform to get the response time, reliability and accuracy by testing improving testing improving testing......


Finally last week, we had a great deal of success while conducting our field test on this platform that is build to process the analog data captured by IoT sensors installed on a standard induction AC Motor. Below are the videos that were recorded during our 4th field test: Based on the field test and results, we were highly confident of the performance, tolerance of the platform and the IoT library that we developed to operate the IoT device/sensors. To start with we have three flavor of this device called as Type 1, Type 2 & Type 3 (These were the terminologies that we used during our development and hence continued the same but I wish we can name them better later).


Type 1 & 2 are focused for industrial purpose and they come with 16 or 8 analog measurements that can be taken at regular intervals of about 30 seconds. The state of the machine would be calculated based on the formula that the users can define with the 16 or 8 Analog parameters as operands in their formula. If the state of the machine is determined to be BAD state for X (User configured) number of analog reading from the machine, the users can configure to allow the platform to cut off the machine and notify the the groups that they configure in the system. Type 1 & 2 can control about 4 or 8 machines.


In some cases, the shut procedure of a operating machine is a series of shutdown procedure before it is taken out for maintenance. In those cases, the device can be configured to record only the analog reading but take NO action. These analog reading would in turn send notification when the data falls under BAD state based on which the staff is expected to response. To complicate things further, it was required to take action even here by executing the series of shut down procedure that one would have done it manually. For such cases, the platform has the logic to interact across the monitoring IOT devices as Parent and Child. Under BAD operating state, the parent would be responsible to send the series of control commands to the Child IOT device, which would in turn operate the respective relays to execute the shutdown procedure.


And we extended our Platform to Type 3 that is more for direct control of the connected systems such as security alarms, electronic devices at home, Webcam etc..


We do have more room to do some research with Amazon Echo, Google Home accept "Start secondary plant of Unit 1", "What's the temperature of Feeding machine at Unit 2" etc commands but that's future and I guess its already here. We would work in this space once we have this product going in the market.


Please do visit us @

Cropin provides Farm-Businesses/Growers with farm management software and mobile apps, which enable them to do connected, and data driven farming. It allows them to take advantage of real time data and insight from farms (an accurate view of their operation throughout the entire growing season) and to improve financial, operational and agronomical aspects.Their solution includes mobile apps, crop analytics, sales insights and warehouse integration.

Krishna Kumar is the Founder and CEO of Cropin. From leaving his job at GE to to digitizing over 2.1 million acres of farmland and enriching the lives of 500,000 + farmers, he has come a long way. Cropin have been funded by Singapore-based early-stage fund Beenext and Denmark’s Sophia Investment ApS & Ankur Capital etc. He is here with us till Saturday (18th November) to answer your questions regarding agritech, funding and the Indian scenario.


Go on, post your question in the comments below.


Artificial Intelligence is a new trend in the world of advertisement and recommendations.  It is effective at making an impact on social media to allow businesses efficiently discover, engage, and learn from their followers.


There are many ways of summarizing Artificial Intelligence; one of which is a Social Artificial Intelligence, which enables one to collect and select user-generated content, and data from social media channels through customer history. This allows them to generate relevant content and as a result, vouches for a better user experience for the followers.


Now-a-days, Social Networks invest in Social AI Technologies. Many brands have yet to turn to social AI to engage their audiences, target new customers, and analyse huge volumes of social data collected in the process.

The association of AI with marketing mediums, such as social media, is gradually proving to be one of the major advancements in the field of digital marketing.


How AI is changing the Social media marketing scenario?

There is a need to maintain good and healthy balance between Human Intelligence and Artificial Intelligence.

On some grounds, machine do better than humans and on some, humans outperform the machines. Using Artificial Intelligence in social media will give businesses a better understanding of the digital market.


Social AI has the ability to provide, overall, a better social experience. The social network has already incorporated Artificial Intelligence as part of the platform in many innovative ways, from facial recognition to customized news feeds.


How Artificial Intelligence is being applied in Social Media?



LinkedIn uses machine learning algorithms to offer a better job-candidate match, creating a just environment for both the parties.

LinkedIn uses this to rate candidates for companies based on prior hiring patterns, location, past work experience and job descriptions.



Pinterest can identify object recognition to boost pins, and product recommendations, ensuring that only the relevant information reaches you.

This, paired with Pinterest’s Visual Graph, allows for image and search recognition, based on user-specific information.



Automation is a powerful way to connect with prospects. In a way, it allows one to manage time effectively, which humans lack at. Simply put, humans have limits, unlike machines. Chatbots provide faster customer service resolution, as well as optimum support, based on previous experiences with customers and also the immediate response.

Facebook as well has introduced Chatbot in its chat messenger. It uses pre-requisite data to start a conversation with clients and steers it into the right direction using its response-based logic.


Facebook Facial Recognition

Facebook has been at the forefront of what AI can do for social media by incorporating a variety of AI technologies that continuously improve the overall experience for its users.

Through years of Research & Development, it has developed a facial recognition utility that facilitates tagging a person in a picture on Facebook. It then uses, its artificial intelligence algorithms to come up with user-specific offers and deals for the person.



The potential of AI & its application in business, marketing & sales is seemingly vast. It is currently one of the most advanced tools in the field of digital marketing that can take your business to new heights in no time.


It has certainly changed the way we consume information on internet & social media, and it wouldn’t be too far-fetched to say it will change the world in near future.


So if you are looking for the rights tools to get your business integrated with AI, allow USM Business Systems to take care of it.

We at USM are leading AI company USA. We provide services in retail, banking and finance, e-commerce, health care, marketing and sales, telecommunication.  We also provide Artificial Intelligence Applications USA.

 Artificial Intelligence is still a little understood concept. Most people still imagine a humanoid when they think of AI. Precisely why, we are trying to bring out specific use cases of AI to explain the technology better. We talk to Bharath Rao, CEO of Precily to understand how they are using AI. 


1. Tell us about Precily, how is it unique?

Precily is an AI based text summary engine that can be deployed on a variety of platforms.


  1. Can you mention some specific use cases? Something in the making that is not launched yet?

Precily churns text generated in different verticals anc converts them into an easy to consume format. This saves executives, students, researchers considerable time and money.

Further, Precily can convert long business discussions into brief snippets obviating the need for taking notes


  1. Given that AIs can be trained in any language, would we see Precily developing non-English abilities too?

Presently, Precily is focussed on the English language documents. However, the non English speaking audience is at least four times as large in India. Regional Indian languages are an important milestone for us achieve in the future.


  1. We recently did a story on DheeYantra which uses AI for conversations in regional languages. The language and AI segment is still a niche. Did you face any talent issues while developing your solution?

AI needs to be augmented with inputs from human experts. We think training AI engines to learn from human experts and real users to perform on-the-go is where top class talenet is needed.


  1. What sort of partnerships are you looking for?

Firms that can facilitate converting these opportunities into monetizable streams would be ideal partners of Precily.


Read about other brilliant startups in the series with the pitch


Happy to share a brief about our CEATEC, Tokyo conference participation last week. CEATEC is Asia’s largest IOT conference that attracts over 150K visitors each year. This edition was special as we had India showcase at CEATEC where 27 companies from India participated. 10 Indian startup companies that got free pass to attend conference with all travel related expenses paid for (these were selected from 92 nominations). And 17 Indian companies that paid to be part of India showcase.


  • India’s showcase with 27 companies was largest from foreign countries at CEATEC this year
  • Opening ceremony saw METI and Home Minister of Japan make mention of India show case at this year’s event
  • During this ceremony they invited Indian Ambassador to be on stage for a commemorative photo with the Ministers
  • This ceremony was attended by CxO’s of Honda, Toyota, Sony, Panasonic, Mitsubishi, Hitachi, and many other Japanese giants
  • Ceremony had Hindi translation on the live screen besides English – which I think was very good gesture and huge boost for India at this high stage
  • We did a India day on 4th Oct that saw opening ceremony where H.E. Indian Ambassador to Japan made the speech along with Chairman Panasonic and Director General (DG), METI
    • DG METI was wonderfully articulate about Japanese hardware and Indian Software marriage - a matter of ‘when’ rather than ‘if’ ; he spoke from heart and was the star

  • A special roundtable where Indian companies sat with Japanese companies discussing partnership opportunities with Society 5.0 initiative from Japan
    • Session was moderated by Mckinsey, Japan
    • Deputy Chief of Mission (DCM), Indian Embassy in Japan also participated and spoke at the event
  • India showcase:
    • We had Bosch and NEC India also part of India showcase adding foreign element to India story
    • Feedback and interest from participating companies was very good some reporting very nice foot fall and good leads ; some companies reported more than 200+ footfalls
  • Networking dinner hosted by CEATEC ; India showcase was a star at the event
  • Media interest was very high with one Nikkei business paper publishing a full page story about India show case that is extremely reputed and well-read paper ; with many other online stories


Our main aim from this participation was to break the stereotype associated with the industry as a low cost supplier of  human bodies to the west. And, I think it turned out to be a very wise decision on that count as it promoted Indian IT to be at a centre stage in Japan biggest show’s in tech with CxO & Chairman level people in attendance. For a change it felt nice to be part of Indian IT in Japan last week.

I am pleased to update you all with the progress made with China – Dalian province on our cooperation arrangement. We hosted a daylong conference on 20th Sep with Govt of Dalian on IoT cooperation after we signed MOU inking the intent a day before.



IoT Conference:

  • Theme was to Co-develop for the global markets leveraging our respective strengths.
  • 32 companies from Chinese side participated with about 17 companies from Indian side
  • The event was hosted by NASSCOM and Govt of Dalian in association with Embassy of India, Beijing
  • There were 4 presentations from Chinese side and 4 from Indian side
  • 5 Chinese companies from manufacturing space unequivocally said that they feel threatened as manufacturing is fast becoming low value additive job and they realise that they would have to become smarter else will be out of business
  • Almost all companies echoed that they are ready to partner with Indian companies to make their offerings smarter
  • Even though Robotics firm could not show their bot Mitra in action as they were not allowed to carry the battery by airlines - still stole the limelight as it became an easy example to show how Chinese hardware and Indian software could gel well together
  • Event was covered by Chinese and Indian media with several stories emanating out of it (link below)
  • Local TV news also covered the conference :


MOU with Dalian:

  • A day before the conference we signed MOU with Dalian Govt to promote IOT collaboration between the two ecosystem
  • The signing ceremony was attended by the Mayor and Vice Mayor of Dalian signed the MOU (pic enclosed)
  • Later they hosted a dinner for the Indian contingent


About Dalian:

  • Is no# 1 place for doing IT-BPO in China
  • Indian companies have 8000 employees based in Dalian out of their total strength of 20K in China
  • Dalian is tipped by Beijing to be AI hub China and we want to cooperate with them on the same topic


B2B cooperation with BEST city:

  • BEST city is a smart city or one of the district in Dalian that has sprawling IT campus that houses many global companies including Wipro base
  • This city has mountains, lakes, beach, sea, International school, hospital, and adorable housing, etc.
  • BEST city wants to build AI park and seeks NASSCOM cooperation in realising this dream
  • Wipro has built an envious campus and ramped up 800 people in 2 years with very proactive support from the BEST city
  • All other chapter members who visited Wipro campus wished they were also based here


Cooperation terms with BEST City:

  1. NASSCOM will try and consolidate all its members to BEST city under NASSCOM IT Park ;
    1. companies present in this meeting gave principal nod to NASSCOM with their interest ;
    2. NASSCOM will negotiate package that can be extended to all its members
    3. Whereas, Indian industry is scattered all over the place and in turn pays 5 times the cost for lower amenities


  1. Offer for the SME + Startup companies (not present in China)
    1. Will be available to companies who are ready to invest the theme for which we have signed the MOU
    2. Best city will help locate customers with whom they can partner from the manufacturing space to co-develop on IoT stack
    3. We will negotiate to make them offer plug and play office, with residences, tax breaks, training grants, etc. These terms are being negotiated with them
    4. They will also offer assistance in the form of long term low interest loan for starting up expenses


3. Seed fund

  1. BEST city will offer seed fund by pooling in investors from China who are ready to invest in Indian startup




Short term business opportunities:

  1. Mitra robot:
    1. BEST city will buy Robots for its own use after successful POC
    2. BEST city will help Inventico set up and boot strap in BEST city
    3. They will offer loan for startup assistance
    4. Next steps: Actual demo and POC to be setup and thereafter discussion plan ; they have okayed initial investment for this venture
  2. AI collaborative platform for Chinese buyer and Indian seller
    1. This platform will be developed by Zeta V a startup ; initial investment has been assured
    2. They seek some share from NASSCOM and we’ll have to evaluate the options
    3. Project plan and business plan discussion to boot strap have been setup for mid Oct with Zeta V
    4. They will provide office and house for Zeta V executives
  3. Smart parking solution
    1. PoC to be conducted by Wipro
    2. On successful conduct of POC idea will be invested in by BEST City for its own use


Our endeavour would be to close in on the above opportunity and take them to conclusion in next couple of months.


Chinese media stories.

Video link:


Stories in Indian media


NASSCOM inks pact with China HTML
The Hindu Business Line
National, 21 Sep 2017

NASSCOM inks deal with Chinese city govt to push AI
Financial Chronicle
Delhi, Mumbai, Bangalore, 21 Sep 2017


Indian, Chinese IT companies discuss avenues in artificial intelligence
The Economic Times
Online, 20 Sep 2017


Indian, Chinese tech firms explore tie-ups
Business Standard
Online, 20 Sep 2017

Indian, Chinese IT companies discuss avenues in artificial intelligence

Financial Express
Online, 20 Sep 2017

NASSCOM signs agreement with local Chinese government to push for AI and IoT

Online, 20 Sep 2017


NASSCOM signs agreement with local Chinese govt to push for AI
Online, 20 Sep 2017


Articles stating that NASSCOM has signed a major agreement with an influential local Chinese government to provide a head start for Indian firms in the field of Artificial Intelligence and Internet of Things. On the side lines of the first India-China Dalian IoT (Internet of Things) Conference, NASSCOM signed a framework agreement with Dalian Municipal People’s Government, allowing more Indian companies to penetrate huge Chinese software market. Mr. Gagan Sabharwal, Senior Director, Global Trade Development, NASSCOM is quoted as saying that India and China have both leveraged our human capital to become world leaders and are heralded as the fastest growing economies. He states that with the new digital wave, both countries today have a unique opportunity to merge hardware and software together to create smart solutions for the world.


Indian robot Mitra made in China steals the show at IT event
Mint | K J M Varma
Onlone, 20 Sep 2017


India's robot ‘Mitra’ could be symbol of AI cooperation with China
Hindustan Times | Sutirtho Patranobis
Online, 20 Sep 2017


Meet Mitra, the robot designed by Bengaluru firm and made in China
India Today | Ananth Krishnan
Online, 20 Sep 2017


Mitra, the robot designed by Bengaluru-based start-up, manufactured in China
Financial Express
Online, 20 Sep 2017


Articles stating that Indian robot made in China, which could recognise people by their nationalities and guide customers in a bank, captivated Chinese manufacturers at the first India-China Dalian Internet of Things (IoT) Conference the southern port city of Dalian. The five-feet robot Mitra, which was designed in Bengaluru but manufactured in the Chinese city of Shenzhen was presented as a model of merger of India’s software prowess with China’s hardware, according to Mr. Gagan Sabharwal, Senior Director, Global Trade Development, NASSCOM. He stated that Mitra is a perfect example of how Indian software and China’s hardware can be merged, adding that the agreement helps to bring the small and medium-sized enterprises and startups of both the countries to launch into IOT and AI.