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.