biomedical language processing. powered by machine learning. using big data.

Built and trained for pharma business use cases, tellic’s cutting edge automated machine learning text curation engine lets you process billions of text documents with PhD level accuracy

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curated external data asset

Purpose-built for biomedical text, machine learning pipelines processes unstructured data with PhD accuracy

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biomedical Concept search

+6X increase in results & ranking by what is most relevant to quickly deliver insights your scientists

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PHARMA Knowledge graph

Automated pipeline generates a knowledge graph uncovering insights  that enable data-driven discovery decisions

What makes us different is that we are designed for pharma and customize for your organizational language.

  • AI-based NLP delivers PhD accuracy for detecting biomedical concepts in text data

  • Proprietary machine learning links biomedical concepts into preferred ontologies

  • Context-driven ranking delivers the most important research based on concept

  • Support for company specific jargon, ontologies and identifiers that evolve over time

  • Integrated solution of proprietary and best-in-class packages is engineered to scale for big data

  • Robust preprocessing and domain specific metadata enhances downstream processes

Employing a combination of dictionary terms and hand coded rules, most search solutions for biomedical data lack the ability handle different structures of text data and therefore fail to deliver useful results, and lack utility for scientists to focus in on what’s most important to them. tellic’s machine learning and AI technology extracts common terms and relationships from biomedical text then to help scientists:  1. Find a greater set of relevant documents by extending the reach of search terms by identifying synonyms and understanding the context a term is used.  2. Ignores sets of irrelevant documents by using the context a term is used to identify false positives results  3. Enabling scientists to quickly drill down to find the results most documents most relevant to them using customized labels trained on their organizational language

Employing a combination of dictionary terms and hand coded rules, most search solutions for biomedical data lack the ability handle different structures of text data and therefore fail to deliver useful results, and lack utility for scientists to focus in on what’s most important to them. tellic’s machine learning and AI technology extracts common terms and relationships from biomedical text then to help scientists:

1. Find a greater set of relevant documents by extending the reach of search terms by identifying synonyms and understanding the context a term is used.

2. Ignores sets of irrelevant documents by using the context a term is used to identify false positives results

3. Enabling scientists to quickly drill down to find the results most documents most relevant to them using customized labels trained on their organizational language

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Contact us to see a demo and learn more about tellic