We provide cutting-edge data science solutions to our clients in a range of industries to help them eliminate revenue leakages and increase bottom-line efficiency. You can explore new market opportunities while discovering creative ways to strategize and enhance operations with the help of our team of professionals

BFSI

Due to digitization and the speed at which it has been adopted in recent years, the Banking, Financial Services, and Insurance (BFSI) industry has been expanding its capabilities. Additionally, it is becoming more competitive than ever before, giving modern consumers greater influence.

Data science is assisting the banking sector in managing the many difficulties it currently faces more intelligently. Predictive and prescriptive analytics are now beginning to produce powerful insights, resulting in a considerable value addition while basic reporting and descriptive analytics remain essential for banks.

BFSI

Pharma & Health

One of the most cutting-edge and fiercely competitive sectors of the global economy is the health and pharmaceutical sector. With a wide range of products, it must compete on many levels, from marketing and sales to research and development.

Through applications like predictive modelling, segmentation analysis, machine learning algorithms, visualisation tools, etc., data science is widely employed in the pharmaceutical business to enhance its operations. By evaluating data from clinical trials and other clinical research to find areas for improvement or new treatments, data science in the pharmaceutical business can also aid improve patient outcomes.

Pharma & Health

Manufacturing

With the technological advances and the quickly changing corporate environment. The automation of production processes is being funded by manufacturers. In addition. Manufacturing companies are focusing on boosting production and cutting costs by implementing cutting-edge technology like big data and artificial intelligence, among others.

Because of the enormous database needed to gather, store, and analyse the vast amounts of data generated by devices (such as sensors, RFID etc). Big data technology is assisting the manufacturing sector to enhance the manufacturing process and assist in data extraction from numerous devices to improve output, quality, automated operational workflow, and lower maintenance costs.

Manufacturing

Automobile

The use of technologies, software, and services ranging from sensors to artificial intelligence to big data analysis is redefining the vehicle business and altering how people go from point A to point B. In addition to producing more fresh data, the rise of autonomous vehicles is also radically altering consumer preferences.

Autonomous vehicles, advanced production techniques, and elevated customer expectations are some aspects of the changing auto industry. The interaction between the key planners and data science becomes more significant and well-known as the car industry develops. Automobile manufacturers are transitioning from lean producers to creators of sophisticated goods and services.

automobile

Retail

Customers anticipate a seamless, easy, and quick purchasing experience. Adopting a retail analytics approach will give businesses the skills and technology necessary to build and automate seamless consumer experiences between online channels and brick-and-mortar stores from a customer-centric perspective.

Businesses who proactively use retail analytics are already witnessing a considerable increase in their financial performance as a result of their ability to take immediate action on important data-driven insights. Companies need to use retail analytics to provide the experiences that customers demand and to stay competitive.

Retail

What We Do

Several sectors are using data science, and the outcomes have been transformative. Revenue margins are rising as a result of significantly higher operational efficiencies. The proliferation of information offers innovative chances for industries to develop through data science. Data science is allowing industries to gain insights from huge data that support strategic decision-making and result optimization

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Customer
Segmantation

Determine which client will be more profitable than others. Then, this serves as the criterion for differentiation

Through giving personalised services, group analysis, and the creation of strong, lucrative relationships with clients, the process produces better services

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Fraud
Detection

The convenience of banking online or through your mobile device has raised the risk of fraud, phishing, and other scams.

Because of the adoption of continuous analysis of transactional patterns, banking institutions may now quickly identify any suspicious dealing or irregular transaction.

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Risk
Modelling

Examine how borrowers plan to pay back their debts. There could also be defaulters.

A bank can identify defaulters, calculate the default rate, and alter lending policies with the aid of risk modelling.

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Lifetime
Value Prediction

The discounted worth of all future revenues that the client will be required to pay is provided by a customer lifetime value.

Knowing which of the current clients will stick around for a while and contribute to future income production makes it easier to anticipate future revenues.

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Drug Discovery
and Development

As the patents on blockbuster drugs approach expiration, Pharma industry is attempting to speed up the process of getting a drug to market.

By applying prediction algorithms to enormous quantities of scientific publications and research papers of data can help derive vital information.

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Increase
Drug Utilization

The operating margin of pharmacies is under increasing strain. It becomes imperative to boost the effectiveness of the entire production process.

Granular analysis of key metrics such as average ingredient cost per subscription & drug utilization review saving per member per year can help cut costs.

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Efficacy
of Clinical Trials

By collecting and evaluating different data points, including participant demographics and historical data, pharmaceutical companies try to lower the cost and expedite clinical studies.

By optimizing entire process, companies can speed up disease diagnosis and design more efficient clinical trials.

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Personalized
Targeted Medications

As each person has a distinct genomic make-up, individualised treatment is suitable.

Pharma businesses may expedite disease identification and create more effective control groups and clinical trials by streamlining this entire process and utilising big data analytics.

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Demand forecasting
& Inventory management

Improve inventory management and decrease the need to keep a lot of unnecessary products in storage.

And obtain a more complex understanding of the operation of your manufacturing business for future planning.

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Cost
Control

By aggregating, analysing, and generating optimum price variants, modern price optimization tools can effectively boost your profit.

Price optimization becomes essential and develops into a continual process in highly competitive markets with changing client needs.

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Robotization

Robots are frequently used for repetitive activities, as well as those that could be harmful or difficult for humans to complete.

The models of AI-powered robots assist in meeting the rising need. Additionally, industrial robots significantly improve the quality of a product.

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Warranty Analysis

Warranty claims reveal useful information that can be used to enhance currently available products.

Warranty analytics tools assist manufacturers in processing massive amounts of warranty-related data from numerous sources to identify the causes.

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Emission
Monitoring

Given that the Air Quality Index in a number of locations around the world has already beyond dangerous levels.

It would be necessary for automakers to use AI-powered data engineering to aid with automated pollution tracking.

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Predictive
Vehicle Maintenance

Maintenance on cars was more predictive than preventive. We would estimate the best time for an overhaul based on our best judgement.

Real-time actionable insights regarding the car and its upkeep can be given to the user by the AI-powered software.

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Automatic
Guided Vehicles

Autonomous and connected vehicles are the trendiest topics in the automotive business which rely on techniques for sensor fusion and deep learning models.

Data science is essential to the development of these vehicles which is used to convert IoT signs, such as battery charge monitors into useful insights.

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Driver
Monitoring system

The AI enables the system to automatically recognise the driver's presence and make the necessary modifications to the seats, mirrors, and temperature.

It continuously scans the driver's behavioural traits, such as eye openness and head position to take action to avoid any fatal vehicle damage.

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Customer
Sentiment Analysis

In order to better understand clients, businesses utilise sentiment analysis to gather customers' subjective data.

Sentiment analysis models can extract sentiments from text using Natural Language Processing. NLP can determine whether a customer is providing positive or negative feedback on a product.

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Price
Optimization

The price strategy chosen is one of the most important factors when evaluating that affect a product's or service's success.

The use of Machine Learning is a very attractive approach for retailers. it can benefit from predictive models that allow them to determine the best price for each product or service.

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Utilizing
Social Media

Due to the widespread use of social media. These communication channels are being utilised by the retail industry for global marketing.

Social media provides a large amount of customer data to the retailers, which helps to find the patterns, customer behavior, and trends.

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Recommendation
System

Recommendation systems proved to be of great use for the retailers as the tools for customers’ behavior prediction.

It helps to gain the customer opinion of any particular product. Providing recommendations enables retailers to increase sales and to dictate trends.

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