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The Silicon Review Asia

Transform Customer Engagement & Create Strategic Value with Amazing Deep Learning Platform

The Silicon Review
August, 2017

thesiliconreview-vinay-kumar-ceo-arya-ai-2017“Majority of our investment is in Research and Development to find better and optimal ways of reaching our goal.” is a leading enterprise deep learning platform, enabling businesses to build and adapt complex deep learning systems by automating complex data science tasks in building neural network based applications and predictive models. And, this amazing transformation is done through a wonder tool ‘VEGA’, designed for enterprises to build and deploy Deep Learning algorithms at scale. An end to end platform, ‘VEGA’ simplifies complex ‘AI’ processes enabling enterprises to focus on core product features. 

Headquartered in Mumbai, the company was founded in the year 2013 and today it is widely adapted as the machine learning platform powering core products in banking, insurance and manufacturing. 

Today, is works with leading financial institutions in US, UK, Europe and India in experimenting and designing complex large scale use cases using Deep Learning and as a part of long term strategy of ‘Artificial Intelligence’ into the key products and services. 

Making a difference through wonder tool ‘Vega’

Unlike point focused tools, is designed to handle multi-functional tasks with any type of data input.’ Vega has advanced features like:

  • GUI framework for deep learning to augment the complexity of building complex neural networks by just drag and drop the layers. With debugging tools to diagnose the training and learning inside the neural networks.
  • Simulation tool that can automate the hyper-parameter tuning and experimenting the data on multiple neural networks.
  • Instant Inference system setup that can be scaled to any number of users or data sets without the need to worry about the infrastructure complexity. 

Additionally, the platform can handle the complexities involved in data preparation modules to prepare the data and data connectors to stream the training data. The platform can accept training data sets of both structured datasets (like transactional datasets, Sensor information etc) and unstructured data sets (like Images, Videos, Text etc). The platform is optimized to run on multiple-cloud platforms like (Google Cloud, AWS etc) and can also scaleon premise data centers for high consumption data sensitive use cases. 

“A journey of a thousand miles begins with a single step” 

Little behind inception

Established in the year 2013, is the brainchild of Vinay Kumar & Deekshith. Started with an initial step to build super intelligence for academic researchers that can process all the research information rendered, give high level inference that can reduce the need to read thousands of research papers to solve an academic problem. The company raised the first seed round in 2014 from Venture Nursery increasing the team size to 7 researchers. After experimenting with Deep Learning and solving various problems from Text processing to Image processing, the team went through the complexities involved in building deep learning based products in 2014. Lack of data sets, human resources and complexity of the process are the key challenges of deep learning adoption. The team them started focusing on solving larger problem of simplifying it such that the businesses can adapt and extrapolate the value of data and has been involved in investing in Deep Learning to facilitate various businesses to build advanced deep learning systems. 

Since then, the company never looked back, their growth and success is evident with the growth in their team. Today, their team is all about 16 people with more than 90% being engineers and 60% researchers in the team with rich background in Computing, Mathematics and Statistics. 

Market Monarchs of Deep Learning Domain

From building a neural network to production system design, solves end to end Deep Learning product journey and thus reduce the complexity of managing too many tools. And, by using the replicable stack to deploy multiple applications, it reduces the time to production from with subsequent developments. Additionally, the eco-system of micro-services on the platform enables business to have enough options while choose the data sets or the SIs to engage with. And, the vertical apps in industries like financial services and manufacturing, enterprises can plug and use these solutions quickly into their business tools.

Challenging, but doable!!

Highly competitive and result oriented domain, Deep Learning is amazing but it is complex and needs to sync efforts from multiple researcher level experts from building to maintaining the system. Hence, it consumes huge time to take it to production and needs huge resources to build a product. And for autonomous system, the complexity is exponential with number of task level AI systems.  The best way to adapt to complex advanced technology is simplify the tools and enhance the efficiencies of the users.

In every technology wave, the strategic value rests in ability to identify and deploy applications ahead of the competition. For such high competitive market, offers the key strategic values like quick experimentation and reduced production lifecycles thereby enabling the enterprises to test and deploy Deep Learning application on large scale production systems ahead to their competition. 

Meet the Leading Duo 

Vinay Kumar, CEO- A thought leader and a true entrepreneur at heart, Vinay holds Bachelors and Masters from IIT Bombay. A researcher in Nano Technology, Vinay has received ‘Excellence’ award in Research. Also, he was one of the key speaker at TED, GTC Nvidia etc. 

Deekshith, CTO- A visionary, Deekshith founded along with his buddy Vinay. He has been recently featured in Forbes 30 under 30 lists.  Also, very recently he was a speaker at De-HPC. 

Customer Centric ensures full client satisfaction from the services offered. The company chiefly focuses on financial sector followed by manufacturing. “B2B is our touch point but the influence can be experienced by both end consumers and internal teams. Like Risk Analysis teams, Innovation team, Data Science Team in Banks, Insurance companies and Investment banks are early adopters to intelligence/data driven applications and are our key focus. We work with most of the Fortune 500 companies for big transformation using Deep Learning & AI.” Elucidates Vinay Kumar.

The company today, takes pride in gaining the trust of industry biggies, such as: Large Banks in India & Europe, Large insurance company in India, Large re-insurance company in Europe, Leading consulting companies in UK etc. 

Future Roadmap

With a strategic focus to build industry focused platform that can address end to end processes of businesses in Insurance, Banking and manufacturing, team strives to scale and expand in Financial Services in next 15 months and also in Manufacturing & retail sector in a year.  

Case Study!! 

The use cases vary from Predictive Analytics to Intelligent Automation. Of all these engagements, engages on long term vision of deploying strong ‘AI’ into financial services vertical. 

Fraud Detection in Credit Transactions:

With growing complexity of transactions and advancements in complexity of faulty transactions, banks need to adapt to new and advanced systems that can track and monitor risky transactions. Often handcrafted statistical or traditional ML system fail to look for such complex fraud transactions. 

Unlike classical statistical based fraud classification system, the deep learning module built on Vega provides better classification of fraud transactions in real time. Currently the performance is much ahead of the commonly adopted statistical models. These deep learning models can look for complex and long range patterns.

Claims Processing in health insurance:

Processing health claims needs huge operational investments and takes considerable time to release or accept the claim. is currently working with leading insurance players to deploy a Man + machine ecosystem around ‘Vega’ to increase the efficiency of the performance, reduce the errors and track frauds.

“Our vision is to simplify the process of building ‘Artificial Intelligence’ in a way that anyone can use it to build Next Generation products for solving the most complex problems!”


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