TQ uses many large language models (LLMs) to answer queries in everyday language, and our tool will understand the context and provide accurate and relevant results. Whether you need to analyze large datasets, conduct sentiment analysis, or extract valuable insights from text, our TQ tool is your go-to solution.

TS uses LLMs to help summarize data, wherein the user can set the number of words in the output. Save time and effort in digesting lengthy documents with our TS tool. It employs advanced algorithms to generate concise and coherent summaries, preserving key information and crucial points.

We use AI/ML models to provide appropriate word breaks (instead of hyphens) for typesetting in various Indian languages, including Tamil, Bengali, and Malayalam. Our hyphenator enhances the readability and visual appeal of your documents and achieve aesthetically pleasing and professional-looking typography.

Our AI-based Ref-Styler converts raw reference data to any particular standard style, such APA, AMA and PLOS. This intelligent tool automatically formats your references and citations according to various citation styles. Eliminate manual formatting errors and enhance the credibility of your academic or professional documents.

QUAE is not just another AI; it's your personal speech-to-speech assistant designed to revolutionize how you interact with technology. With QUAE, your voice commands are not just heard but understood within the context of your queries, delivering precise and relevant responses instantly.

As a part of our commitment to the publishing domain, we have developed a WYSIWYG (CQRL) editor with math support, which is an extended markdown format editor that allows keyboard macros and supports rich HTML5 formatting with animation, graphics, and tables.

Our unique MathML editor tool facilitates the creation and publishing of mathematical content in any platform as a plugin. Our solutions enable publishers to efficiently produce high-quality scientific and technical documents with complex mathematical equations.

For Tamil language (unlike English language), the rules for breaking words have not been automated yet. We have implemented an SOTA (state of the art) hyphenation algorithm using a robust machine learning frameworks using a corpus of hyphenated words with breaking points curated by language experts.