Ever felt like the wealth of knowledge in your organization's documents is being underused?

Explore Parseqa

Extract information nuggets from
large knowledge bases efficiently

ParseQa is a state-of-the-art Natural Language Prccessing (NLP) system that combines information extraction, semantic search and question answering to leverage large heterogeneous knowledge repositories effectively.
ParseQa can alter the way content management systems are perceived and used in organizations.

Go beyond off-the-shelf search engines

Extract valuable insights
Find precise information from deep inside knowledge bases
With cutting-edge NLP technology
Leverage the latest advances in NLP and Deep Learning coupled with proprietary Inscripta technology
From heterogenous data
Supports variety of data sources like docs, videos, blogs, short text like reviews, including unstructured text, tables and images
Just Ask
Get contextual and precise answers to ad hoc questions, at different levels of granularity
// Key Challenges Addressed by ParseQa

ParseQa: Pushing the Boundaries of Knowledge Search

Most organizations have crucial knowledge scattered across a large number of documents, such as, technical manuals, knowledge articles, patents, policy documents, contracts and scientific publications, often in varied formats. Huge value is created by making this knowledge searchable and extracting the valuable insights therein. To this end, Inscripta’s ParseQa suite of libraries builds on the latest research in Document Processing, NLP and Search to create cutting-edge solutions that can be customized for a variety of domains and use-cases with little effort.

Easily extend and adapt for different domains

Ideal back-end question anwsering engine for customer support chatbots, automated helpdesks, and more
Generate relevant questions from data for automated FAQ creation
Information extraction and question answering for scientific documents

Case Studies

// Life Sciences | Pharma

Insights from clinical trials data

ParseQa has been used for information extraction and analysis from clinical trials data published in journal articles. This system extracts relevant data from unstructured text, tables and images. Also integrated is a question answering and search interface to facilitate intuitive and natural dialogue with the system.

Extraction of quantitative data like study statistics, efficacy outcome and adverse effects
Extraction of qualitative data like study details, inclusion/exclusion criteria and investigator details
Analytics layer for quick insights from extracted data
Natural language querying for easy access to the wealth of extraced data
// IT | Fintech | Legal


ParseQa has been deployed as an intelligent backend question answering-cum-search engine for FAQbots. ParseQa performs semantic indexing on various document formats, including unstructured text, tables and images in its ambit. Unlike question matching or keyword based chatbots, our smart search API allows natural queries that make the FAQbot intuitive and powerful for end users.

Parse in multiple formats (DOCX, PDF, and image content) for a rich backend knowledge base
Smart search and question answering for a more powerful bot experience
Provide answers at the right level of granularity
Quick customization for different domains

Leverage large knowledge repositories effectively with ParseQa

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Find out what ParseQa can do for you
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