Making the most of your data
Data Science is the practice of applying advanced machine learning techniques to address complex business problems. At DSI, we combine our client’s domain expertise and data with our AI expertise to develop production-ready AI models.
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Combine your domain knowledge and data with our machine learning expertise to build powerful AI solutions

Nimmy Thomas
Cloud Solution Architect – Data Science

Nimmy is the Cloud Solution Architect for Data Science responsible for leading AI solutions at DSI. With a bachelor’s in electrical and electronics engineering, Nimmy loves making data talk. In addition, she has a unique fondness for statistics which, when paired with technology, helps uncover hidden opportunities in business and create new versions of the future. With over 5 years of industry experience, she has worked on exciting AI solutions in the energy, retail and eCommerce space with a specialization in Natural Language Processing and Predictive Analysis.

We have offers no matter what stage of the technology acquisition journey you are on.


DSI will analyze your AI maturity and create a plan to achieve your AI goals. We can address your specific business problems or guide you on your AI journey built from scratch. Together, we will explore options for best approaches – be it simple automation or complex machine learning-driven solutions.


DSI will collaboratively define your business problem, audit your data, and develop a minimal viable AI product that objectively demonstrates value. We will provide detailed reports on the methodology adopted, evaluation metrics, improvement techniques, and any additional considerations. The timeframe for this will vary.


DSI experts will develop and implement the AI solution after building a POC with satisfactory results following industry-standard CI/CD best practices, best coding, and governance practices. Following deployment, we will provide comprehensive training for your business users to ensure smooth adoption & product understanding. The timelines for this will vary.

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Trends & Technology Updates

                                                      Industry use-cases

The healthcare industry is regularly cited for its creative use of AI – and good reason. This multi-billion-dollar industry is a public good, therefore any application that improves patient outcome and reduces resource utilization garners great interest and publicity. One of the best domains for AI use within healthcare is in Computer-Aided Diagnoses applications. Being able to detect the early onset of disease to intervene is of critical importance to overall patient health outcomes. Indeed, machine learning has continually demonstrated its value in accurately detecting patterns within the data that enable it to make accurate diagnoses, oftentimes more accurate than seasoned medical professionals. This industry is ripe with opportunities to improve early diagnosis and overall patient outcome and satisfaction. For example, AI has been used to accurately identify diabetic retinopathy and more recently, to detect asymptomatic COVID-19 infections through forced coughing into your phone.

Retail: The retail landscape has never been as competitive as it is today. With so many alternative product options available to customers today, brands must compete for consumer attention to remain top-of-mind awareness to remain on top of shopping lists. Using the latest AI technologies, master brands can continually assess consumer sentiment towards their latest marketing campaigns, package re-designs, and overall brand sentiment over time. Moreover, the retail is especially well-positioned to reap the benefits of many AI applications ranging from ad targeting, inventory optimization based on demand, and supply chain logistic improvements.

Insurance: The insurance industry is a natural data aggregator where accurate predictive capabilities are of top priority. Given how well AI resonates with the industry it is not surprising to witness the adoption of a wide variety of AI applications within the industry. Insurers today have trained AI models to assist in accelerating manual underwriting workflows, streamline claims processes, and have even been used to forecast large-scale loss events which are accelerating with climate change. Indeed, the need for accurate predictions has never been as valuable as it is today, and the correct application of AI can make all the difference.

Diversified Financials: In many ways, asset management seems perfectly suited for the widespread adoption of AI. Many transformative capabilities of AI rely on its ability to predict and better understand the most likely outcome of specific circumstances. Higher predictive quality is of tremendous interest to asset managers, which is why we are experiencing portfolio management teams increasingly augment their quantitative and qualitative analytical capabilities with AI. With the right domain knowledge, relevant dataset, and AI model the financial sector has never been better positioned to manage risk, assess uncertainty, and identify real-time patterns in financial markets to make smarter investment decisions.

Mining firms possess a natural drive to increase efficiency and reduce capital costs. Today, AI is frequently being used in conjunction with the latest IoT technologies to identify processes that reduce waste & enable smarter business decisions. As more easy-to-use cloud-based AI platforms are adopted, more and more enterprise-grade solutions will be developed and maintained in the economic sector, streamlining workflows, and reducing overall costs. For example, Freeport-McMoran, the world’s largest copper producer has implemented a comprehensive AI platform combined with agile methodologies to increase production output by 5% in 2019.

As with other economic sectors, real estate is experiencing seismic shifts in how buyers, sellers and agents operate. Regional demographics, a warming climate and general lifestyles are in a constant state of flux. These shifts result in the constant need for digital real estate products and services to tailor themselves to the ever-growing and ever-changing demand. AI-powered tools can assist the real estate sector in several ways, ranging from predicting a property’s value, identifying likely buyers, and providing nuanced digital services that are smart enough to know exactly the sort of property attributes that are important to you.

Data Science Process

Using a variety of Azure services, data from various sources is transferred into an AI-enabled environment to build optimized models that are deployed and utilized by business users

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