- Version 3.4
- Sold by Mphasis
A quantum simulator based Recommender System for cross-sell recommendations of products.
Mphasis applies next generation technology to help enterprises transform businesses globally. Customer centricity is foundational to Mphasis and is reflected in the Mphasis FrontBack™ Transformation approach. 'Front2Back' uses the exponential power of cloud and cognitive to provide hyper-personalized digital experience to clients and their customers. Mphasis Service Transformation approach helps 'shrink the core' through application of digital technologies across legacy environments within an enterprise, enabling businesses to stay ahead in a changing world.
A quantum simulator based Recommender System for cross-sell recommendations of products.
Solution enhances the search for specific business processes by accounting for language and intents of different user roles in the domain.
This solution identifies data belonging to the same entity/duplicate records across two data sources and creates a linked master dataset.
It is a Generative AI (LLM) based offering which can generate SQL query given a table schema and meta data and validate it through a dry run
Machine learning based service desk ticket triaging model to improve accuracy of ticket assignments and thereby improve FCR and MTTR.
Restaurant Reviews Topic Extraction is a deep learning algorithm which can extract up to 14 types of aspects from restaurant reviews.
The solution uses a Double - Hard DeBias Algorithm to remove targeted biases from the vector space representation of a text corpus.
Explainable AI solution that identifies algorithmic bias and thereby incorporate AI fairness
The solution predicts which customers are more likely to discontinue their existing insurance policies with the provider.
This solution helps in processing textual data to identify named entities present in the corpus of text.
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