Artificial Intelligence


What We Offer

Predictive Analytics

Predictive Analytics

Predictive analytics involves applying a wide range of machine learning algorithms to gain predictive power. It is more or less synonymous with machine learning, the most popular and widespread variant of AI, comprised of a set of algorithms that learns to model data by observing large chunks of it. Predictive algorithms or machine learning is used by startups to big corporations today to make use of large amounts of data they gather and generate everyday. Predictive ability is obtained through sophisticated modeling of data and key to success is applying right algorithm to solve right problem. This decision depends on multiple factors such as data volume, type, velocity and also the specific problem you are trying to solve with machine learning. Here, we help you not only by guiding you to pick right tools but also by building solutions with powerful predictive capabilities.

Deep Learning

Deep Learning

Deep learning is a subset of machine learning that utilizes neural networks with many hidden layers, hence the deep. It has a history of over 50 years but recent high availability of data and computational resources made real world applications of deep learning possible and successful. Deep learning is the underlying technology behind most of the notable applications from Apple Siri (intelligent assistant) to Tesla Autopilot (autonomous driving). With large amounts of right data deep learning can deliver high predictive power and understanding of complex and unstructured data types such as natural language, images and video. It may require significant computational resources specially on large datasets, thus considering deep learning as a silver bullet can be an overkill unless you make the right choice.

Computer Vision

Computer Vision

Modern day computer vision is a powerful crossover of traditional computer vision and deep learning. It focuses on analysing image and video content that have become widespread with the emergence of social media and consumer devices that can produce rich multimedia content. Computer vision has become immensely helpful in analysing these rich data types. It can be used to perform many tasks ranging from simple object recognition to more sophisticated applications such as virtual fitting room. Yet most of the multimedia content that can be used to generate valuable insights is thrown away unanalysed today, one of the best examples being CCTV video footage. We add value to your business by identifying and analysing right multimedia content using our powerful computer vision stack.

Natural Language Processing


Human-like natural language understanding is sometimes considered as the holy grail of AI. Human languages are inherently complex for machines. Yet if we are to analyse and understand large amounts of data generated by humans (in natural language) everyday, we need AI technology to decipher that data into a machine-understandable form. The emergence of advanced NLP techniques and deep learning have made this possible, best examples being chatbots and intelligent assistants. NLP gives you the ability to interact better with your customers and understand them better, which eventually brings competitive advantage to domains ranging from marketing to healthcare. We offer a wide range of NLP capabilities to transform your data into insights no matter which domain you are from.

From Our Blog

AI can solve all your business problems. Or maybe not!

by Janak Gunatilleke July 12, 2018
A 2017 survey of more than 3,000 executives by MIT Sloan Management Review and Boston Consulting Group revealed that almost 85% believed that Artificial Intelligence (AI) will give their companies or help them maintain a competitive advantage. Non-public AI companies raised $15.2B funding in 2017, an increase of 141% compared to 2016.

The biggest challenge of AI is not people or technology

by CD Athuraliya May 2, 2018
Yes, that’s correct. Well, I think so and let me explain.
I think the biggest challenge of AI right now is making it an industry or an engineering field. All the other challenges were there for a while if you really think about it. There can be slow downs and booms in fundamental research, talent can be in short supply, still we will make progress. But I think we are yet to define AI as an industry. And we need to do it right away!

Generative Adversarial Networks — A Theoretical Walk-through

by Sameera Ramasinghe February 14, 2018
Since Ian Goodfellow first proposed the idea of GANs (, it has become a buzz word within ML community, simply because it works stunningly well (given that you came up with a perfect architecture). Many people, specially Yann LeCun, a who is considered as one of the giants in Deep Learning, stated at some point that GANs are a significant breakthrough in deep learning.

Latest News

ECCV 2018
Our paper A Context-aware Capsule Network for Multi-label Classification accepted at the ECCV 2018 workshop Brain-Driven Computer Vision

Tech in Asia Singapore 2018
We were one of the 6 finalists out of 185 global companies at Tech in Asia Singapore 2018

Self-Organizing Conference on Machine Learning 2017
We were at Self-Organizing Conference on Machine Learning (SOCML) 2017 held at Google Sunnyvale office

Who We Are

We are an artificial intelligence (AI) technology company that focuses on applying machine learning and deep learning to solve problems in multiple domains. We make AI accessible to businesses. We are on a quest to transform traditional and emerging industries across the world with AI.
Our team consists of highly experienced talent from world-leading institutions and enterprises. In our prior work we have applied AI technologies in enterprise software, telecommunication, remote sensing and social media. We have presented our work at top conferences and published in international journals.

Reach Out

Talk to us, we would love to hear from you

[email protected]
+94 77 621 2254
+44 74 9604 6311
+61 43 589 3391