Our Technology Stack
AI technologies are evolving at a very fast pace and bringing disruptive changes in many industries. To remain competitive & relevant, each business needs to evolve to keep pace with these evolving technologies. Data science projects require multi-disciplinary competencies spanning across domain, data science & software technologies and hence are inherently complex to conceive & execute.
Our data science services help enterprises accelerate their AI journey. We provide consulting, design, development, deployment & operation services to clients worldwide.
Statistical Learning is a branch of statistics that involves using statistical models and a vast set of tools for understanding data. Statistical learning forms foundation of machine learning and is relevant for large number of ML algorithms and use cases.
Explainable AI deals with interpretation of ML algorithm results & Causal ML algorithms helps with counterfactual queries related to data. They help in building AI solutions that are fair, interpretable and go beyond correlations and find related causes.
Machine learning (ML) is a branch of AI that uses mathematical & statistical techniques to enable machines to "learn" from data automatically without need to specific program, a process known as “training” a “model. ML algorithms are defined across unsupervised learning, supervised learning, reinforcement learning, anomaly detection, dimensionality reduction categories and focus on accuracy of prediction/outcomes. Some example use cases of ML include and are both are written are Fraud Detection in Financial industry, product recommendations in Retail Industry, predictive maintenance in Manufacturing Industry, path/schedule optimizations in logistics industry.
Deep Learning (DL) is an area within ML that mimic layers of neurons in human brain to learn complex patterns in data. The “deep” refers to the large number of layers of neurons in models that help achieve better performance gains. Large scale neural networks of various kinds like artificial neural networks (ANN), convolution neural networks (CNN) or recurrent neural networks (RNN) are typically referred to as deep neural networks.
Deep Reinforcement Learning technologies a combination of deep learning and reinforcement learning and involves machine/agent learners to make decisions based on either live or historical data in different environments using algorithms inspired by brain neural networks. This helps to build machines/robots that can learn & perform human tasks (using robotic-arm motion control). Such machines/robots improve process efficiencies like automating, manufacturing line or can be deployed in environments risky for humans e.g. Fire/flood & help save human lives. Some commonly used Deep RL algorithms include deep Q networks (DQN), deep deterministic policy gradient (DDPG) etc.
Data Engineering is the practice of data collection, processing, cleaning, joining, merging, transforming and making data ready for downlink reporting, AI/ML algorithms and big data storage applications. Data’s journey from ingestion processing to final storage is called a “data pipelines.” As the information travels through the pipeline, it may be transformed, enriched and summarized several times.
Design & Implementation of data pipeline for batch processing involving ETL , distributed data processing, No SQL/RDMS databases for creating Data Warehouse & Data Lake, Integration with ML algorithms & Visualization tools.
Design & Implementation realtime pipeline for applications requiring real time processing with distributed event processing, ML /Visualization integration and storage.
Natural Language processing is a branch of AI/ML that involves machines to process written and spoken natural language data & produce varied interpretations. This helps with various use cases like virtual voice assistant (chatbox) or virtual voice assistant (Alexa/Siri) using automated voice recognition and response systems (Conversational AI) among many others.
We have competencies in different MLP use cases like chat boxes for question answering; intelligent document processing for text summarization & topic modelling; social media stream processing for named entity handling & sentiment analysis etc.
Our competencies speech processing span across signal processing & data science domains for use cases like speech to text conversion, conversational AI etc.
Computer vision is a branch of AI/ML that involves machines to process visual data such as images/ videos extracting complex information and produce varied interpretations. This helps with wide ranging use cases that impact many industries e.g. autonomous driving cars, facial recognition systems for national security, unmanned quality control for industrial automation, video analytics solution like intruder detection etc.
Our competencies in image analytics span across signal processing & data science domains for use cases like object detection, optical character recognition (OCR), medical imaging etc.
Our competencies for computer vision span across areas like object detection, video analytics etc.
Generative AI is a branch of deep learning that can generate various types of content,including text, imagery, audio or code. Foundation Model are deep Learning model trained on a large data volume using either self / semi-supervised learning and can be for text, vision, audio or multimodal data. Foundation models built for language are called Large Language Models (LLM) and for vision are called Visual Foundation Model (VFM).
Generative AI has use cases in different industry vertical and across multiple functions. Some examples of use cases of generative AI in enterprise include
With our Enterprise Generative AI services, we help enterprises build and deploy custom, reliable and secure solution using their data that achieves significant tangible outcomes. We help define strategy, select right components and assist in journey from developing first MVPs and build scalable enterprise-ready solution that drive business transformation. Our platforms and solutions significantly reduce cost and time for such projects and help achieve faster return on investments for enterprises.