About Us

What does teX.ai offer?

Businesses today face challenges deriving insights from their text data. teX.ai helps produce structured data, metadata & insights by extracting data, summarizing information and classifying content.

Product

Too long; didn’t read (TL; DR) shouldn’t be an excuse to ignore your valuable text data. What to do with your text heap? When you propose this question, we don’t dispose it. We rather pick the nuggets, extract the tables, classify into labels from your unstructured data. In short, teX.ai extracts, summarizes and classifies!

Audience

Firms which have text data in the form of documents, reviews, articles, records of text description are good candidates for teX.ai. There are multiple modules which cater to different needs of different firms. Financial research firms with loads of documents wanting to extract tables; all B2C firms looking to sift through the reviews and grasp the key topics discussed; firms who want to classify their text description records which can be product description, calls, tickets, complaints, into categories – can all use teX.ai.

Indium Software Logo

teX.ai is a SaaS product solution from Indium Software, a technology solutions company. Incepted in 1999, Indium is a ISO 27001 certified company with 1000+ team members, servicing 350+ clients across several domains.

Indium Software’s mission is to provide customer-centric high quality technology solutions that deliver business value for Fortune 500 and Global Enterprises.

For more information, visit www.indiumsoftware.com

Benefits

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Prioritize and address the focus areas by finding answers to the following questions:

Which topics are being discussed in your reviews and complaints?
What key phrases form these topics?
What are the weights of these words?

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Reduce the time in extracting tables from the documents.

How to extract the unstructured tables, images of tables, PDF tables out of the text maze in a document?

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Save time and effort by identifying the categories from large volumes of content.

Some examples – How to categorize millions of products?
What are the labels for the complaints?
Which department should be allotted for each ticket?

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