AI Research

AI & ML solutions

Research is the most vital tool for organizations to get information and be up to date on the latest innovations and trends. With the power of Artificial Intelligence, secondary data research can be made much faster and accurate, processing large amounts of information simultaneously from various sources such as peer-reviewed publications, journals, government archives, online libraries, and even by scraping the internet for information.

NATURAL LANGUAGE PROCESSING - NAMED ENTITY RECOGNITION

Market Research

When considering new market opportunities or potential investments, evaluating the competitive landscape is standard due diligence. The procedure typically involves enumerating competitors and then analyzing their relative position to your organization or others. With hundreds of competitors having their own portfolios of products and services in the same space, manual market research can become exhausting. Using Natural Language Processing, market research can be made more thorough and accurately represent the business landscape. It can identify major players, their technologies, and capabilities by analyzing and extracting textual information from websites, organizational database platforms, search engine listings, news clippings, and more.

Market Research

NATURAL LANGUAGE PROCESSING - NAMED ENTITY RECOGNITION

Automatic Text Summarization

With the advent of the internet, there has been an explosion in the amount of text data available from a variety of sources. These data sources can span from PDF text documents to textual information on websites and data libraries, and can be used for a variety of research operations but it may need to be effectively summarized to be useful. Manual summarizations efforts do not scale well with the large amount of text information at hand and is prone to human-error. AI automatically summarizes the text data using Natural Language Processing to produce a concise summary of it and preserves the meaning of the original text information.

Automatic Text Summarization

NATURAL LANGUAGE PROCESSING - NAMED ENTITY RECOGNITION

Legal Contract Analysis

With the advent of the internet, there has been an explosion in the amount of text data available from a variety of sources. These data sources can span from PDF text documents to textual information on websites and data libraries, and can be used for a variety of research operations but it may need to be effectively summarized to be useful. Manual summarizations efforts do not scale well with the large amount of text information at hand and is prone to human-error. AI automatically summarizes the text data using Natural Language Processing to produce a concise summary of it and preserves the meaning of the original text information.

Legal Contract Analysis

NATURAL LANGUAGE PROCESSING - NAMED ENTITY RECOGNITION

Financial Contract Analysis

Documentation analysis is one of the most time-consuming, yet most crucial processes in financial institutions. Employees spend a lot of time reading through physical and digital documents, and can still miss key information. Using Named Entity and Optical Character Recognition, AI models can assist in this analysis by automatically extracting relevant clauses and entities from Loan/Credit Agreements, Collateral Valuations Reports, Financial Leasing Contracts, etc. This saves banks hundreds of thousands in man-hours, minimizes risk, uncovers hidden costs, and maximizes revenue by diverting employee attention to more productive tasks.

Financial Contract Analysis

NATURAL LANGUAGE PROCESSING - NAMED ENTITY RECOGNITION

Legal Research

As part of legal research, law professionals have to search through various sources that include legal dictionaries, legal encyclopedias, legal periodicals, annotations, and treatises, etc, most of which are digital these days. Text search methods give exact matches but most often miss the researcher’s intent, and most often very time-consuming. AI provides a smarter solution to this problem, easily identifying related documents while taking context into consideration. Natural Language Processing can help find significance and relevance between the text or string input with these documents, where regular text search would fail. This vastly improves the speed, accuracy, and efficiency of retrieving the most relevant information.

Legal Research

NATURAL LANGUAGE PROCESSING - NAMED ENTITY RECOGNITION

Biomedical Named Entity Recognition

Bio-entity extraction is a core task of information extraction from medical literature. Examples of such entities include names of genes, proteins, location of the activity, etc. With the increase in biomedical record content, human annotators find this extraction task tedious. Artificial Intelligence and Deep Learning can automate identifying and classifying bio-medical terms into predefined categories, from blocks of unstructured text data.

Biomedical Named Entity Recognition

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