# AI Assistant Build and train a specialized AI assistant using real-time data from your knowledge base. ## Workloads Conversational AI / NLP Generative AI ## Industries Financial Services Healthcare and Life Sciences Retail/ Consumer Packaged Goods Telecommunications ## Business Goal Innovation Return on Investment ## Products NVIDIA AI Enterprise NVIDIA NIM NVIDIA NeMo NVIDIA NeMo Retriever NVIDIA Riva NVIDIA ACE NVIDIA DGX 1. Overview 2. Technical Implementation 3. FAQ ## Scale Your Business Operations With an AI Assistant AI-powered tools like chatbots and virtual AI assistants have become essential for companies to scale operations and service their growing customer base. According to a recent IDC study on [conversational AI](https://www.nvidia.com/en-us/glossary/conversational-ai.md), 41% of organizations use AI-powered copilots for customer service, and 60% have implemented them for IT help desks. [Generative AI](https://www.nvidia.com/en-us/glossary/generative-ai.md) applications trained in domain-specific languages and enhanced with [retrieval-augmented generation](https://www.nvidia.com/en-us/glossary/retrieval-augmented-generation.md) (RAG) deliver highly accurate, context-aware interactions far beyond what traditional solutions, and even chatbots, can provide. More recently, advances in AI reasoning are enhancing how these tools can operate as autonomous [AI agents](https://www.nvidia.com/en-us/glossary/ai-agents.md). **Supporting human agents with real-time customer communication tools** According to NVIDIA’s [2025 State of AI in Financial Services survey report](https://www.nvidia.com/en-us/industries/finance/ai-financial-services-report.md), 60% of respondents are exploring generative AI and [large language models](https://www.nvidia.com/en-us/glossary/large-language-models.md) (LLMs) for elevating customer experiences and engagement. From call center transcription to intelligent chatbots, AI is helping execute common banking tasks to remove barriers to quality customer support. Self-service banking tools powered by LLMs help automate bill payments, transfers, and even personalized financial advice and investment recommendations. **Offering personalized experiences to capture sales conversions** According to NVIDIA’s second annual [State of AI in Retail and CPG survey report](https://www.nvidia.com/en-us/lp/industries/state-of-ai-in-retail-and-cpg.md), 80% of companies are either using or piloting generative AI projects. Retailers are building AI chatbots and virtual assistant solutions to predict ecommerce user intent and provide next-item recommendations, answer common questions, and optimize in-store product placement. **Advanced patient healthcare to offload staff workload** Automation is key to operational efficiency, as patients benefit from streamlined services for appointment setting, medication reminders, and post-visit communications. With multi-language support capabilities, providers can also ensure patients receive high-quality advice that helps them make better-informed decisions. **Greater operational efficiency across your business to scale services** Telecommunication companies must maintain network availability, performance, and security—all while serving their customers’ everyday needs. Using AI in call centers to automate processes like order management and case summarization helps retain customers and increase revenue opportunities. Quick Links: [Read Solution Brief: T-Mobile Details Speech AI for Award-Winning Customer Care](https://resources.nvidia.com/en-us-ai-powered-operations/speech-ai-customer?lx=2dC8ST) [Download Report: State of AI in Financial Services](https://www.nvidia.com/en-us/industries/finance/ai-financial-services-report.md) [Download Report: State of AI in Retail and CPG: 2025 Trends](https://www.nvidia.com/en-us/lp/industries/state-of-ai-in-retail-and-cpg.md) [Explore AI Agents for Healthcare Contact Centers](https://www.nvidia.com/en-us/use-cases/ai-agents-for-healthcare.md) ## Develop an AI Assistant With NVIDIA AI To build an AI assistant with generative AI and RAG, you must consider data curation, governance, security, scalability, and complexity. Organizations can simplify the development and deployment of these applications with NVIDIA Blueprints and [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise.md), a cloud-native software platform that provides institutions with enterprise-grade security, support, and key technologies to deliver optimized performance and scale AI confidently. **Use a Reference Workflow to Jump-Start Building an AI Assistant** NVIDIA Blueprints are comprehensive reference workflows that accelerate AI application development and deployment, featuring NVIDIA acceleration libraries, SDKs, and microservices for AI agents, digital twins, and more. Download the [AI assistants for customer service blueprint or](https://build.nvidia.com/nvidia/ai-virtual-assistant-for-customer-service/blueprintcard) develop a scalable, customizable [enterprise RAG pipeline as the foundation to your application](https://build.nvidia.com/nvidia/build-an-enterprise-rag-pipeline). Use them as is or combine them with other blueprints for advanced applications, such as [digital humans](https://build.nvidia.com/nvidia/digital-humans-for-customer-service). The [digital humans for customer service AI Blueprint](https://build.nvidia.com/nvidia/digital-humans-for-customer-service) is powered by NVIDIA ACE technologies, bringing enterprise applications to life with a 3D or 2D animated digital human interface. With approachable, humanlike interactions, customer-facing applications can provide more engaging user experiences compared to traditional customer service options. **Leverage State-of-the-Art Generative AI Models** [NVIDIA NIM™](https://www.nvidia.com/en-us/ai.md) streamlines the deployment of the latest AI models with industry-standard APIs and continuously maintained, enterprise-grade software. Its prebuilt, optimized inference microservices enable AI assistants to run efficiently across cloud, data center, and workstation environments. **Customize Your Generative AI Models for Personalized, Enterprise-Ready AI Assistants** The [NVIDIA NeMo™](https://www.nvidia.com/en-us/ai-data-science/products/nemo.md) platform is the complete solution for building enterprise-ready assistants, with several components that enhance AI assistant performance. To drive continuous improvement and adaptability of your software, you’ll need a [data flywheel](https://www.nvidia.com/en-us/glossary/data-flywheel.md). For example, as business requirements change or grow in complexity, performance and cost often become a differentiating factor for success. * [NVIDIA NeMo Curator](https://developer.nvidia.com/nemo-curator) processes enterprise data by removing duplicates and personally identifiable information (PII) while also generating synthetic data for model customization. * [NVIDIA NeMo Customizer](https://developer.nvidia.com/blog/fine-tune-and-align-llms-easily-with-nvidia-nemo-customizer/) customizes the embedding models to improve RAG accuracy. * [NVIDIA NeMo Evaluator](https://developer.nvidia.com/blog/streamline-evaluation-of-llms-for-accuracy-with-nvidia-nemo-evaluator/) measures the performance of RAG applications by evaluating both the retrieval and generation components independently and as an integrated whole. * [NVIDIA NeMo Guardrails](https://blogs.nvidia.com/blog/ai-chatbot-guardrails-nemo/) ensures AI assistants remain accurate, appropriate, secure, and on topic. * [NVIDIA NeMo Retriever](https://developer.nvidia.com/nemo-retriever) enables precise, privacy-preserving information retrieval at scale with multimodal data ingestion and world-class embedding and reranking. Pulling from large volumes of enterprise data, NeMo Retriever interacts with existing relational databases, searches for the most relevant pieces of information, and answers complex business questions in real time. **Integrating Speech AI Capabilities** [NVIDIA® Riva](https://www.nvidia.com/en-us/ai-data-science/products/riva.md) is a set of GPU-accelerated multilingual speech and translation microservices for building fully customizable, real-time conversational AI pipelines. Riva includes automatic speech recognition (ASR), text-to-speech (TTS), and neural machine translation (NMT), enabling organizations to transform their AI applications into engaging and expressive multilingual assistants and avatars with a [speech and translation interface](https://youtu.be/jZ5TQs7NihU?si=cunS_2iBFjBK1eSn). **Get the Best of NVIDIA AI in the Cloud** [NVIDIA DGX™ Cloud](https://www.nvidia.com/en-us/data-center/dgx-cloud.md) is a fully managed AI platform, co-engineered with leading clouds, that includes NVIDIA AI Enterprise and expertise from NVIDIA AI experts to fast-track AI initiatives. Quick Links: [Read Blog: Three Building Blocks for Creating AI Assistants for Customer Service With an NVIDIA AI Blueprint](https://developer.nvidia.com/blog/three-building-blocks-for-creating-ai-virtual-assistants-for-customer-service-with-an-nvidia-nim-agent-blueprint/) [Get Access to the NVIDIA AI Blueprint for Retail Shopping Assistants](https://www.nvidia.com/en-us/ai-data-science/ai-workflows/retail-shopping-advisor.md) [Learn About NVIDIA Clara™ for Digital Healthcare Solutions](https://www.nvidia.com/en-us/clara/digital-health.md) ## How does an AI assistant work? An AI assistant is an intelligent, context-aware software application that’s an evolution of the traditional AI chatbot. It uses generative AI NLP, NLU, and ML technologies to effectively understand, process, and respond to user inputs. By considering past interactions and user behavior, it can personalize support for complex tasks and inquiries that enhance customer experiences, streamline operations, and ultimately address unique business needs. AI assistants can perform a wide range of tasks, answer questions, and facilitate workflows across various domains and data silos, making them an essential tool for modern digital interactions. ## How do I create a realistic voice for my AI assistant? Although speech AI can drive significant improvements to call centers, successfully implementing speech-to-text comes with a few challenges, including: * Phonetic ambiguity * Diverse speaking styles * Noisy environments * Limitations of telephony * Domain-specific vocabulary Enhancing model effectiveness is one way to overcome these challenges. By integrating model training and retrieval techniques, chatbots can deliver a more reliable and responsive experience. ## How do I train an AI assistant? Training an AI assistant involves: 1. Collecting domain-specific data and processing it to remove any PII, duplicates, or toxic or harmful data, and converting it into a suitable format for training the models. 2. Selecting the correct generative AI model and fine-tuning it using various techniques to provide user-friendly responses with techniques such as SFT or RLHF. 3. Enhancing the model response by retrieving the latest data using the RAG technique. 4. Using the user feedback to refine and retrain the assistant by leveraging AI [data flywheels](https://www.nvidia.com/en-us/glossary/data-flywheel.md), self-reinforcing feedback loops where data collected from interactions is used to train and refine AI models, which in turn generate better outcomes and more valuable data. ## How can I shorten development time for customer service chatbot applications? Enterprises can build custom generative AI models for applications in customer support with tools and frameworks from the NVIDIA AI platform. Here are the steps that help reduce development time: * Leverage prebuilt AI frameworks and tools. * Use pretrained models. * Implement a modular architecture. * Leverage open-source libraries and frameworks. * Use cloud-based services. * Collaborate with domain experts. Refer to the “Technical Implementation” section to learn how NVIDIA NIM accelerated inference microservices can help with deploying RAG-powered chatbots for virtual call center agents. Quick Links: [NVIDIA API Catalog](https://build.nvidia.com/explore/discover) [NVIDIA AI for Telecommunications](https://www.nvidia.com/en-us/industries/telecommunications/ai.md) [NVIDIA AI for Financial Services](https://www.nvidia.com/en-us/industries/finance.md) [NVIDIA AI for Retail](https://www.nvidia.com/en-us/industries/retail.md) ## Start Developing AI Assistant Solutions Get started with easy-to-use, NVIDIA-managed serverless APIs, or jump-start development with NVIDIA Blueprints or customizable reference applications, available for free on the NVIDIA API catalog. [Try NIM Microservices](https://build.nvidia.com/explore/discover) [Try the AI Assistant Blueprint](https://build.nvidia.com/nvidia/ai-virtual-assistant-for-customer-service) ## Explore More Resources for AI Assistants Amdocs ### Accelerating Generative AI Performance at Lower Costs For faster generative AI deployment, Amdocs is using NVIDIA NIM inference microservices to deploy custom AI models, open community models, and NVIDIA AI Foundation models. [Read the Blog](https://developer.nvidia.com/blog/amdocs-accelerates-generative-ai-performance-and-lowers-costs-with-nvidia-nim/) ### Navigating Generative AI Opportunities in Finance Hear from financial industry leaders on how generative AI is redefining customer experiences, risk management, and decision-making processes. [Watch On Demand](https://resources.nvidia.com/en-us-financial-services-industry/gtc24-s62592) ### NVIDIA AI Blueprint for Retail Shopping Assistants Develop a [generative AI](https://www.nvidia.com/en-us/ai-data-science/generative-ai.md)-powered shopping assistant that provides interactive customer experiences using RAG and [NVIDIA NIM](https://www.nvidia.com/en-us/ai.md) microservices. [Learn More](https://www.nvidia.com/en-us/ai-data-science/ai-workflows/retail-shopping-advisor.md)