Data & Analytics

Helping clients to collect, analyze, and interpret data in order to make informed decisions.
Services include data mining, predictive analytics, business intelligence, and machine learning. The goal is to help organizations gain insights into their data that will help them better understand their customers, develop better strategies, and make more informed decisions.

Model Development

The process of model development typically involves the following steps:


Defining the problem

Identifying the system or process to be modeled, the data required for the model, and the. Objective of the analysis.


Data collection and preprocessing

Collecting relevant data from various sources and processing it to ensure that it is clean, complete, and relevant.


Model Selection and Development

Choosing the appropriate modeling technique and developing the model using the collected and processed data.


Model Validation

Testing the accuracy and reliability of the model using different validation techniques.


Model Implementation

Integrating the model into the decision-making process of the organization and monitoring its performance over time.

What We Offer​​

Data Aggregation
Gathering raw data from multiple sources and datasets and summarizing it into a more comprehensive, manageable and informative format or dataset. It helps to simplify complex datasets and provide insights that can inform strategic decisions. Various methods of data aggregation, such as summing, averaging, counting, grouping, and so on, are used depending on the type of data being collected and the purpose of the analysis. Ensuring that the aggregation process is accurate, consistent, and transparent to avoid biased or misleading results.
Data Engineering
Creating the systems and processes that enable the collection, transformation, and storage of data from various sources, as well as ensuring the data is accurate, consistent, and secure. Data engineers work with a variety of tools and technologies to build and manage data pipelines, data warehouses, and other data systems. They may use programming languages such as Python or SQL, as well as tools such as Apache Spark, Hadoop, and NoSQL databases like MongoDB. Allows data driven organizations to process and analyze large volumes of data in real-time, enabling better decision-making and more effective business strategies.
BI & Reporting
BI tools and technologies enable organizations to gather data from multiple sources, transform it into actionable insights, and deliver reports and visualizations to stakeholders. These reports can provide insights into key performance indicators (KPIs), such as sales revenue, customer satisfaction, and market share, and help organizations identify areas for improvement and growth. Reporting is a key component of BI, as it involves the delivery of information and insights to stakeholders in a clear, concise, and actionable format. Reports can be generated using various BI tools, such as dashboards, scorecards, and ad hoc reports, and can be customized to meet the specific needs of different stakeholders, such as executives, managers, and analysts. Effective BI and reporting can provide numerous benefits to organizations, including improved operational efficiency, increased revenue, reduced costs, and enhanced customer experience.
Model Development
Process of creating a mathematical or computational representation of a real-world system, process, or phenomenon. Purpose is to simulate the behavior of the system under different conditions and predict its future outcomes. Various methods and techniques such as statistical modeling, machine learning, simulation, optimization, and data mining are used for model development, depending on the nature of the system being modeled and the objectives of the analysis. Model development is used in various fields, including finance, marketing, engineering, and healthcare, to inform decision-making, optimize processes, and improve performance. However, it is important to ensure that the model is based on sound assumptions, validated using relevant data, and regularly updated to reflect changes in the system being modeled.


Retail / FMCG / CPG
  • Pricing :

    Price Optimization, Markdown Optimization

  • Marketing :

    Mixed Market Modelling, Customer Segmentation, Promotion Recommendation

  • Supply Chain Management, P&A (Planning & Allocation) :

    Inventory Optimization, Demand Prediction, Sales Forecast

  • Digital Analytics :

    Product Recommendation, Digital Traffic Forecast, Funnel Analysis


Use of advanced analytics tools and machine learning algorithms to help identify the optimal price points.


Strategically reducing prices on products to increase sales and profits. Retailers can optimize their markdowns and improve their profitability while also satisfying customer demands for lower prices.


Combining both qualitative and quantitative data to identify the relationship between variables, estimate the impact of marketing activities, and predict consumer behavior.


Dividing customers into segments and enabling businesses to tailor their marketing efforts to the specific needs and preferences of each group, resulting in more effective marketing and improved customer retention. By understanding the unique needs and preferences of each customer segment, businesses can deliver personalized experiences and build stronger relationships with their customers.


Strategic approach considering the goals and objectives of the business, as well as the needs and preferences of the target audience.


Process of managing inventory levels to maximize profitability and minimize waste. It involves balancing the costs of holding inventory against the benefits of having enough inventory to meet customer demand.


Process of accurately forecasting the future demand for a product or service. Enabling businesses to optimize their operation, inventory levels, production planning, marketing strategies, reduce costs, and improve customer satisfaction.


Process of predicting the number of visitors or customers who will visit online or offline store over a given period. Essential for businesses to plan their marketing strategies, allocate resources, and optimize their presence.


To understand the customer’s journey from initial contact to conversion and identify areas for improvement to increase conversion rates. Businesses can identify areas of the customer journey that are causing drop-offs and make data-driven decisions to improve conversion rates. It can help businesses optimize their website design, content, and marketing messages to increase customer engagement and drive sales.

Fintech/ BFSI


To automate underwriting process and support credit underwriters in decision making &  to quantify risks associated with retail loans.


Segmentation of the customer base and evaluate risk associated with each segment.


Providing models based on facial landmarks to minimise documentation errors.


Predicts EMI default- post loan disbursal and to find out distress customers early on.



To analyze the feedback survey data for preventive maintenance, break-down and customer equipment renewal process, improve patient experience, etc.

Case Studies

How we created impact on the life of shrimp farmers by incorporating cutting edge technologies.

How we created impact on the life of shrimp farmers…

How we created impact on the life of shrimp farmers…

How we enabled a leading loan provider to do customer segmentation and minimize documentation errors.​

How we enabled a leading loan provider to do customer…

How we enabled a leading loan provider to do customer…

How we enabled a leading retail super store chain to enhance sales and revenue using AI based solutions.

How we enabled a leading retail super store chain to…

How we enabled a leading retail super store chain to…