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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
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Overview
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Technical Implementation
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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, 41% of organizations use AI-powered copilots for customer service, and 60% have implemented them for IT help desks.
Generative AI applications trained in domain-specific languages and enhanced with retrieval-augmented generation (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.
Supporting human agents with real-time customer communication tools
According to NVIDIA’s 2025 State of AI in Financial Services survey report, 60% of respondents are exploring generative AI and large language models (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, 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.
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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, 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 develop a scalable, customizable enterprise RAG pipeline as the foundation to your application.
Use them as is or combine them with other blueprints for advanced applications, such as digital humans. The digital humans for customer service AI Blueprint 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? 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? 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. For example, as business requirements change or grow in complexity, performance and cost often become a differentiating factor for success.
- NVIDIA NeMo Curator processes enterprise data by removing duplicates and personally identifiable information (PII) while also generating synthetic data for model customization.
- NVIDIA NeMo Customizer customizes the embedding models to improve RAG accuracy.
- 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 ensures AI assistants remain accurate, appropriate, secure, and on topic.
- NVIDIA 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 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.
Get the Best of NVIDIA AI in the Cloud
NVIDIA DGX? Cloud 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.
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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.
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.
Training an AI assistant involves:
- 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.
- 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.
- Enhancing the model response by retrieving the latest data using the RAG technique.
- Using the user feedback to refine and retrain the assistant by leveraging AI data flywheels, 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.
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.
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