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

    OpenAI Application for Conversational AI

    Conversational AI, also known as chatbots, are powered by natural language processing (NLP) and machine learning (ML) technologies. We leverage OpenAI's NLP capabilities which allow chatbots to understand and respond to human language in a natural way. This includes the ability to understand the meaning of words and phrases, as well as the ability to generate text that is grammatically correct and semantically meaningful.

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    How It Works?

    We are leveraging OpenAI's NLP and ML capabilities to make the Conversational AI systems flexible and cognitive. OpenAI's GPT models are capable of generating text that is almost indistinguishable from text written by humans and enabling chatbots to generate natural and engaging conversations.

    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 collection for the development of Conversational AI involves gathering diverse and relevant conversational datasets, including user queries and corresponding responses. This data is essential for training and fine-tuning machine learning models, enabling the AI system to understand and generate appropriate responses in real-world conversations.

    Model Training Using OpenAI

    For training our Conversational AI model, we leveraged the power of OpenAI's ChatGPT, a state-of-the-art technology known for its impressive capabilities in understanding and generating human-like responses. This choice aligns with the current trend of utilizing advanced AI models to develop highly effective and engaging conversational experiences.

    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 This Conversational AI model can also be integrated on various websites as well like Customer Support , E-commerce, Voice Assistants, Social Media, Healthcare, Education, etc.

    What We Offer

    ThirdEye Data builds advanced Conversational AI systems for enterprises to execute natural, relevant, semantic and engaging conversations though chatbots. 

    OpneAI Integration

    Leveraging OpenAI's machine learning models and NLP capabilities o understand and respond to human language in a natural way. This includes the ability to understand the meaning of words and phrases.

    Data Transformation

    Taking care of the data collection, cleaning, transforming, and preparing the audio and text data at scale for feeding the chatbots, which allows them to learn how to respond to different prompts and questions.

    Model Development

    Develop custom machine learning model including feature selection, algorithm and techniques to execute smart and prompt conversational AI systems. We also take care of the model maintenance and re-train parts.

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    We have prepared smart Conversational AI models 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 conversational AI systems, businesses may have some common asks. We are trying to answer them here.

      The chatbot's NLP engine works by breaking down the user's input into smaller units of meaning, such as words, phrases, and entities. The engine then uses these units of meaning to identify the user's intent and to generate a response.

      The chatbot handles complex queries by using a combination of NLP techniques, such as semantic parsing and machine reasoning. These techniques allow the chatbot to understand the meaning of complex queries and to generate accurate and relevant responses.

      The chatbot can integrate with existing CRM system by using a variety of APIs. This allows the chatbot to access customer data from the CRM system and to use this data to generate more personalized responses.

      You can measure the success of the chatbot by using a variety of metrics, such as customer satisfaction, lead generation, sales, and cost savings. These metrics can help you to track how the chatbot is performing and to identify areas where it can be improved.

      The cost of developing and deploying a chatbot will vary depending on the complexity of the chatbot, the features that are included, and the development team that is used.

      The chatbot's accuracy rate depends on a number of factors, including the quality of the training data, the complexity of the queries, and the design of the NLP engine. However, in general, chatbots can achieve accuracy rates of over 90%.

      Services We Offer

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      Image Detection Solutions
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      Conversational AI & ChatBots
      Intelligent Document Processing
      Summarize Large Documents
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      Contact Center Analytics
      Customer Churn Prediction
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