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    Mostly Used By Manufacturing, Healthcare, Legal, Finance, Retail Industries

    OpenAI Application for Summarize Large Documents

    Leveraging OpenAI's LLMs to develop a comprehensive system to summarize large documents to help enterprises. OpenAI's GPT-4 is a large language model that has been trained on a massive dataset of text and code. This means that it has a deep understanding of language and can generate summaries that are accurate, comprehensive, and informative.

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    How It Works?

    We are leveraging OpenAI's NLP capabilities and LLM's to summarize documents at three levels of depth: abstractive, extractive, and hybrid. This allows enterprises to choose the level of detail that is most appropriate for specific needs.

    Data Collection & Preparation

    In this phase, we leverage tools like Azure Data Factory, Azure Blob Storage, Azure Databricks or Azure Synapse Analytics to store, manage and transform the data collected. Data has been gathered from various sources, including books, websites, written materials, pdfs, etc. for training of this model. This diverse collection of information enables the model to understand and summarize extensive textual content effectively.

    Model Training Using OpenAI

    We've harnessed OpenAI's cutting-edge techniques to develop a powerful model for Large Document Summarization. Using OpenAI's expertise, our model has masters the art of extracting crucial insights from vast documents, empowering you to uncover the essence effortlessly and also experience the future of document summarization with OpenAI's groundbreaking technology.

    Model Deployment

    We prefer Azure Services like Azure Functions, Azure Kubernetes Services (AKS), or Azure App Services to deploy the Contact Center Analytics model, but the same model will also be able to deploy on other cloud platforms like AWS Services e.g; Amazon SageMaker, Amazon Elastic Compute Cloud (EC2), Amazon S3 (Simple Storage Service) AWS Lambda, for GCP Services like Google Compute Engine, Google Cloud Functions, Google Cloud AI Platform can be used, HuggingFace, etc.

    What We Offer

    ThirdEye Data builds advanced OpenAI-powered systems for enterprises to summarize large documents. 

    OpneAI Integration

    Leveraging OpenAI's LLMs and NLP capabilities to summarize large files and datasets.

    Data Transformation

    Taking care of the data collection, cleaning, transforming, and preparing the dataset at scale.

    Model Development

    Develop custom model including feature selection, algorithm and techniques to summarize large files.

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    We have prepared advanced document summarization system which are ready to be deployed.

    Customer Feedbacks

    All the reviews are independently verified by Clutch.

    ThirdEye Data brought in their best to support us.

    Kumar Mankala Advisor of AI/ML Program & Products, SCE

    They went above and beyond to actualize the our ambitions.

    Matt Singer CEO, EcoTrak

    Their project management and communication were top-notch.

    Andriy Romanyuk Head of R&D, Glas Trösch Group

      Frequently Asked Questions

      Before leveraging the Large Documents Summarization systems, businesses may have some common asks. We are trying to answer them here.

      The main challenge is that GPT-4 is a large language model, which means that it requires a lot of computational resources to run. This can be a problem for businesses that don't have the necessary infrastructure.

      One benefit is that GPT-4 can summarize documents at three levels of depth: abstractive, extractive, and hybrid. This allows businesses to choose the level of detail that is most appropriate for their needs.

      Another benefit is that GPT-4 is available through an API, which makes it easy to integrate into existing applications.

      One way is to sell the summaries to businesses that need to quickly and easily understand large amounts of text.

      Another way is to use the summaries to improve the customer experience. For example, a business could use summaries to generate personalized recommendations for customers.

      Finally, businesses could also use summaries to generate new products or services. For example, a business could use summaries to create a new line of educational materials.

      • One ethical consideration is that GPT-4 can be used to generate summaries that are biased or inaccurate. Businesses need to be careful not to use GPT-4 in a way that could harm or mislead people.
      • Another ethical consideration is that GPT-4 is trained on a massive dataset of text and code. This dataset includes text from a variety of sources, including some that may be considered sensitive or controversial. Businesses need to be aware of the potential for GPT-4 to generate summaries that contain sensitive or controversial information.
      • One way to ensure the quality of the summaries is to use a feedback loop. This involves having a human review the summaries and provide feedback to the GPT-4 model. This feedback can help the model to improve its performance over time.
      • Another way to ensure the quality of the summaries is to use a variety of metrics to measure the accuracy and comprehensiveness of the summaries. These metrics can help businesses to identify any areas where the GPT-4 model is not performing well.

      The amount of time it takes to build a document summarization system with GPT-4 depends on a number of factors, including the size and complexity of the documents you want to summarize, the level of detail you want in the summaries, and your own experience with natural language processing (NLP).

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