Project Summary

Our client wanted the development of a Generative AI module that functions as a chat interface to expedite the work of legal teams. This module should be able to automatically produce document summaries, identify crucial phrases and keywords, and verify the inclusion of essential legal clauses. Furthermore, it must incorporate bias-detection capabilities to ensure the documents’ fairness and impartiality.

Technical Stack

  • TypeScript
  • C++
  • Java
  • .Net
  • SQL
  • Industry

    Information technology

  • region
  • Region


  • project-size
  • Project Size



Structured Document Parsing

OCR for Scanned Images

Generative AI Module

Bias Identification


  • Create a versatile knowledge base that caters to a wide range of legal document types and can effectively parse the structured format of such documents.

Technical Challenges

  • During the parsing of formatted documents, an issue of overfitting to similar structures was identified. To address this, we employed vectorization techniques on repeated sections, enhancing document quality.
  • Maintaining robust data security and anonymity was of paramount importance to our clients. To safeguard this, we adapted the vectored documents so that sensitive information, such as names, remains concealed unless authorized by administrators.
  • Additionally, we were tasked with developing a restriction for scanned image pages, allowing only English-language images to be processed by the algorithm. This restriction was essential as users could upload various image formats and request algorithmic operations.


  • We developed a web application featuring a chat interface that enables users to upload various document types, including Word documents, PDF files, and scanned images. The application provides results tailored to the user's requirements. Pre-defined prompts were allocated for each task, while users also had the option to input their prompts to customize the results.
  • For scanned images, Optical Character Recognition (OCR) technology was applied to extract text, which was then tokenized and used as input tokens for the Generative AI model. Word documents and PDF files were tokenized directly.
  • The system was designed to answer legal document-related queries within a speedy 1-2 second response time. The analysis of the document, regardless of its size, was targeted to be completed within 8-10 seconds.

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Years of Business




Countries with
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Agile enabled

Benefits Post-Implementation

Integrating GenAI into the legal sector is viewed as an innovative approach. The law firm for which this GenAI was tailored has identified several advantages, including:

  • Rapid generation of document summaries and keyword extraction from historical documents.
  • Simplification of the process of comprehending and sharing knowledge.
  • Efficient identification of bias within extensive documents, even spanning hundreds of pages.
  • Automatic flagging of unsuitable content within the documents.
  • An impressive initial productivity increase of 14%, which subsequently led to a boost in revenue.
  • no.-of-resources
  • No. of Developers


  • time-frame
  • Time Frame

    April 2020- March 2022

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