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Countries where we have happy customers

1050+

Agile enabled employees

06

World wide offices

12+

Years of Experience

05

Agile Coaches

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1000+

Clients projects

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Happy customers

Overview

This case study explores how PdfGPT leverages AI, machine learning, and natural language processing to convert PDFs into interactive chatbots. PdfGPT provides a user-friendly experience by allowing users to communicate with PDFs conversationally. It understands complex language and provides tailored responses based on users’ prior interactions.

Technical Stack

  • React JS
  • Open AI
  • NextJS
  • HTML5
  • CSS
  • AWS

    • Industry

    Information Technology

  • region

    • Region

    USA

  • project-size

    • Project Size

    $10000 to $20000

Highlights

OpenAI API Key

Easy User Interface

Interact with PDF

AI-powered tool

Challenges & Solutions

OpenAI, being a new technology, little is known about its limitations and challenges. Integration complexity was one of the major issues we faced during this project.

  • Solution: We got the API from OpenAI to access their ChatGPT model and seamlessly over-bridged the usage limits. With a step-by-step approach, we conducted thorough testing and debugging; we made the best use of a well-documented API for seamless integration.

The client wanted a clean and easy user interface that supports a Q&A interaction format similar to ChatGPT.

  • Solution: We used React for the front end to create a dynamic and responsive user interface. In the backend, we used Next.js with Flask. We also focused on a sleek, contemporary appearance, with a mix of blues and grays, giving a professional look to PdfGPT. We enhanced the navigation and made the tool mobile responsive.

Another challenge was training the ChatGPT model to assess and analyze the PDF document. Some PDF documents may have formatting or encoding issues that could affect the accuracy of the analysis.

  • Solution: We utilized pre-processing techniques such as OCR (optical character recognition) to extract text from the PDF. We then used ML algorithms and trained ChatGPT to analyze and understand the extracted data. We also introduced NLP techniques, making it easier to understand the context and structure of the text.Besides, our ML developers worked on summarization and entity recognition techniques to extract critical information and make it more accessible to the user.

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12+

Years of Business
Experience

1458+

Happy
Customers

12+

Countries with
Happy Customers

1050+

Agile enabled
employees

Core Features

  • Chat with PDF documents and ask queries.
  • Contemporary appearance and an intuitive user interface.
  • Appealing icons and labels for seamless navigation.
  • Responsive design, adjusting any screen size of any device.
  • Real-time Q7A feature to interact with the uploaded PDF.
  • Faster response time to answer user queries.
  • no.-of-resources

    • No. of Resources

    02

  • time-frame

    • Time Frame

    January 2023 - March 2023

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