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

Hi, I'm Ofer from Vectara. At Vectara, we developed a trustworthy agent operating system, and there's a lot of really cool use cases with this, like document generation, conversational AI or chatbots (either internal or external), and Enterprise Deep Research, which I'm going to talk about today.

But before I jump into Enterprise Deep Research, let me tell you a little bit more about our operating system for agents. First of all, it's a SaaS platform, but also runs on your own VPC or on-premise in your own data center.

And here's some of the main features that we really are proud of: we have very advanced multimodal ingest to support images, tables, in a way that makes them findable and retrieved, to be able to make sense of them in a RAG or agentic RAG workflow.

Very strong focus on, again, retrieval accuracy with hybrid retrieval, lots of features around metadata, re-ranking, etc. And then we're kind of known for a lot of work around hallucination mitigation, both hallucination detection and correction. In fact, our hallucination detection model, also called HHEM, has just passed 5 million downloads about a couple months ago.

I think it's at 5.5right now, or something like that. And generally, our operating system platform is what you would need for enterprise-grade deployments: security, role-based access controls, bring-your-own model, custom prompts, observability, monitoring—everything you would need. So why does this matter?

Well, in any generative AI application, and Enterprise Deep Research is no different. Hallucinations are still a problem, and you want to base your applications on really robust information. In fact, this is statistics that shows that about 73% of LLM customers implementing use cases say that factual accuracy is their top challengeright now.

So that's why we spend so much time in hallucination mitigation, which enables Enterprise Deep Research at really high quality. Okay, so with that in mind, let me jump into what is Deep Research. Deep Research itself is something many of you probably know already.

It's when an AI agent conducts in-depth, 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.

Many have implemented this sort of web-based deep research. Gemini or Google has that, ChatGPT, Anthropic, Perplexity, etc. Here's an example of what it looks like. If you haven't used it, I highly encourage you to use it. It's a very powerful tool, and I use this all the time.

This is the screenshot of how you choose it in Gemini, for example, and here's how you choose it in ChatGPT. And this is usually something that takes about 20, 30 minutes to complete, because it does a lot of work underneath the covers.

Now, Enterprise Deep Research: think about it as exactly the same idea, only now it goes to your private data. So again, the same process: multi-agent with reflection, with synthesis of the final results, parallel execution of agents underneath. And it queries your enterprise data, of course, using, in this case, Vectara agentic RAG capabilities, with all the bells and whistles of high accuracy and hallucination mitigation.

And then we have corpus understanding, which allows you to plan properly based on your data.

There are many really amazing use cases for this, and I'm going to just mention a few here that I like. One is responding to an RFP. I've had this in my career multiple times: when you have to respond to an RFP, and getting the answers to 150 questions is really difficult.

So being able to use Enterprise Deep Research to go through all of your enterprise datasets, picking up theright documents and answering those questions is a really cool use case. Employee onboarding is this idea of: if you come to a new team, or you join a new company, and the company wants to onboard you quickly, it's usually very difficult.

Nobody knows what's going on, there's no onboarding guide, the last one was generated 3 years ago and it's not up to date, etc. So again, being able to generate an on-demand onboarding guide using all the documentation we have on Jira, or on Notion, or Google Drive, or SharePoint—very, very powerful.

And then, in different industries, there's also different use cases. For example, in financial services, you might have the generation of an investment memo as a really cool use case. And you can imagine the same thing in healthcare, in insurance, and other industries as well.

So those are some of the use cases. There's a lot more. I'm Ofer, you can connect with me here in the links I'm showing below. And if you are interested to learn more about Vectara and Enterprise Deep Research, please contact us, and we'll be happy to do a demo for you.

Thanks very much.