Artificial Intelligence and Machine Learning.

Deep Learning

A classic AI can perform specific tasks as instructed but it is not capable of learning on its own and program. Machine learning is the processing of data using algorithms, analyzing the results, and predicting the output data using multiple iterations.

One example of deep learning is the artificial neural network (ANN) which is based on an idea of how our human brain works. ANN finds common patterns from given data and predicts the best result.

Computer Vision

Humans use their eyes and their brains to see and visually sense the world around them. Computer vision is the science that aims to give a similar, if not better, capability to a machine or computer.Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. Computer Vision involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.The applications of computer vision are numerous and include: autonomous vehicles, character recognition, security and surveillance, biometrics and forensics.

Text Analytics and NLP

Natural Language Processing is the scientific discipline concerned with making natural language accessible to machines. NLP addresses tasks such as identifying sentence boundaries in documents, extracting relationships from documents etc. NLP is a necessary means to facilitate text analytics by establishing structure in unstructured text to enable further analysis. Text analytics is a broad term that "includes meta-information annotations (e.g., people and places mentioned in the text), sentiment analysis, text clustering, and categorization.

Internet Of Things

Predictive analytics

Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast activity, behavior, and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets. The application of the above process creates predictive models that place a numerical value on the likelihood of a particular event happening.

Predictive analytics has grown in prominence alongside the emergence of big data systems. As enterprises have amassed larger and broader pools of data in Hadoop clusters and other big data platforms, they have increased data mining opportunities to gain predictive insights. Heightened development and commercialization of machine learning tools by IT vendors have also helped expand predictive analytics capabilities. Predictive analytics requires a high level of expertise with statistical methods and the ability to build predictive data models.

Smart Retail

Retailers have started adopting IoT solutions and using IoT embedded systems across several applications . Through IoT physical retailers can compete against online challengers more strongly. Retailers can regain their lost market share and attract consumers into the store, thus making it easier for consumers to buy more while saving money.

Smart Supply Chain

Supply chains have already been getting smarter for a couple of years The popular offerings solve problems like tracking goods in transit or helping suppliers exchange inventory information. With an IoT enabled system, factory equipment with embedded sensors communicate data about different parameters, such as pressure, temperature, and utilization of the machine. The IoT system can also process workflow and change equipment settings to optimize performance.

What we do

  • Our endeavor is to incorporate Machine Learning techniques into our customer products and solutions.
  • From this point of view, whenever we work on a customer project, we keenly analyze and check if anything can improve in terms of performance, accuracy, and usability if we apply AI techniques.

Data Engineering

Performing ETL and ELT jobs

The ETL and ELT are necessary in data science because information sources—whether they use a structured SQL database or an unstructured NoSQL database—will rarely use the same or compatible formats. We have an expertise in providing modern ETL solution using cloud-based data warehouses and cloud-based SaaS platforms.

Developing complete end-to-end Data Pipelines

Faster access to accurate and prepared datasets is critical for enterprise analytics to deliver better business outcomes. We provide a scalable data and machine learning solution with faster data discovery and preparation that accelerates development and increases model accuracy.