Enterprise Deep Research applies web-style, multi-step investigation to a company's private data.
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Retrieval accuracy, hallucination detection, and corpus understanding help agents produce grounded reports and answers.
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RFP responses, employee onboarding guides, and investment memos are examples of document-heavy workflows that can benefit from this approach.
Summary
Ofer Mendelevitch presents Enterprise Deep Research as a private-data version of the research tools available in Gemini, ChatGPT, Anthropic, and Perplexity. An agent searches enterprise data through multiple steps, reflects on results, runs work in parallel, synthesizes an answer, and provides citations. The system depends on accurate retrieval, metadata, reranking, multimodal indexing, and hallucination detection and correction. Mendelevitch also describes corpus understanding, which helps the system plan its investigation around the available company data. He gives examples from different workflows: answering the many questions in an RFP, creating an up-to-date onboarding guide from Jira, Notion, Google Drive, or SharePoint, and drafting investment memos in financial services. The talk is brief and product-oriented, but it makes a clear case that enterprise research requires more than a chatbot. The hard part is finding and trusting the right internal documents.
Vectara's agent platform is designed for enterprise deployment
Mendelevitch begins with Vectara's agent operating system, which is available as a SaaS platform and can also run in a customer's VPC or on-premise data center. He names multimodal ingestion for images and tables, hybrid retrieval, metadata features, reranking, custom prompts, bring-your-own-model support, observability, monitoring, and role-based access control. These capabilities support applications such as document generation, conversational AI, and Enterprise Deep Research. The focus is on the infrastructure needed to use agents with enterprise data and security requirements.
Grounded answers depend on retrieval and hallucination controls
The platform combines retrieval features with hallucination detection and correction. Mendelevitch says Vectara's hallucination detection model, HHM, had passed 5 million downloads a couple of months earlier and was around 5.5 million at the time of the talk. He connects this work to the problem of factual accuracy in generative AI applications. He cites a statistic that about 73% of large language model customers implementing use cases identify factual accuracy as their top challenge. Enterprise research therefore needs more than an agent that can search. It needs information that can be retrieved and checked.
Deep research uses an agent to investigate a question in several steps
Mendelevitch defines deep research as an in-depth, multi-step investigation carried out by an AI agent. The agent usually browses or searches, gathers results, synthesizes them, and produces a comprehensive report with citations. He points to versions available in Gemini, ChatGPT, Anthropic, and Perplexity. These tools can take 20 or 30 minutes because they perform substantial work behind the scenes. The output is meant to answer a particular question with supporting information rather than produce an immediate conversational reply.
Enterprise Deep Research moves the same workflow onto private company data
Enterprise Deep Research follows the same general process as web-based deep research, but it queries a company's private data. Mendelevitch describes a multi-agent workflow with reflection, parallel execution, and synthesis of the final results. The system searches enterprise data through Vectara's agentic RAG capabilities, using retrieval and hallucination-mitigation features. Corpus understanding helps the system plan its investigation based on the company's actual data. This changes the source of research from the open web to internal documents and knowledge bases.
RFP responses can be assembled from an enterprise's document collection
One example is responding to a request for proposal. Mendelevitch describes the practical difficulty of answering 150 questions in an RFP and finding the right information for each one. Enterprise Deep Research can search across enterprise data sets, select relevant documents, and use them to answer the questions. The value comes from connecting a large set of requested answers to scattered internal material. The workflow is document-heavy and repetitive, which makes it a suitable target for a multi-step research agent.
Onboarding guides can be generated from changing internal documentation
Mendelevitch also describes employee onboarding. New employees often have trouble understanding a team or company because there is no current onboarding guide, or the existing guide was written years earlier. An agent could generate an onboarding guide on demand from documentation in Jira, Notion, Google Drive, or SharePoint. The guide would draw on the material already held by the company instead of relying on a static document that may no longer match how the team works.
Industry workflows can turn internal research into specific business documents
A financial services example is generating an investment memo from enterprise information. Mendelevitch says similar ideas apply in healthcare, insurance, and other industries. The common pattern is a high-value document that requires collecting and combining information from many internal sources. Enterprise Deep Research provides a way to make that investigation more systematic, with the agent searching the relevant corpus and synthesizing the result.
"It's when an AI agent conducts indepth multi-step investigation, usually by autonomously browsing or searching the web in some way, getting results, synthesizing all these results together to generate a comprehensive report for you with citations and all the information you need to answer a particular question."02:19
Who should watch
You are building an enterprise agent that must search private documents and produce a cited report.
Your team spends substantial time answering RFPs or creating internal documents from scattered company knowledge.
You want to understand which retrieval, hallucination-control, and planning components sit behind an enterprise research workflow.
