This dataset provides detailed, synthetic healthcare chatbot conversations with annotated intent labels, message sequencing, and extracted entities. Designed for training and evaluating conversational AI, it supports intent classification, dialogue modeling, and entity recognition in healthcare virtual assistants. The dataset enables robust analysis of user-bot interactions for improved patient engagement and automation.

We develop a chatbot using deep bidirectional transformer (BERT) models to handle client questions in financial investment customer service. The bot can recognize 381 intents, decides when to say I don’t know, and escalate escalation/uncertain questions to human operators. Our main novel contribution is the discussion about the uncertainty measure for BERT, where three different approaches are systematically compared with real problems. We investigated two uncertainty metrics, information entropy and variance of dropout sampling, in BERT, followed by mixed-integer programming to optimize decision thresholds. Another novel contribution is the usage of BERT as a language model in automatic spelling correction. Inputs with accidental spelling errors can significantly decrease intent classification performance. The proposed approach combines probabilities from masked language model and word edit distances to find the best corrections for misspelled words. The chatbot and the entire conversational AI system are developed using open-source tools and deployed within our company’s intranet. The proposed approach can be useful for industries seeking similar in-house solutions in their specific business domains. We share all our code and a sample chatbot built on a public data set on GitHub.

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Generative AI In Customer Services Market Size 2025-2029

The generative AI in customer services market size is forecast to increase by USD 969.6 million, at a CAGR of 25.3% between 2024 and 2029.

The market is experiencing significant growth, driven by the rising imperative for hyper-personalized and proactive customer engagement. Companies are increasingly leveraging Generative AI to provide tailored solutions and anticipate customer needs, enhancing the overall customer experience. However, this ascension of hyper-personalization at scale presents complex challenges. Navigating the intricate web of data privacy, security, and evolving regulatory landscapes is essential for businesses seeking to capitalize on this market opportunity. Predictive analytics and Big Data analytics offer advanced capabilities, while deployment models cater to on-premises integration needs.
Additionally, the integration of Generative AI into existing customer service systems requires careful planning and execution to ensure seamless implementation and optimal performance. Companies must address these challenges head-on to effectively harness the potential of Generative AI in customer services and stay competitive in today's market. Ensuring customer data is protected while generating personalized responses is a critical balance to maintain. Model bias, data privacy, and data security remain critical concerns.

What will be the Size of the Generative AI In Customer Services Market during the forecast period?

Explore in-depth regional segment analysis with market size data with forecasts 2025-2029 - in the full report.
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The market for generative AI in customer services continues to evolve, with applications spanning various sectors, including healthcare, finance, and retail. Text-to-speech technology and speech-to-text conversion are integral components, enabling seamless communication between customers and AI systems. A continuous learning system and feedback loop mechanism facilitate improvements, while supervised learning and unsupervised learning algorithms refine intent classification and entity extraction. Security protocols and data privacy measures are essential, with reinforcement learning and model evaluation metrics ensuring compliance with industry standards. Hybrid chatbot approaches, combining rule-based and policy-based systems, provide contextual understanding and response generation. Model training pipelines employ deep learning algorithms, while scalable architecture and API integration strategies ensure efficient integration.
For instance, a leading retailer reported a 25% increase in sales due to the implementation of a generative AI customer service system. Industry growth is expected to reach 20% annually, driven by the ongoing development of advanced AI technologies and the increasing demand for personalized, efficient customer interactions. Engaging virtual reality (VR) and augmented reality (AR) language learning videos are gaining traction, providing users with authentic language experiences.

How is this Generative AI In Customer Services Market segmented?

The generative AI in customer services market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029,for the following segments.

Deployment

  Cloud-based
  On-premises


Product

  Chatbot and virtual assistance
  Sentiment and feedback analysis tools
  AI driven ticketing system
  Personalized recommendation
  Others


End-user

  BFSI
  Telecommunication
  Media and entertainment
  Healthcare and life sciences
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Deployment Insights

The Cloud-based segment is estimated to witness significant growth during the forecast period. The market is witnessing significant growth, with cloud-based deployment leading the way. This model enables businesses to access advanced AI capabilities through third-party cloud service providers like Amazon Web Services, Google Cloud, and Microsoft Azure. The cloud's accessibility, scalability, and economic efficiency make it an attractive option for small and medium-sized enterprises, allowing them to avoid substantial upfront investment in specialized hardware and infrastructure. Generative AI technologies, such as AI-powered chatbots, are revolutionizing customer services by optimizing resolution time, enhancing conversational analytics, and improving first contact resolution. Natural language processing and machine learning models enable intent recognition and response time measurement, while real-time interaction tracking and knowledge graph technology ensure a seamless customer journey.

📢 Government Complaint Audio Dataset (Hindi & English)

This dataset contains bilingual audio recordings of government-related customer complaints in Hindi and English, generated using Text-to-Speech (TTS). It is designed to help in research and development of speech recognition, intent classification, sentiment analysis, and multilingual voice-based chatbots for public service platforms.

  📂 Dataset Structure

GCD-Government_Complaints_Dataset/ ├── english/ │ ├──… See the full description on the dataset page: https://huggingface.co/datasets/Munavvara-17/GCD-Government_Compliants_Dataset.