Our products use the following AI and ML techniques for extracting knowledge from structured and unstructured data, and they use this knowledge to enhance decision-making, make predictions, automate laborious tasks, and much more.
Association Rule LearningThis enables discovery of associations in one or multiple large data sets and algorithms.
Deep Learning NetworksArtificial Neural Networks that are fairly deep to capture the interplay among features.
Inductive logic programmingUsing the expressive and declarative first-order logic to tackle problems involving structured data and background knowledge.
Knowledge GraphsProbabilistic hypergraphs that capture various aspects of data and information using different features.
Reinforcement learningTraining of machine learning models to come up with solutions to n uncertain, complex environments.
Generic Rule-Based Machine LearningIdentifying and utilizing a set of rules that collectively represent the knowledge collected by our products and solutions.
Sparse Dictionary LearningUsing compressed sensing and other sparse sampling techniques for efficiently acquiring and reconstructing signals and features.
Genetic AlgorithmsStochastic algorithms that act on a population of possible solutions to a problem to find the “fittest” one.
Heuristic AlgorithmsDesigned to find faster and more efficient solutions in a limited time frame where the underlying problems are very hard (e.g., NP-hard or have exponential time complexity).
We train algorithms to identify objects and patterns in images and video. Once trained, these act on new data to achieve almost the same accuracy as humans but at speeds that are several thousand times faster. We apply real-time scene detection to medical image processing, industrial machinery, video surveillance, construction equipment, and other system that collect and process visual data.
Natural Language Processing
We use several proprietary and open source algorithms for semantic and syntantic processing and understanding of English and related languages. In turn, this is used for summarization and natural language generation.
We take a scientific approach to solving the challenges of large volumes of data and help businesses streamline complex data processing operations. As a result, they gain mission-critical insights that unlock data-driven decisions across their entire organization.
Association rule learningA rule-based machine learning method that enables discovery of associations in one or multiple large data sets.
StrategyOur data science team will help you assess your current business context and map out a plan that will ensure seamless implementation and maximum value for your Big Data initiative.
Data MiningWe bring together all your big data sources and use a combination of AL, ML and statistics to discover patterns and ultimately predict outcomes.
Data GovernanceWe make sure that your data is always available, clean, up-to-date, secure, consistent, and ready for integration with AI, ML, IoT, and other technologies.
Master Data ManagementWe ensure accurate and consolidated management of an entity data across your enterprise to incorporate in our products and solutions, thereby, unlocking your competitive edge while complying with applicable privacy and security laws.
Big Data IntegrationWe harmonize your disparate data silos into a single source of truth (and a 360-degree view) to ensure data accessibility, reduced latency, scalability, and continuity. This helps our products and solutions provide deeper and better insights.
Big data analyticsWe turn massive volumes of structured and unstructured data into customizable, interactive reports and dashboards that provide substantial insights to both technical and non-technical people.
We enable manufacturing and supply-chain industries (including retail and consumer packaged goods industries) to uncover actionable insights with the help of our robust AI-Legos platform that exploits big data, machine learning, cognitive computing, and related technologies.
IoT data platforms
Providing a scalable data management platform (cognitive bricks) that collects, stores, and analyzes data from gateways, sensors, and devices across supply chain networks, manufacturing plants and construction zones.
Embedded, sensor-level analytics
Making IoT devices and sensors smarter and more independent, thereby enabling them to make intelligent decisions by turning large amount of raw data into insights.
Applying AI to sensor and device data to make IoT more independent in analyzing the current context, predicting next steps, and taking automated actions.
Developing systems that rely on data mining, natural language processing, and computer vision to mimic some aspects of the human brain, e.g., understanding and learning.