Our lean and agile data scientists, engineers, and application developers takes part in innovation and development of custom machine learning and AI solutions.
As a data science consulting firm, we offer range of services from organizational assessment, strategy formation, services like data preparation and modelling, model optimizing and fine tune the performance along with migration of algorithms across advanced analytics platforms.
Our in-house competencies in Data Science are continuously evolving by attracting best talents from the industry to grow and differentiate as trusted data science service provider for our customers.
We bring extensive cross-industry expertise backed by deep knowledge of state-of-the-art techniques to design, build, and deploy bespoke AI solutions.
Define your success stories with data. We can help you to understand the present state of data and analytics landscape in your business and how you stack up against industry best practice. We also help you to build the business case and detailed plan of action and capabilities required to put your data to work.
It is undeniable that 80% of a data scientist’s time and effort is spent in collecting, cleaning and defining the data. Nobody is interested in moving faster if they are heading in the wrong direction for analysis, but this is what truly happens in many organizations. Data cleaning plays a crucial role in ensuring that the rocket points in the right direction.
Data modelling is an essential part of the data science pipeline. A big part of data science modelling involves evaluating a model, for example, making sure that it is robust and therefore reliable. Also, data science modelling is closely linked to creating an information rich feature set. Moreover, it entails a variety of other processes that ensure that the data at hand is harnessed as much as possible.
Make your proposition and experience more targeted, relevant and profitable. Reduce the frequency of your communications whilst enhancing response rates. Gain an edge over the competition. Optimise spend, identify next best product and those most likely to respond to a deal. Focus retention spend on customers most likely to churn.
Data lakes have quickly become mainstream, growing in popularity and prevalence, as businesses realize how they help solve scalability and duplication issues while significantly enhancing analytics insights to gain a competitive advantage. Data platforms such as on-premises Hadoop are commonly used for data lakes, but a shift is underway. Organizations are starting to transition to cloud-based data platforms such as Amazon Web Services (AWS), Microsoft Azure, Snowflake, Google Big Query, Intellicloud, etc. to meet modern data lake requirements. They offer easier, more affordable, and flexible data platforms compared to on-premises Hadoop.
Typically, our customers continue working with us on new feature development or improvement of the existing ones because our team already has the necessary technical knowledge. This could be a full-time team of developers, data scientists, testers, and project manager as well as on-demand services like DevOps. We offer such services both on-premise and from offshore (INDIA). We can also bring 100% funding for your projects, when you commit a long-term association with us (special T&C is applicable)
AI is machines making decisions. Whether learned from data (machine learning) or by rules imputed by humans, computers are able to take in new information, process it and make a decision that maximises the chance of success or minimises the chance of loss.
Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
Natural language processing is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data
Good sales forecasting helps you grow your business. But, for many years, forecasting has relied on the human element — emotions and hunches can make or break a quarter. Just as big data and artificial intelligence (AI) pervade many aspects of how we work, it's doing the same for forecasting.
Marketing Mix Modelling is a set of statistical analysis techniques that help marketers and businesses understand what kind of impact their marketing efforts are having on their sales or market share, and also gives them the ability to predict how future tactics will impact ROI going forward.
Essentially, Marketing Mix Modelling (which we’ll henceforth refer to as MMM) is a way of analysing historical data, such as point-of-sale information and internal data streams to quantify how sales are being impact by marketing efforts.
A key activity in predicting customer churn is feature/variable importance, that is, using AI methods to assign a weighting indicating how important each feature/variable is to predicting customer churn. Gathering data from each stage in the churn funnel is an important activity in ensuring a robust churn dataset.
Churn management is the art of identifying the valuable customers, who are likely to churn from a company and executing proactive steps to retain them. The telecommunication industry has got fierce competition among the various service providers. ... This customer tendency to switch is referred to as Churn