Enterprise Deep Research: The Next Killer App for Enterprise AI — Ofer Mendelevitch, Vectara

Key Concepts

• Victara Agent Operating System: A platform for building and deploying AI agents, offering features for document generation, conversational AI, and enterprise deep research.
• Enterprise Deep Research: An AI agent's in-depth, multi-step investigation of private enterprise data to generate comprehensive reports with citations.
• Multimodal Ingest: The ability of the system to process and understand various data formats, including images and tables, for retrieval.
• Retrieval Accuracy: The precision and relevance of information retrieved by the AI agent from a data source.
• Hybrid Retrieval: A retrieval method combining multiple techniques to enhance accuracy.
• Hallucination Mitigation: Techniques and models designed to detect and correct factual inaccuracies or fabricated information generated by AI.
• Hallucination Detection Model (HHM): Victara's specific model for identifying hallucinations, which has achieved over 5.5 million downloads.
• RAG (Retrieval Augmented Generation): A framework that combines retrieval of information with generative AI to produce more accurate and contextually relevant outputs.
• Generative RAG: A specific implementation of RAG that leverages generative AI capabilities.
• Corpus Understanding: The AI's ability to comprehend the entirety of a data corpus to plan and execute research effectively.
• RFP (Request for Proposal): A document issued by an organization to solicit proposals from potential suppliers for a specific project or service.

Victara's Agent Operating System and Enterprise Deep Research

Victara's Agent Operating System Features

Offer from Victara introduces their trustworthy agent operating system, a platform designed for various AI applications, including document generation, conversational AI (chatbots), and enterprise deep research. The system is available as a SaaS platform and can also be deployed on-premises or within a customer's own VPC (Virtual Private Cloud) or data center.

Key features highlighted include:

• Advanced Multimodal Ingest: Supports the ingestion of images and tables, making them searchable and retrievable for use in RAG or generative RAG workflows.
• Strong Focus on Retrieval Accuracy: Employs hybrid retrieval methods, extensive metadata features, and reranking to ensure high accuracy in information retrieval.
• Hallucination Mitigation: A significant area of focus, encompassing both hallucination detection and correction. Victara's Hallucination Detection Model (HHM) has surpassed 5.5 million downloads, indicating its widespread adoption and effectiveness.
• Enterprise-Grade Deployment: The platform is built to meet enterprise requirements, offering features such as security, role-based access controls, the ability to bring your own model, custom prompts, and comprehensive observability and monitoring tools.

The Problem of Hallucinations in Generative AI

The transcript emphasizes that hallucinations remain a significant challenge in generative AI applications, including enterprise deep research. A statistic is cited indicating that approximately 73% of LLM (Large Language Model) customers implementing use cases identify factual accuracy as their primary challenge. This underscores the importance of Victara's focus on hallucination mitigation to enable high-quality enterprise deep research.

Understanding Deep Research

Deep research is defined as an AI agent conducting an in-depth, multi-step investigation. This typically involves autonomously browsing or searching the web, gathering results, synthesizing them, and generating a comprehensive report with citations. Examples of publicly available deep research tools include Gemini (Google), ChatGPT, Anthropic, and Perplexity. These tools often take 20-30 minutes to complete a research task due to the extensive underlying work.

Enterprise Deep Research: Extending Deep Research to Private Data

Enterprise deep research applies the same principles of multi-agent collaboration, reflection, synthesis, and parallel execution, but directs the investigation towards an organization's private data. This process leverages Victara's generative RAG capabilities, ensuring high accuracy and hallucination mitigation. A crucial component is corpus understanding, which enables the AI to plan its research effectively based on the specific enterprise data it needs to access.

Use Cases for Enterprise Deep Research

Several compelling use cases for enterprise deep research are presented:

• Responding to RFPs: For organizations that frequently need to answer extensive questionnaires in RFPs, enterprise deep research can autonomously sift through internal data to find and synthesize answers to hundreds of questions, significantly reducing manual effort.
• Employee Onboarding: Creating up-to-date and comprehensive onboarding guides for new employees can be challenging due to outdated or scattered documentation. Enterprise deep research can generate on-demand onboarding materials by accessing information from platforms like Jira, Notion, Google Drive, and SharePoint.
• Industry-Specific Applications:
  • Financial Services: Generating investment memos by analyzing relevant internal financial data.
  • Healthcare and Insurance: Similar applications can be envisioned in these sectors for tasks requiring in-depth data analysis and report generation.

Conclusion and Call to Action

The transcript concludes by inviting interested parties to connect with Offer for further discussion and to contact Victara for a demonstration of their platform and enterprise deep research capabilities. The core message is that Victara's agent operating system, with its robust features for retrieval accuracy and hallucination mitigation, is essential for enabling high-quality, enterprise-grade deep research on private data.