Generating valuable insights from varied data sources is a necessity for enterprise decision making. The accuracy, speed, cost and ease of availability of right data metrics influence strategy, products, and services of enterprises. Challenges in enterprise decision making is driven by the exponential growth in unstructured data, which impacts data retrieval, extraction, aggregation, and analysis. Monitoring and mining structured and unstructured data spread across various sources presents significant issues related to data ingestion, extraction, cleaning, analytics, reasoning, visualization and interaction. The Mphasis AI for Text Processing enables enterprises to engage with their customers through personalized experiences and explore newer business models. The service allows users to perform the following tasks:

1. Smart Data Ingestion - Machine Learning based intelligent ingestion of Unstructured text data from a variety of data sources
2. Cognitive Analytics - Deep analysis of Unstructured data using machine learning, deep learning, NLP and graph theory
3. Actionable Insights - Generates actionable insights from the data and trigger downstream workflows for manual actions or robotic process automation
4. Integration with Enterprise Systems - Integrates with enterprise systems through application programming interfaces (APIs)

The AI for Text Processing solution has several customizable Deep Learning based models, trained using State of the Art Transformer models. Neural-network-based models have the ability to automatically learn the distributed representation for sentences and documents. It leverages a large repository of proprietary processing modules built on Mphasis patented cognitive computing platform to solve enterprise use cases which need text processing tasks such as text summarization, sentiment analysis, classification, pre-processing, entity extraction, topic extraction, knowledge graph, semantic triplet generation, sentence generation, email intent identification etc.
Assessment & Workshops Our Assessment and Workshops help clients get started with their Text Processing This engagement enables clients to plan and identify the relevant business problems which could be candidates for solving using AI for Text Processing. Assessments are a fast way of identifying if AI for Text Processing is the relevant technology to leverage for solving a business problem. In the workshops, the Mphasis AI/ML experts, SMEs, architects, and data scientists work closely with the client stakeholders to understand the system and data. The engagement includes activities such as stakeholder interviews, assessment and user journey workshop, identification of frameworks and tooling, gap analysis, understanding and assumptions, recommendation of solution, and report-out. The typical duration of the assessment and workshop exercise is spread across 3-4 weeks and the outcome of this exercise is a prioritized set of use cases along with recommended infrastructure to solve the problems, recommendations on frameworks and tooling, and reference architecture.

Algorithm Development and Implementation Algorithms Development and Implementation is a 2–3 month long engagement, where we bring our AI for Text Processing IP as well as customizations to solve the specific client problems. Our services are powered by our patented AI/Ml platforms and frameworks such as DeepInsights, PACE-ML, HyperGraf and InfraGraf. The Mphasis IP overcomes the limitations of systems not being able to work on varied input datasets and consists of 200+ pre-built and customizable Machine Learning, Deep Learning and Natural Language Processing algorithms. Our IP helps clients arrive at actionable recommendations faster and provide measurable results across scenarios. Our experience in AI/ML algorithm development and implementation are spread across several industries as well as use cases such as improved customer experience, recommendation of right products at the right time for the right customer, profit margin maximization, faster checkout processes, improved productivity, reduced disputes, reduce false positives, anomaly detection, predictive analytics recommendations, text categorization on large document corpus, Natural Language Generators etc.