The power of Data via

Artificial Intelligence and Machine Learning

Vision Analytics

Text Analytics

Conversational AI

Text Analytics

Involves semantic analysis of text data like documents, email, web chat, social media, surveys, patents, etc. along with the process of building a searchable knowledge graph and insights.

Automated Email Routing

Secured, automated email classification and routing solution using Cognitive Automation techniques

Natural Language Processing (NLP) is a broad set of tools and techniques that can be used to classify text data, to extract specific entities, insight, and knowledge from it and tabulated it in a manner that allows a better search. We have successfully completed email classification and automation solution for a Fortune 500 logistics firm using NLP and Robotic Process Automation (RPA). This solution has helped the enterprise to automate the process, reduce manual errors and respond quickly to the inbound Emails. The solution is integrated with UiPath to automate the final stages of the workflow.

Solutions for Law Firms

Cognitive AI to translate legal context understandable for all stakeholders

Around 20% of the attorney’s time is consumed by their legal research on previous judgments, case files and recordings. Especially in Patent application filing where the prior art search involves manual keyword-based searching for related patents. A good part of the attorney’s time is spent on such mundane and repetitive work that they don’t have time to invest in the more innovative and creative aspects of patent applications. We are working on building a semantic analysis tool that will do a semantic search on all the documents to pre-select existing closely related to the new cases for the attorney to review. The ability to search within a defined boundary and also across the web would help attorneys to increase their efficiency.

Semantic Indexing

The conversion process of unstructured text data to structured analyzable data

Semantic Indexing is sorting of documents like proposals, resumes in order to relatedness to each other or to a reference document. This is a very useful technique in automated pre-ranking of documents for humans to analyze. Semantic indexing is very useful in the context of many use cases that allows our clients to automate repetitive part of their work and concentrate on high-value propositions. We are building a semantic indexing solution for automatically ranking proposals for an RFP by comparing if the proposals have addressed every requirement in the RFP or not.

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