At the heart of Front2BackTM transformation is harnessing the power of cognitive technology, which serves as your Systems of Intelligence. Businesses can leverage machine learning services to uncover insights from unstructured data and drive efficiency.However, without an overall data strategy that includes applied machine learning and artificial intelligence to bring hidden value from the data, you will not have an edge over your competition. Most of the innovation comes from linking datasets (which were hard to manage earlier) with cognitive computing— that connects human-like thinking and computer’s unparalleled efficiency.
Mphasis innovation labs are leading the transformation with the following offerings:
Cognitive Analytics
Using deep learning with neural networks, graph theory, and big data analytics to convert data to ‘smart data’
Contextual and Temporal Decision Making
Relationships are inferred from data and instantly made visible to generate insights and trigger workflows for robotic process automation (RPA)
Machine Learning
Leveraging pattern recognition, natural language processing (NLP), and machine learning for intelligent extraction of data from thousands of structured/semi-structured/unstructured file types including images, documents, PDF, and HTML
DeepInsightsTM is a cognitive computing platform for deep analysis of structured and unstructured data sources. It provides the most contextual and accurate decision metrics and leverages machine learning-based intelligent extraction of data. The platform implements cognitive decisioning using unstructured data by inference-based rules and triggers automated decisions
Machine learning based intelligent extraction of data from thousands of file types including images, documents, PDF, emails, and HTML
Deep analysis of structured and unstructured data through deep learning, neural networks, graph theory, image analytics, machine learning, and natural language processing
Contextual and temporal decisioning to generate the right insights from documents and trigger downstream workflows for manual actions or robotic process automation
Deep analysis of structured and unstructured data through deep learning, neural networks, graph theory, image analytics, machine learning, and natural language processing
Contextual and temporal decisioning to generate the right insights from documents and trigger downstream workflows for manual actions or robotic process automation
Interact with enterprise systems and channels such as virtual and human agents, databases, as well as APIs across industries such as wealth advisory, trade finance, insurance, and banking
Contextual and temporal decisioning to generate the right insights from documents and trigger downstream workflows for manual actions or robotic process automation
Interact with enterprise systems and channels such as virtual and human agents, databases, as well as APIs across industries such as wealth advisory, trade finance, insurance, and banking
Machine learning based intelligent extraction of data from thousands of file types including images, documents, PDF, emails, and HTML
Interact with enterprise systems and channels such as virtual and human agents, databases, as well as APIs across industries such as wealth advisory, trade finance, insurance, and banking
Machine learning based intelligent extraction of data from thousands of file types including images, documents, PDF, emails, and HTML
Deep analysis of structured and unstructured data through deep learning, neural networks, graph theory, image analytics, machine learning, and natural language processing
InfraGraf® is an intelligent platform to optimize and automate technology infrastructure that identifies and predicts key incidences leading to major failures in infrastructure. It identifies complex interdependencies in systems and automates repeatable tasks with respect to monitoring and resolution.
Provides early warning system for device and component failure and identifies inter dependencies, cascading, and ripple effect
Predicts inventory requirement at each and every location to reduce costly downtime and optimized inventory
Optimizes on-premise and virtual server capacity, predicts the demand, and suggests the need to go on cloud
Predicts inventory requirement at each and every location to reduce costly downtime and optimized inventory
Optimizes on-premise and virtual server capacity, predicts the demand, and suggests the need to go on cloud
Generates root cause analysis of incidences, failure, and performance. It also provides drill down geo spatial analysis
Optimizes on-premise and virtual server capacity, predicts the demand, and suggests the need to go on cloud
Generates root cause analysis of incidences, failure, and performance. It also provides drill down geo spatial analysis
Helps in optimized maintenance schedule, service downtime, and respective resource planning
Generates root cause analysis of incidences, failure, and performance. It also provides drill down geo spatial analysis
Helps in optimized maintenance schedule, service downtime, and respective resource planning
Redesigns the vendor SLA and insurance contracts on the basis of business priorities and device health index for optimized cost
Helps in optimized maintenance schedule, service downtime, and respective resource planning
Redesigns the vendor SLA and insurance contracts on the basis of business priorities and device health index for optimized cost
Offers intelligent ticket routing, in-depth incidence analysis, and SLA breach reporting
Redesigns the vendor SLA and insurance contracts on the basis of business priorities and device health index for optimized cost
Offers intelligent ticket routing, in-depth incidence analysis, and SLA breach reporting
Identifies the defects and automates repeatable tasks with respect to monitoring and resolution
Offers intelligent ticket routing, in-depth incidence analysis, and SLA breach reporting
Identifies the defects and automates repeatable tasks with respect to monitoring and resolution
Redesigns the network to optimize performance by monitoring and predicting redundancies and errors
Identifies the defects and automates repeatable tasks with respect to monitoring and resolution
Redesigns the network to optimize performance by monitoring and predicting redundancies and errors
Provides early warning system for device and component failure and identifies inter dependencies, cascading, and ripple effect
Redesigns the network to optimize performance by monitoring and predicting redundancies and errors
Provides early warning system for device and component failure and identifies inter dependencies, cascading, and ripple effect
Predicts inventory requirement at each and every location to reduce costly downtime and optimized inventory
HyperGrafTM is an omnichannel, digital 360 solution that offers comprehensive real-time customer insights. It generates enterprise metrics using enterprise data and channels and leverages third-party and social data to provide insights at the profile level.
