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

Channel: aiDotEngineer

Published at: 2025-11-24

YouTube video id: fh9LgKXBGnQ

Source: https://www.youtube.com/watch?v=fh9LgKXBGnQ

Hi, I'm Offer from Victara. At Victara,
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 chat bots, 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 SAS
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 injust to
support images, tables in a way that
makes them, you know, findable and
retrieved to be able to make sense of
them in a in a rag or a gentic rag
workflow. Very strong focus on again
retrieval accuracy with hybrid
retrieval, lots of features around
metadata, reranking, etc. And then we're
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 HHM,
has just passed 5 mill downloads about
couple months ago. I think it's at 5.5
right now or something like that. And
generally our our operating system
platform is what you would need for
enterprisegrade deployment. So 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, an 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 a statistics that shows that about
73% of LM customers implementing use
cases say that factual accuracy is their
top challenge right 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 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. Many have
implemented this sort of web- based deep
research. Gemini or Google has that chat
GPT anthropic perplexity etc. Here's an
example 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 Chat GPT. And this is
usually something that takes about, you
know, 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- aent with
reflection with synthesis of the final
results parallel execution of of agents
underneath and it queries your
enterprise data of course using in this
case victarogentic rank 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 having
being able to use enterprise deep
research to go through all of your
enterprise data sets, picking up the
right documents and answering those
questions is a really cool use case.
Employee on boarding is this idea of if
you come to a new team or you join a new
company and a company wants to onboard
you quickly, it's usually very
difficult. Nobody knows what's going on.
There's no like onboarding guide. The
last one was generated three years ago
and it's not up to date etc. So again
being able to generate a ondemand
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 offering. You can
connect with me here in the links I'm
showing below. And if you are interested
to learn more about Victara and
enterprise deep research, please contact
us and we'll happy to to do a demo for
you. Thanks very much.