Introduction

Good evening, everyone.

Speaker Background

Just to start with, let me introduce myself.

My name is Kapil Bhaumik.

I used to work for FinTech and financial services for many years, recently worked for JPMorgan Chase.

From FinTech to AI: The Genesis of FinSight Hub

and for last few years I was kind of exploring how to build something into

FinTech space okay and with that you know beginning of this year I just

exploring about something to build in AI based FinTech product and there's the

way I came up with the product called FinSight hub so before starting there I

Audience Check-in

I want to ask you a few questions.

How many of you are investor here?

Yeah, looks like 50%.

Any financial advisor, anybody?

No.

Problem Overview

So what is actually the product is about?

Pain Points Across Participants

I spoke to a few financial advisor,

and I'm sure if you are connected to LinkedIn, right,

many times you are getting a request

from different financial advisor that can I reach you do you need any kind of

financial services you know services so that I can or advise we can help you and

they try to go through LinkedIn network to identify potential investor based on

their profile in LinkedIn so that thing is that as I spoke to few financial

advisor their strike rate is less than 3 % or less than 2 % to identify potential

investor okay and is a really pain point for them now for the investor when I was

talking about the investor like me as well it was always kind of how to do my

you know own research efficiently so that rather than going to the engaging

with the advisor I can do my own financial research some AI based

research platform so that it will save my time and I can able to do more

efficient way of investing decision in additionally also if there is a

opportunity I can find out a financial advisor rather than going through a 10

12 13 different platforms I want a one place where I can do my own research and

everything and if i need help i can get engaged to a financial advisor so that is the pain point

of investor has and the third one is a financial analyst financial analyst what they do suppose

they build a kind of a kind of a portfolio for the based on their customer requirement that

that portfolio is the right choice based on on their high growth potential low growth potential

or the customer needs they can build the portfolio for on the customers and for

that they need a platform where they can do a lot of research and everything

based on the lot of news articles even geopolitical issues how they can get a

research platform so that they can build a and our financial portfolio for their

customer it can share with their clients so there are the three participant if

If you see everybody has a pain point, how to get a better platform where I can efficiently

do my own research and able to potentially increase my customer base as well as I can

able to potentially be financially strong enough to increase my wealth.

What FinSight Hub Aims to Solve

So the Financial Insight Hub is solving the problem of how we can able to do a real -time portfolio tracking, how we can do AI -powered research and recommendation.

And recommendation in the sense, suppose based on your portfolio, AI will automatically take your portfolio.

suppose a portfolio in the sense if if some of you are new to the financial

world everybody has a 401k right or everybody has a kind of a trading

account or do you have some kind of investment if a kids got 529 plan or if

you have a kids who whom you created a child investment account so all this

come your investment your 401k everything come under a portfolio so

So based on your portfolio, it is like that we need to reach out the one -to -one in -person collaboration with advisor and it is a time consuming, it takes time and we do not get the right insight.

So with the AI based recommendation based on your portfolio analysis, AI will give you kind of a recommendation what will be the right investment choice.

choice also it will what it will help other than the AI based research it will

help you to engage the you know investor analyst and advisor in a common

collaboration platform kind of a networking so at a high level this

platform is going to help to a building a collaboration relationship between

different participant in a kind of a networking line of lending network in

the financial sector space as well as it will provide you kind of a research tool

which through the help of AI so that all the participant can able to get the

best outcome of it so that's about the inside of the product about it so for

Demo Plan and Mission

this today's demo I'll go through couple of slide then I'll show you a kind of a

demo product the product is not built yet it is still in fancy but kind of a whole the design

prototype is kind of ready i am going giving you a work through and then then we'll go for a question

and answer sessions any questions so far great okay so this is if you see our introduction slide

Mission and Core Pillars

uh what is our mission as i uh mentioned you we are building a financial inside half called finsa

adapt to unify investor, analyst, advisor as single AI -driven insight in real -time collaboration

platform, which is transformed the all kind of fragmented information into a smart investment

outcome.

So that is our kind of a main goal of the product.

And in the right side, you can see already what I have explained real -time portfolio

tracking, AI -powered research recommendation, and secure collaboration.

Secure collaboration in the sense it is like that investor and advisor or analyst they can

able to work real time in a chat bot where they can able to type okay I need some help on this

kind of a research on these three stocks or three bonds can you help me out so it will be

automatically will be you know sent to your advisor who can real -time

connected to you and provide you a real -time feedback as well so that's the

kind of a secure collaboration is going to happen and as I told you secure in

the sense it is completely kind of a following the kind of financial services

guidelines and compliance and into a secure environment so yeah yeah yeah so

How AI-Powered Research Works

AI power research in the sense suppose if you have a portfolio right we have a

401k we have a trading account okay or investment account you have a kind of a

529 plan those are all portfolios right and what we'll do once we upload all

your data into the platform the ai will automatically detect your portfolio okay

and it will automatically identify based on your portfolio and historical analysis and snapshot

it will provide a recommendation automatically by you know uh uh uh going through all your

portfolio documents and give you a real -time insight that okay i think uh you are more kind

of a high growth investor where you are and with the high risk so that we can able to provide you

a kind of a portfolio which will be the best you know portfolio for you to invest further

for example if you are invested in call S &P 500 index fund it is little bit less risky

moderate growth but if your goal is that you are a high risk investor you want high growth

okay rather than investing into the S &P 500 fund I think those are the five

stock or call magnificent seventh or nowadays they call it Netflix you know

Nvidia Amazon Google all those companies those will be the right portfolio for

you okay to a diverse your investment so that's the way AIU is going to help

you automatically and give you a research outcome based on your portfolio

your analysis so for for that what we are trying to do is that all the model

will be trained by kind of synthetic data okay

synthetic data will be trained and whenever the trained model is ready then

we do real data where we can able to processes but all kind of for example

PII data and everything will be controlled there will be a guardrail so

that all kind of a compliance related data will not be sent to your AI model

yeah yeah and what is kind of a publicly available data along with the synthetic

data will be gathered and trained those model yeah yes a synthetic data is not

proven sources there that's why I'm telling you the publicly available data

for example if you know the SEC SEC SEC has all publicly available data lot of

kind of for especially company related data everything it is available so we

we can get those data to train our model.

Even from the Yahoo Finance, you can

be able to get all kinds of financial data

for different companies, kind of a stock, bonds, and everything,

and try to train our model as well.

No, SEC is kind of a publicly available data.

It is once you have an account with the SEC,

you can be able to access their data in your platform.

That's the way most of the funds developed yet, yeah.

Okay.

It is, as I told you, we are, I'm working with, yeah, this is the goal, yeah.

Market Problem and Proposed Solution

So now if you, as I'm already explaining you the problem statement, the financial investor

maker system is fundamentally fragmented, forcing investor, analyst, advisor to operate

in a disconnected silos, they are disconnected, okay.

And there are a lot of platforms are there, I spoke to already, to product market feed,

we talk to financial analyst, investor and advisor,

they say they have to go through the 10 different system

to get connected to each other, okay?

And that is kind of very, you know, time consuming task

and not everything is kind of integrated together.

So that's the why investor wants to, you know,

write opportunity for investment where they get the tool,

where they can do their own research

as well as they can get connected.

financial analyst face difficulty accessing and synthesizing valuable

insight so they want to spend less time with use of the automated AI based

research same with the financial advisor they want to provide yeah you know

connected with the more customers through this platform as well as they

can able to do their own research as well now as I told you what is the

Unified Insights and Collaboration

proposed solution as explained you already so what actually insight it is going to provide

it is a single source of truth what does it mean that suppose if i am as an investor

got some data and found that based on my portfolio this is the right investment it will be

automatically shared with my connected advisor or connected analyst as i told you when through the

network connection if you are connected to them it will be automatically shared with them so that

all three of us will get our one you know source of data it will eliminate any kind of a confusion

between you know all the participants uh from monologue to dialogue means rather than i am

able to do my own work and everything uh it will be more the dialogue base more about the

conversational with my advisor and me uh through the ai chatbot the real -time collaboration so it

will going to help on that it will help on the contextual conversation uh so contextual

conversation this is this is my area i am focusing on this only thing uh for example i want to invest

in you know some kind of a high growth bond uh can you help me on that so it will be only on that

context discussion will be done not nothing like then if you're asking for a

bond I don't want to invest on a stock so those kind of contextual conversion

will be there and seamless expertise means simply expertise in the sense

suppose based on my you know goal as an investor I will get connected to the

right advisor so that they can able to support my goal so I will show you

through a demo how the investor goal is what is the advisor goal is so with the seamless expertise

Key Features

it is going to help us on that some of the key features initially i am building or my team is

building is user onboarding experience ai power intelligent insights and research and financial

treatment analysis participant engagement networking that is very important because

because value addition to the AI -powered research,

because they don't want to,

advisor doesn't want to go through the LinkedIn

to identify the potential investor.

An investor does not want a platform

where LinkedIn is kind of a more about

the carrier -based network.

They want a specific platform

where it is only for the financial services.

So, and that, and in a platform,

we help them to get connected with each other because they have their everybody

has their own goal financial services and how we can grow our money and wealth

and the third thing is that automated alert communication data storage so if

as an advisor or an analyst I am doing some research for my customer investor

make sure that once my research is complete I upload it into the system it

is automatically alerted to the investor your research document is ready do you

want to communicate with us or have a collaboration for based on my research so all kind of a alert

communication and data storage and all the data whenever advisor is performing a research document

or analysis performing is a research document or investor got some kind of recommendation

all this document will be exported and stored into within the platform is a cloud -based storage so

so that you can be able to go through your all kind of historical analysis as well.

Key Capabilities and Expected Impact

What are the key capabilities?

Some of the key capabilities is connected to the features,

AI power, financial parsing,

so automates the analysis of financial reports, filings and market data,

intelligence insights.

With the help of generative AI, agentic AI,

we can be able to identify the financial trend,

brand, company performance, investment risk, all those kind of insight you are going to

get.

Real time collaboration, it will give you a collaboration chat along with video chat

as well so that you can able to connect investor and advisor together and they can work on

a specific dashboard together on that platform as well.

Unified search, all kind of a search will be supported by NLP,

you know, across filing, KPI, portfolio, or there could be,

there will be one option, ask FinCite anything.

So whatever question you have, it will be able to search together.

And the personalized recommendation, as I told you,

based on whatever the expectation of the three participants,

you will get the specific recommendation from this platform.

some of the quantifiable impact what we are going to get out of it so it will reduce the

manual analysis time by 60 to 80 percent with the AI based automation improve investment decision

accuracy it will almost 30 to 40 percent through AI based anomaly detection and trend prediction

it can able to do it boost productivity of participants so think about the

investor analysis investor advisor and analyst they spend a lot of time I'm

telling you I was working for Moody's investor service where used to do a

financial rating for a specific you know securities either stock or bond for

rating a one stock or bond it takes almost seven days to rate by the

financial analyst but with the ai base they can do from seven days within a minutes on an hour

they can able to rate it together so that kind of a productivity improvement will happen uh

it will help the client engagement retention and trust uh by you know 50 through interactive

dashboard and real -time collaboration and has networking and it will reduce analytic and

compliance cost because any kind of a fintech platform it requires lot of you know compliance

and regulation so the platform will be completely taken care all kind of a compliance and regulation

of the financial industry so that all the customer data and how they are communicating

between all participants will be safe and secure and compliance so that any kind of a issue with

with their data will be safe and secure.

Market Landscape and Opportunity

So with that, let me, I think, how much time I have?

Target Markets and Segments

Okay, I think, so if you see, what is the US market?

The initially, we are focusing to build a product for US market, but

in the future, the product will be internationalized as well,

in the international market.

But if you see the US market, what is the kind of a demographic or

population in the different categories so there is a two kind of investor one

is a retail investor like us another is called institutional investor kind of a

hedge fund asset manager a private equity VC venture capitalist so those

are the different kind of institutional investor they also try to get the best

investment outcome for their customers okay so if you see a retail investor is

is 160 million individuals in U .S.

and 18 ,000 to 20 ,000 firms are there in U .S.

So combined it is almost kind of 50 % of U .S.

population, means little bit less than 50%,

almost 45 % of U .S.

population are engaged in real -time investment to get the best outcome

of their, you know, portfolios.

so and if you see the financial advisor was there is a 330 ,000 to 350 ,000 advisor are available so

I'm not sure you are already get engaged with the advisor or not but my for one cue is with

the fidelity and I my fidelity advisor always trying to reach me that how we can provide you

a better advice how we can manage your funds and everything so those are the kind of a financial

financial advisor and every financial companies have their advisor or wealth management come.

I work for a potential sometimes, so potential has a big advisor platform as well.

So those advisor platforms are kind of connected through a specific network, but their pain

point is that there is not a single platform where they can get connected to more investor.

and as a financial analyst if you see is a 200 to 220 thousand professionals are there which is

able to you know provide financial services through this platform to so many investor as

well as financial advisor I am NOT going through much detail about it but this is a kind of a chart

from there you can see that with this platform there is a big opportunity to

almost 50 % US population to bring them into a platform where they can get the

best AI based financial advice and outcome of it.

This is kind of a very

high level with this what is the market opportunity I'm not going much details

but at this current moment within five to seven years we have opportunity of

of $12 billion to market share with this platform where

this kind of early fintech adopters or large investor

communities willing to get advice or services

or a platform through which they get the best

financial services or outcome.

But if you see at a higher level,

total addressable market is almost $400 billion.

And where the new fintech firms are coming,

so it is kind of gradually expanding further with that I will go you can see

Competitive Landscape and Differentiation

the competitive advantage I just did some research with the peach books

Weichert and Bloomberg how this platform is going to you know what they are

actually doing how our platform is going to a differentiation especially the

real -time collaboration AI intervention and multi -user engagement as a social networking

within the financial space so this is the key delivery or kind of a you can say best services

which is not available on those competitors so this new services will definitely going to help

the investor community as well as advisor there are some more details about the you know

competitive analysis with the some online platform betterment wealth from raven hood

and compared to all of this if you see that our fins site is able to differentiate us in multiple

multiple areas.

Audience, Offerings, and Scope

The primary audience, we are looking both B2C and B2B, but other platform

is somewhere they are between B2C, either B2C or B2B focus, but our platform is focus

in both areas.

Our core offering, if you see the ERP or financial insight, analysis and

shared dashboards, AI and automation, collaboration tool, scope of data, client engagement, so

So those are the all kind of a feature if you compare with the existing platform.

So our platform is going to provide additional services which will empower our investor, advisor, and analyst community.

So with that, any question before I show you the demo?

Technology Strategy

Initial LLM Approach and Future Roadmap

Yeah, for our language model, initially we are starting with the kind of a multi -model

LLM with OpenAI and Cloud we are starting with.

It will be a multi -model training model.

But in the future, once our product will go bigger and bigger, more the enterprise, then

our objective is use the publicly available LLM, like as Lama, Facebook Lama.

we have to kind of a download in our cloud platform and we have to install

and manage by ourselves and train this model to for their specific platform so

that all kind of a data and everything rather than sharing to that individual

you know LLM where they are holding our data based on our client requirement

especially the institution client in their own cloud environment we can train

the LAMA model and it could be, you know, help service their own clients, rather than

data goes out of their ecosystem, it will be within their ecosystem for the institutional

client.

Did I answer your question?

Deployment Models: SaaS and Enterprise

And there will be two kinds of offering, one is called SaaS offering, SaaS offering where

retail customer like us will get the platform and another will be kind of a on -prem or cloud

offering in the specific customer on location like as for example JPMorgan or

the big financial services where they want that know our customer data within

our own platform we don't want to get it out of it so in their own platform

itself will deploy the LLM and will kind of use that whatever deployable LLM in

their environment and that LLM will be used to for our AI research so that that

That is the objective.

Any other question before I go?

System Architecture Overview

So on top of LLM, so we have a kind of an entire architecture

we are following with the LLM.

There will be an API -based layer will be there.

In the API and behind the API -based layer,

there will be a language -based language communication

with our LLM, with the length chain libraries.

libraries, it will be able to expose all kind of data or communication through our API layer.

And our frontend application will be connected to the API layer and it will be able to communicate

with our backend LLM.

Proprietary in the sense, suppose for our LLM, right, we'll train the LLM with the specific

kind of a our model so that model will not be existing model we are going to use we are taking

the existing model then retrain the model based on their own is so that it understands our NLP

based you know training rather than using the kind of a open AI or what is publicly available

Product Walkthrough

so with that let me go by the way this is digital is my company but with this we are

building this product so this is our Finsight product so if you see if once

Onboarding: Roles and Goals

you go into the Finsight product you will see you have to kind of register

yourselves and once you register then I'm just putting some dummy data so now

you as kind of a signing in so once you signing in you'll see welcome to

financial what is your kind of a role you want to play are you investor or

or an analyst or an advisor, okay?

Suppose I am starting with the investor, okay?

With the investor, then I put my kind of basic information.

Okay, then it is kind of a giving,

kind of a onboarding experience

that what exactly your investment goal is.

Suppose I want to income generation and long -term growth,

okay, and I am a kind of a, for example,

aggressive maximize my return,

as I told you in the beginning.

and once we do that then what is your kind of a portfolio size means total portfolio suppose i

have 250 to 1 million it supports different sizes 52 even 5 million over 5 million as well and my

experience is intermediate for example and my focus is technology and finance once we do that

then we can kind of upload the our whole portfolio but in the future the our objective is that

that initially we starting that you whatever portfolio you have the csv file you upload

into our platform but in the future we will based on your social security and everything

right account information will read directly integrated with all find the financial system

you are getting connected okay so you need not to upload it by yourself so that kind

of a pain point we are going to go in the in the future once we integrated with those

kind of a you know your actually a financial platform so once you upload it then just i'm

Investor Experience: Portfolio and AI Assistant

skipping for a step so then welcome about now we'll see the actual experience begin so based

on the whatever the portfolio whether you uploaded or we are able to fetch from your

all kind of a financial institution where your account is there you will see you are getting a

summary of your portfolio overview and what kind of account type you have what

is your recent activity how your sector investment sector is there and how many

accounts you have for example I have a three taxable account okay I have a

health account as well HSA account so those are all kind of a different

accounts based on your portfolio and everything you will able to get display

out of it.

Now the most important part what I was talking about the AI assistance.

In

the AI assistance if you see this kind of AI based chat bot will able to provide you

kind of a real time based on your recent market trends I recommend you rebalancing tech sector

currently 35 percent suggest 30 percent.

So AI will automatically see this is kind of

AI assistance.

AI is automatically giving you a recommendation based on your actual

portfolio how you are kind of a you are investing as well as what is your

onboarding goals based on your existing portfolio and the onboarding goals

AI assistance will automatically analyze and give you the best recommendation

additionally you can get connected to kind of a your own financial advisor for

For example, Michael Torres, I already get connected to my financial advisor.

And he is able to give me some advice through kind of AI chatbot as well.

The text recommendation aligns well with your financial goals.

So whatever, let's schedule a meeting to discuss the implementation.

So that kind of a team collaboration also can happen with whom you get connected.

Now, if you see there is a, you will get the whole financial insight from their portfolio

rebalancing opportunity.

If you see it is a green, we are 92 % confident.

If you see a market volatility alert, it is 87%, so if it is less than 90%, it is kind

of a little bit risky, so you have to make sure you look into it.

it.

You can able to all AI suggested task you have to work on it.

So, the review, rebalancing

proposal, whatever AI has provided.

So, all those task are provided by the AI.

And then

if you see the analytic wise, you see how you are engaging with your team, what kind

of AI accuracy you have, how much time it save for you with the AI.

with the document research suppose if you want to get specific research based on whatever some

document you have you can upload those document and based on uploaded documents also you can

able to do your own research so that platform will provide that additionally if you see you

Networking and Collaboration Rooms

you are able to network with your advisor or kind of a analyst, and if you see in that

in your network what they are is an advisor, Sarah is an analyst, those with whom you get

connected, okay.

So, some of the connection you already sent, it is pending, so you can

can see a new analyst you get connected.

So you can see all the connections

is kind of recommended by our AI -based recommendation engine.

And then if you see, if you want to create a collaboration

room with few participants, you can create a new room.

So you can put a room name where you can able to.

It is nothing but in LinkedIn, you create a group, right?

Within that group, you invite a lot of participants.

participants.

So in this platform we call it a collaboration room where you can create

a collaboration room or a group where you can invite the right people and we can have

a conversational chat between all the participants.

Requests: Research and Advisory

And some of the suppose as an investor if

you want to do a research request, you can able to service a request, you can create

create a new request whether you want a research request for analyst or advising request from

the advisor.

So, we can fill out all the details and then you can submit a request and once

you submit it, it will go to the specific you know financial advisor queue with whom

you are get connected.

So, you have to select all those.

So, that automatically with this

platform we can create either a research request or a advising request with your connected

connected analyst or connected advisor.

These are the some profile details you can

update.

Advisor Experience and Onboarding

So, this is about the investor side, but if you see I am showing you the advisor

profile.

So, advisor profile you can see a kind of a different kind of a view.

What kind

of a research is available to me and how many unread, what is the research item is there,

how many requests I created okay and how much is completed how much is active so

those kind of information you will have so we have a different experience based

on the role advisor analyst or investor and also just I want to show you the

onboarding experience for the financial advisor if you see it is a little bit

different kind of a data where we are trying to put so if you see what is your advisor what is

your certification your cfa certified or all those what is your practice specialization tax planning

estate planning and all those uh what is your how much advisor experience you have so you have to

put all those information through the onboarding experience and you put the what kind of a client

types you are looking for high net individual or mass effluent or retirees you can able

to put that so based on your onboarding right you are able to you know kind of a get recommendation

matching with this potential investor based on your onboarding so once you onboarded then

you will get all kind of for example I am managing as an advisor I am managing the Kirkland

client portfolio so I can see how much you know asset under management total AUM I am

managing 4 .78 million dollar what is how many total clients I have I have five total clients

average portfolio return I this so as an advisor you can see all kind of information and all

kind of details for your potential clients so you can see the all the client information

as well based on the total asset under management so what is the report

portfolio video what is their year -to -date return so if you see for

the advisor right now it is very difficult for them get this kind of a

view so with this they can able to get this kind of view as well in

additionally they can use use the AI assistant do any kind of research based

on the investor you know what they are asking for and additionally they can

able to network with those people as well.

So these are though this is for investor experience

Analyst Experience and Workflows

then we have a financial analyst have a different experience for example.

So I'll not go much I'll quickly finish it and probably then we'll go for a

you know question and answer session.

So if you see there is a different kind of a

a onboarding experience for the financial analyst and then suppose what kind of fee

structure because when the investor gets connected with the advisor, advisor has to charge some

fee and make sure that their fee structure is well defined and when the suppose investor

with the air recommendation we find a five advisor and they have a different

kind of a fee structure accordingly at of a investor can choose I want to get

advice from this advisor because he's or her you know profile looks whatever

exactly I am looking for and their fee structure and everything automatically I

can choose my own advisor if needed so the same thing analyst can see their

client portfolio below views and if you see analyst can have a client investor and advisor

because analyst advisor does not have an in -depth knowledge of how the portfolio has been created

analyzed have more knowledge so advisor can able to get connected to analyst and become

their client so that analyst can help the advisor so that's the way you know we get different views

views or details for the analyzed experience.

Q&A Highlights

So with that, my kind of presentation is kind

of done, all the contents, but any further question you have?

Yes.

So if you see that,

I showed you a section, right?

Let me go to the investor view, okay?

Suppose AI assistant.

AI Assistant for Research and Templates

in the AI assistant you can ask the AI assistant right any kind of a question

okay I want to research the kind of a stock of Nvidia and kind of and compare

them with kind of a for example AMD or some similar kind of a stock can you

able to guide me is there any historical news or recent news which is going to

influence kind of a the stock price and everything with the ai assistant you can easily do it okay

as the as the ai and it can able to analyze and whatever the format you want they can give it

and you can save it in your uh specific folder so that you can in the future you can do it

yes it is possible another thing is that suppose if you are able to get download of any financial

statement of Nvidia or any kind of a company right you can upload those

document as well and do a specific research on those document as well so

that is possible ah it will be little bit kind of a different because when we

are trying kind of training the model right all kind of a guardrail guardrails

and everything will be put by us chat GPT put a generic guardrail and

everything based on the customer but for the specific customer need we can able to put the

specific guardrails as well okay so then we can able to manipulate the output as the customer

needs uh means i don't have a kind of a view right now but the in the sense for example uh

i want uh kind of a i'm doing uh okay nvidia stock is going to because everybody i think

invested in NVIDIA, I think it's very popular.

So I'm talking NVIDIA, but I can go to any other

stock.

So NVIDIA stock, how it was performing for last five years, okay?

And what is the kind

of recent news and geopolitical, you know, influence on the NVIDIA stock in the future,

right?

So for that, I want a kind of a specific output, comparison output with the similar kind

of a stock right and the output will be look like specific kind of a format okay for example either

tabular format or a kind of a csb format or kind of a more specific research report or guidelines

for a specific company's needs we can able to put the template and with this template we can able to

to, you know, get the output based on whatever, you know, research you want to do it.

With the existing chat GPT, we do not have those kind of opportunity right now.

For example,

if you go to an institutional client like JP Morgan, they have a specific way, we call

call it electronic publishing documents, EPI documents, how we are able to show the customer

data or research and publish into the specific other companies or to the customer.

So we can upload those kind of a template into this platform and the output will be

automatically customized based on the template as well.

So those kind of things we are going to introduce with this platform.

So it will be a little bit different with a lot of guide and the template based customization.

Institutional Workflows and Data Ingestion

any other questions okay yes so if you see and we call a institutional client those are called

large asset manager and everything so what large asset manager is does that have a lot of client

information everything so this platform they can easily use it and normally most of the asset

manager there is a generic workflow how they are kind of a doing to analyze it okay so our initial

The initial goal is that what is the common workflow followed by the most of the asset

manager, that workflow will be inbuilt.

But in the future, if any kind of asset manager want to build their own workflow, we can able

to integrate our platform with some kind of a call, I forgot the replete or some kind

of other platform, they create their own workflow as well, and the data from our platform can

can be pushed there, that workflow as well.

Those integrated in the case of a future need.

But as currently, for the most of the asset manager,

what are the workflow they have,

that will be inbuilt in this platform.

Yeah, so, yeah, so I think your question is that,

how they can initially,

how they can upload their portfolio, right?

Yeah, yeah, yeah.

So what we are trying to give is that,

that, we are going to give the AI based research tool through which you can upload your document

and we gave a template.

If you see at the beginning, there is a uploaded document with

some kind of a onboarding to go at the beginning, I can show you.

So, if you see, so if you

you see after we fill up all those details and we can see this is the kind of a template

if you see.

So, whenever you have a PDF or any kind of document everything right in the

AIB research tool you can able to upload those document and say and show them that I want

my output in this kind of a CSV format template.

Once you put it automatically your PDF all

those document will be converted into this kind of a format itself and then it will be

uploaded to the actual you know platform where it will be more about the structure kind of a data

Go-to-Market Focus: Retail First

we can build out of it first my objective is to go with the retail investor because retail investor

based on i retail investor and retail advisor because advisor is kind of immediate needs that

how they can do the more kind of a ai based research and everything and get more customer

are connected to them rather than going through the institution.

Institutional investor or

institutional client percentage is few if you see the retail investor is almost 160

million is kind of a 40 to 50 percent US population.

So our population demographic is more in retail

side.

So once we go to the retail side then we will go to the institutional side.

Any

Any other questions?

Closing

Okay, so if there is no more questions,

just want to share with you.

Contact and Next Steps

If you want to further reach out to me,

this is kind of my LinkedIn.

My company is digilite .com,

but the product I'm building is Finsight.

As I told you, this is the kind of a prototype.

The application is getting built,

and my objective is that by end of March,

the product will be ready for kind of a pilot onboarding.

Thanks everyone.

Thanks for your time.

Have a good evening.