Amalgamates effortlessly with the user systems that orchestrate the business processes to transform the data downstream ready. 

Render and auto-navigate 

Validate the predictions 

Track Corrections 

Understands unstructured natural language and the overall structure of concepts in the document to identify and classify entities, intents, objects, and sentiments. 

Domain specific Language Models 

Text classification, extraction and text generation 

Semantic Similarity Models 

Works on mixed structured data representations to discover relationships among these data representation irrespective of their form and performs inordinately well for both classification or regression purposes. 

Structured data with mixed data types 

Numbers, date/time and text, combination 

Classification, Regression and Clustering 

With four steps of preparation, model training, model versioning and model deployment, Auto AI enable AI models to fine- tune the results continuously based on SME feedback. 

Monitors correction volumes 

Schedules retraining 

Orchestrates training on cloud 

Performs multivariate anomaly detection with inputs including a combination of natural language, numbers, date/time and other data type even with fine grained differences. 

No human intervention required 

Deals with mixed data types 

Applies self-supervised learning 

Handles extraction of information from semi-structured documents with highly varying layouts. 

Layout and Language understanding 

Semi-structured & unstructured content 

Contextual object detection