Summary NVIDIA has developed an AI sales assistant to streamline sales workflows and address the challenges of managing complex technologies. This tool leverages large language models (LLMs) and retrieval-augmented generation (RAG) technology to provide instant access to proprietary and external data, enhancing sales teams’ efficiency and effectiveness.
Building an AI Sales Assistant
NVIDIA’s Sales Operations team faces the challenge of equipping the sales force with the necessary tools and resources to bring cutting-edge hardware and software to market. This involves managing a diverse array of technologies, a common challenge for many enterprises.
Key Components
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User-Friendly Chat Interface: The AI sales assistant starts with an intuitive, multi-turn chat platform powered by a capable LLM such as Llama 3.1 70B. Enhancements like RAG and web search through the Perplexity API are layered on for advanced functionality without compromising accessibility.
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Optimized Document Ingestion: Extensive preprocessing is implemented, combining rule-based deterministic string processing with LLM-based logic for translation and editing. This approach maximizes the value of retrieved documents, significantly improving performance.
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Wide RAG Integration: Documents retrieved from internal document and media databases and public-facing content available on the company website are used to accommodate diverse workflows and ensure comprehensive information delivery.
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Balancing Latency and Quality: Response speed and relevance are optimized by using strategies like showing early search results during long-running tasks and providing visual feedback on the progress of the answer generation.
Architecture and Workflows
The AI sales assistant’s architecture is designed for scalability, flexibility, and responsiveness, with the following core architectural components:
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LLM-Assisted Document Ingestion Pipeline: The document ingestion process addresses challenges such as documents translation from other languages, PDF parsing, and inconsistent formatting. All text is processed using an LLM, converting it into a standardized Markdown format for ingestion.
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Wide RAG Integration: The AI sales assistant answers user queries by combining search results from vector retrieval on Milvus, web search restricted to NVIDIA website and Perplexity API. These responses often include a dozen or more inline citations, which pose challenges for an LLM when citations include lengthy URLs or detailed authorship information.
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Event-Driven Chat Architecture: Using LlamaIndex Workflows, the AI sales assistant efficiently manages the response generation through event-driven processes. Events capture the local state required for each step, ensuring smooth progression.
Key Benefits
- Unified Access to Information: Combines internal NVIDIA data with broader insights through the Perplexity API and web search.
- Enterprise-Grade Chat: Handles diverse queries like spell-checking, summarization, coding, and analysis with models like Llama-3.1-405B-instruct.
- Streamlined CRM Integration: Automates SQL query generation and enhances reporting by summarizing sales data directly within customer relationship management (CRM) systems using a Text2SQL approach.
Challenges and Trade-Offs
Developing the AI sales assistant involved several challenges, such as balancing latency with relevance, maintaining data recency, and managing integration complexity. NVIDIA addressed these by setting strict time limits for data retrieval and employing UI elements to keep users informed during response generation.
Future Improvements
NVIDIA plans to refine strategies for real-time data updates, expand integrations with new systems, and enhance data security. Future improvements will also focus on advanced personalization features to better tailor solutions to individual user needs.
Table: Key Components and Benefits
| Component | Description | Benefits |
|---|---|---|
| User-Friendly Chat Interface | Intuitive, multi-turn chat platform powered by LLM | Enhances accessibility and user experience |
| Optimized Document Ingestion | Extensive preprocessing for translation and editing | Maximizes the value of retrieved documents |
| Wide RAG Integration | Combines internal and external data sources | Ensures comprehensive information delivery |
| Balancing Latency and Quality | Strategies for optimizing response speed and relevance | Improves user experience and efficiency |
| LLM-Assisted Document Ingestion Pipeline | Standardizes document formatting for ingestion | Enhances document clarity and usability |
| Event-Driven Chat Architecture | Efficiently manages response generation through events | Ensures smooth progression and error handling |
Table: Key Benefits and Future Improvements
| Benefit | Description | Future Improvements |
|---|---|---|
| Unified Access to Information | Combines internal and external data | Refine strategies for real-time data updates |
| Enterprise-Grade Chat | Handles diverse queries | Expand integrations with new systems |
| Streamlined CRM Integration | Automates SQL query generation and enhances reporting | Enhance data security and personalization features |
Table: Challenges and Trade-Offs
| Challenge | Description | Solution |
|---|---|---|
| Balancing Latency and Relevance | Managing response speed and accuracy | Set strict time limits for data retrieval and use UI elements |
| Maintaining Data Recency | Ensuring up-to-date information | Perform daily updates and implement real-time connections |
| Managing Integration Complexity | Handling diverse data formats | Use NVIDIA Multimodal PDF Ingestion and Riva Automatic Speech Recognition |
Conclusion
NVIDIA’s AI sales assistant demonstrates the potential of AI in streamlining sales workflows and enhancing sales teams’ efficiency. By leveraging LLMs and RAG technology, the tool provides instant access to proprietary and external data, addressing the challenges of managing complex technologies. The development process highlights key learnings such as starting with a user-friendly chat interface, optimizing document ingestion, and balancing latency and quality. Future improvements aim to further refine and expand the capabilities of the AI sales assistant.