Custom Search
 



 
Unearthing information packed nuggets

Ram Sukumar, CEO of Indium software, talks about data, text analytics and more.

October 10 2020: In this day and age, along with the burgeoning amount of data, we also see a drastic increase in the need for finding a way to form structured data in order to derive insights. Across industries, this need has become increasingly paramount. To fill this gap, Indium has come out with teX.ai.

Unstructured data: The struggle

The volume of unstructured data across industries is huge and that is where the treasure lies. Unstructured data contains nuggets of information that may remain unearthed. The struggle with unstructured data is dealing with various formats such as pages of documents, text bytes, images of text, text with tables, so on and so forth. Machines can process patterned datasets only. In order to do this, unstructured data has to be structured.

teX.ai: Structuring data made easier:

It has long been Indium’s vision to expand into a SaaS or PaaS or a product-based revenue model in addition to the core services business. teX.ai was launched in 2019 combining Indium’s Text Analytics, AI, Cloud and Product Development expertise.

teX.ai is an Automated text analytics product powered by AI. It aims to automate the manual activities involved in text analytics and thereby improving the turnaround time and reducing costs. Offered both as a Cloud and On-Premise solution, teX.AI addresses a growing need in several industries to leverage text analytics to drive operational efficiencies and enhance customer experiences.

Text analysis involves pre-processing the text before moving to the analysis part. Pre-processing involves techniques such as Stemming, lemmatization, stop words removal. Analysis for example involves constructing features like the word count by text type, creating TF-IDF vectors and modeling the NER (Name Entity Recognition) pattern.

How teX.ai works:

The text is converted to vectors and then processed to form insights by different methods including Synonymizing, Identifying NER and Summarizing. Depending on the use case, the techniques vary. Summarization uses Condition Random Fields, topics identification uses LDA topic modeling, Text categorization uses ML classification techniques. To pick out key phrases in a paragraph, linguistics techniques like POS tagging and dependency parsing are used.

Industry-wise benefits of Text Analytics:|

Ai based Text Analytics benefits different industries in different ways.

Usually a task like summarization and analysis of 1000’s of customer reviews for an e-commerce or retail firm would be challenging with lot of human effort. With an Ai-based text analytics software this can be achieved in a few hours.

Depending on the industry there are several use cases for Text Analytics. Some examples –

- In the Banking industry, going through several pages of large documents is humanly impossible, and there is a need to extract relevant text from the documents into a format that is more easily consumed by the Analysts. This could also be applied to processing Invoices, processing bank statements for loan processing, Annual Reports etc. This largely streamlines operations by reducing turnaround time, improving accuracy and savings costs. The same use case can be applied in the Insurance industry for Mediclaim Documents Processing and automating tasks in the process.

- For a consumer brand that gets Customer Reviews, there are several channels in which Brands get reviews – at stores, online, Social Media. It is challenging to go through and summarize every review for a global brand. Having a text analytics engine that goes through these reviews and summarizes them can help improve customer experiences and can have a huge business impact for the brand.

- Manufacturing industry has needs to extract text from several documents such as Invoices, Delivery Receipts, Engineering Drawings etc.|There are many more such use cases across several industries.

Significance of teX.ai Post Covid:

For any company to come out stronger through a challenging phase such as this, it is important to look for opportunities to optimize costs and build efficiencies. There are multiple industries like e-commerce, financial services, insurance, manufacturing and healthcare where Ai driven automated text analytics is a solution that helps improve process efficiencies, customer experiences & turnaround times. In the post-covid era, companies will look to reduce costs and increase turnaround times for processing loan documents, Invoice processing, Mediclaims, etc. On the other hand, enterprises will also be looking to use text analytics to better understand customers through review analysis and improve experiences. This is where teX.ai could further help customers in the post Covid era.