Datasite为何停止自研文档解析：垂直AI落地与买建权衡

talk. Uh, just by way of quick intro,

I'm Adee, I'm co-founder and CEO at

Reducto, uh, but most importantly, we

have Raj here who is the global chief

technology officer at Datasite. Um, the

reason why I'm excited, uh, is I think

Raj has probably one of the most

interesting viewpoints for AI in that he

founded company very early on in the

vertical AI space. Um, got acquired into

Datasite and then got to see both like

subsidiary within Datasite to think

about Datasite as a whole. Um, I got

some of Raj's stats before this talk,

um,

where something like 60% of all

transactions over $100 million happen

through Datasite.

trillion plus tokens per year, I'm sure

you have plenty of other cool numbers,

um, but the goal for today is to make

this conversation. We'll talk a bit

about Raj's journey, um, some of the

problems he's gone through, and then

we'll try to open it up for Q&A as well

for whatever you all are curious about.

Sound good?

Cool.

Um, maybe to start, do you want to just

walk through that arc and what it looked

like behind the scenes?

good to meet everyone, Raj Bakhru. Uh,

my background's as a technologist, so I

spent my entire career in development,

started my career at Goldman,

Highbridge, Kepos Capital, was building

research and trading systems for quant

funds and all sorts of fun things.

Started a security firm that I sold to a

private equity back firm, ended up

running M&A and corporate strategy for

them. And then started this vertical AI

solution called Blue Flame in the M&A

space, um, because we sort of knew that

the impact to unstructured data was

going to be pretty pretty big. Um, so we

were building out for about a year, year

and a half, and then Datasite approached

us to acquire us. Um, and we started

working with Reducto actually while we

were separate, um, so before Datasite

had acquired us,

um,

we were small startup, we were scrappy,

uh, we built a lot of our own parsing

and Doc Pipeline and ingestion pipeline.

Um, had a great team, a great CTO,

co-founders. We were trying to save

every dollar we could. Um, so we when we

built all of that, we're like, "This is

great. It's working." Until it did. I

mean, I'll talk a little bit about that

journey.

Um, and then as we got brought into

Datasite, we were acquired obviously

very early on. Our team was about 50

people at the time we got acquired. Um,

the first thing that Datasite wanted to

do with us was bring us into all of the

data rooms. And if you guys aren't

familiar with the data room concept, um,

data rooms are basically where all of

the transaction documents get stored

when you're selling a company. So, as I

deep mention, something like 60% of all

transactions above a reasonable size

threshold, 100 million, happen on

Datasite. It's a place where people,

bankers primarily, are sharing all of

that data with private equity firms. Um,

so it means that there's a massive

unstructured data on Datasite, right?

Billions of pages of content. Every

major bank is using it. Every private

equity firm is using it. Every diligence

provider is using it. Over 500,000

monthly active users on Datasite

accessing that content. Um, and that

meant that adding AI solutions was

obviously top of mind for the board and

everyone else who was accessing the

platform. Users were asking for it all

uh day and day out.

We had a great data science team um from

the days where my CTO co-founder and I

sort of scrapily built the ingestion

pipeline. We had obviously moved on to

Reducto, recognizing that we weren't

going to have the level of quality,

speed, um, recall, cost that we could

get with Reducto. And I can talk about

some of the numbers that led us to that

that decision. Um, and why that panned

out today with Datasite scale.

But we needed to build fast. We needed

to get all of the data rooms that

Datasite had, 55,000 data rooms a year,

um, parsed, ingested into our vector

database so that we could build out our

graph rank platform and answer questions

within the data room uh that users were

submitting at scale.

Um, so that was a problem at hand. How

How How do we build this into the VDR?

They'd never done anything like this.

They'd never taken their data their

content out of their Azure platform.

Historically, it's you know

the walls were high around the data

rooms as you can imagine. It's the

world's most sensitive data. All of the

most material material non-public info

out there sitting in data sites virtual

data rooms. Uh so we had to figure out a

way to do this securely. We had to

figure out a way to do it um in a

place that it uh

complied with all the regulatory

compliance regimes that our clients have

and pass on to us. All of the

certifications, the ISO certifications

and so on that that we had. Um so we

spent a lot of time with the Deeds team

in figuring out how to get there, how to

make sure that it all of the content

remained

protected um and secured and get us all

comfortable with leveraging uh Reducto's

tooling.

tooling as I sort of alluded to is we

basically had to one move fast, but two

we did a whole bunch of benchmarking

actually with our data science team

around sort of our scrappily built

tooling for extraction and parsing and

ingestion. And what I 孔say about sort

of that landscape is that um it's all

edge cases. Like literally is everything

is an edge case. I remember getting, you

know, after we had built this we were

working with some of the world's largest

private equity firms and they would send

to us um examples where our reg platform

got a question wrong. And actually

Carlos here will will remember some of

these.

I got it wrong because the shade of blue

on the legend of this pie chart didn't

correlate perfect perfectly and the LLM

just read it wrong even though we used

the best vision model in the world. And

actually if you were to pass that same

thing as a pie chart or that pie chart

um as a bar chart to that same model it

would have gotten it right. It just

the devil's in the details on all of

this stuff. Um on Excel sheets, we had

to deal with merged cells. And as you

guys know, finance spreadsheets are the

most complicated documents in the world.

Like it is hard for a human to

understand these things. Um so getting

the models to actually understand the

way to parse this this content out and

understand the content was very very

difficult. Um so we found that we were

just spending an enormous amount of

support time dealing with all these edge

cases. And if I sort of think about the

buy versus build trade-off, what do I

want my limited set of high-quality

engineers working on? It is not the

support cases of making sure that we're

extracting extracting content properly

um when that's already been solved. Like

you know, rebuilding the wheel doesn't

make sense. Um so we we got a tremendous

amount of benefit and leverage out of

saying let's let's pass that on to

Reducto. Let's let our engineers work on

value-add revenue-generating business

problems. Focus on building agents that

our clients care about executing on the

interface and the rag platform. Um

building out graph rag was actually a

big thing for us and we leveraged the

outputs of Reducto in that process. So

we wanted our data science team focused

on that, not on how do I get the right

data out of a spreadsheet um and dealing

with all of the pains that we

experienced on that. Then you you run

into the pains of like if it's an XLSB

file um macro-enabled, if it's a

template file, it just it's endless.

Literally the end edge cases are

endless. So we're very thankful to

Reducto for solving a lot of those

problems for us.

we've had like a year of context, but I

think one of the most interesting things

about your journey is that you were very

early to the vertical AI app space. Um

and the problems that you work on are

hard um for many of the reasons that you

mentioned, but also like things beyond

that. Um and especially earlier with the

model landscape as it existed, I'm sure

it felt even more insurmountable. Um

so I'd love to start maybe just by

hearing a bit more of the pre-data site

journey of like how you got started with

Blue Flame, what you were hoping to

achieve, um and how joining Data Site

has kind of changed that.

our startup startup, we had started we

had sold the startup very early on

before. The last startup, cybersecurity

startup, we sold a year in. And we

started this one saying we are not going

to sell quickly. We are going to see

this out. [laughter]

And it's going to be a long time and

we're going to be a unicorn and do all

those fun things that we wanted to do.

Um

So, that was that was the the initial

state. I brought in a lot of folks that

we'd worked in worked with before and

that's one of the nice things of being a

second time

founder is you kind of know the network,

you know the people you want to bring

in.

So, we were very fortunate to bring in a

lot of those people and start with a lot

of the people that we knew and trusted

and we could yell at each other and

still be really good friends at the end

of the day. Um and we knew each other's

families and all that. So, that really

nice experience to start as a second

time.

fund ourselves to a large extent this

this second time. So, we bootstrapped

for a while and started building. Now,

that again made us do some things that

might have been a little excessively

cost-sensitive

and not the most optimal decisions, but

that's what you do as a startup

and when it's your own money especially.

So, we had a really great experience

building that out. Uh we were actually

going to do our first VC round. We had

our LOI signed, term sheet signed.

We had done sort of the dating process

with all the VCs out there, the Sand

Hill Road walk and all the New York ones

and we were you know, a week away from

sort of final final signatures and there

was additional capacity on our round

and they said you should go you know,

see if these folks are interested and so

we we approached Data Science and we're

like, do you guys want to put in 5

million in this round? And we demoed and

showed them a bit of what we were doing

and they said to us actually, we just

want to buy the whole company.

We're like, okay and literally happened

over a dinner.

Um

Like whatever meeting in the office,

subsequent dinner that night and then

they told us that.

So, we we very quickly sort of figured

it out. And the reason that we thought

it was really compelling

was if you know, this was about a year

and a half ago now. At that point sort

Cloud and Office Suite hadn't taken off.

You sort of have to go back to remember

where we were a year and a half ago. It

feels like forever ago and the world was

entirely different. Agentic coding

hadn't taken off to the level it has

now. Um

So, we knew that we had to raise a

massive amount of money. Our competitors

in the vertical AI space,

Um, of whom worked with Reducto, had

raised hundreds of millions of dollars,

literally.

Um, so we kind of knew that if we're

going to remain competitive, we were

going to be raising hundreds of millions

of dollars, we would be taking masses

mass dilution in doing that. Um, we also

knew that staying competitive against

the frontier models was going to be

increasingly difficult, particularly as

they were starting to launch vertical

strategies. And at that point, sort of

OpenAI for FS and Cloud for FS hadn't

been fully announced, but there were

some rumors around it. They'd made some

some ins- insinuations about it. Um, and

we knew that just to remain competitive

and differentiated, that we would have

to bring in new types of data. And to

us, the VDRs were this incredible

valuable resource of prop data that had

never been opened up to anyone in the

world, and we would be the only ones

with access to it at that time. So, that

was super compelling.

Um, Datasite also owns a private company

database firm called Grata. Has about 20

million private companies in its

database, has transaction data,

contacts, etc. Used very heavily in

private equity and banking worlds for,

um, sourcing and business development

and origination. So, how do I find

add-ons for my platform portfolio

company? Uh, very classic Grata type

query.

That data would be brought into a

BlueFlame as well. So, now we had these

two really nice proprietary data sets

feeding us in a vertical AI space where

no one else would have that. Really nice

and differentiated. And that's played

out really nicely. So, over the over the

course of the last year and a half,

we've been bringing those really tightly

together and um,

that's [snorts] that's been a really

nice story for us to combine engineering

teams, combine data, combine solutions.

Um, and then we actually benchmark that.

Like, the the ability or the the fact

that we brought the data closer to the

models saves on token costs, it saves on

run run times, um, and increases quality

of what we're doing. So, that's been a

that's been, um, quite a nice thesis to

see pan out.

Um,

all of that data, obviously, requires

scale. So, that's that's again where

Reducto comes in and helps us with a lot

of that. Ingestion processing and and so

on. Datasite over the course of the last

year and a half since we've joined them

has grown, I want to say about 40% top

line.

Um so just the amount of content that's

coming into the platform is is growing

pretty quickly and pretty massively. Um

the demand on our AI solutions have gone

up. So now people aren't just saying I

want to ask questions against documents,

the agents are obviously iterating

against those documents, doing much more

fanciful diligence analysis, or

generating PowerPoint decks, IC memos,

and so on. Um so it's pretty fullsome

suite of what they can what they can

accomplish with the solution together.

it's an interesting position to be in in

that I I I think you have one of the

most compelling data sets in the

industry. Um

you're in a position now where obviously

it's a global workforce. We were talking

about all the different offices and all

of that. Um and model capabilities have

gotten a lot better. Um so more broadly

like how do you think about what you

invest in internally versus what you

build uh

and buy externally as well? Um

and also just like what sorts of things

are top of mind as you think about the

coming year?

we think a lot about the build versus

buy, right? We we work with a lot of

vendors. We spend a lot with a lot of

vendors.

Um and that's an intentional decision.

Um

if [snorts] I think about what I want my

my most talented people working on, it's

not things that vendors can easily

handle and are dedicated to handling and

solving every day. Um

it's really hard to find really high

quality good engineers, right? The fact

of the matter is where everyone in

market is trying to hire the top tier

talent of engineering talent right now

because

what we're seeing, what you guys are all

probably seeing it is with AI coding,

the folks who get the most leverage and

value out of AI coding are the folks who

have really strong mental models of code

bases, really understand engineering

principles, have done it for a long

time. Um

so they've all been 10xed, you know,

over their normal 10x uh with AI with

agentic coding. The middle layer, so-so.

Um the bottom layer is actually getting

a getting a lot of leverage and now they

don't they don't sort of uh have the

burden of trying to figure out

documentation that doesn't exist in all

of that. The LLMs can build that

dynamically for them. Um so we're seeing

sort of a barbell effect where we want

to hire really really strong talent and

really junior talent and build that up.

Um where I want those people working is

obviously not on support cases. It's not

on things that are already solved. Um

it's on the hardest and most pressing

problems for the business.

At our scale, so Data sites

not a billion-dollar top line, but

general range of a billion-dollar top

line. Um at our scale to grow 20% a

year, which is our ambition or board's

ambition, uh we need to be generating

$200 million of revenue incremental

revenue every year. And if you ask

yourself how you're going to go do that,

um it's going to be by putting new AI

products out in market, leveraging,

getting on top of token flow, expanding

solution set that you're delivering to

your clients. Um

reinventing the wheel is certainly going

to get in the way of that. Um

having internal teams trying to uh

iterate on some of the existing

solutions that are that are already out

there and deal with the support burden

of those is not the best use of my

talent. I have about 250 engineers in

the org. So if I allocate out where I

want those 250 focus, we have five major

initiatives across the firm. Um those

are five revenue-generating

projects that we're putting out, new new

builds or consolidations of tools, new

commercial or monetization models.

Um

I want those 250 people split across

those five and I want as few working on

the layers underneath that as possible.

And if I can outsource everything on the

under the layers underneath that, that's

an ideal um ideal split of time for my

my team.

Uh before I turn over for questions, I

realize we have 2 minutes. I guess maybe

one last thing is like where do you find

friction today? Um what are problems

that you would hope people in the

audience would solve for you?

we still struggle with getting the

models to do exactly what we want all

the time. Um

we do a lot of working of all of the

models and actually, you know, if you

you back to pre-Reducto world, we wasted

a lot of time on trying to figure out

the right model for the right type of

chart for the right type of

uh document that we're passing in. So,

we've we've gotten rid of a lot of that,

but we still deal with it on the agent

front, trying to benchmark out, you

know, why is 61 Soul tool spam doing

tool call spamming when Opus 55 is not,

but then 61 Soul is so much cheaper that

we want to move in that direction, and

we're trying to get it to go to go

there. Um

so, we do a lot of sort of the

optimization of the three factors we

care about, which is cost, speed, and

quality. Quality is top top of mind for

everyone in our space, right? Like, any

finance user will pay any amount of

money to have the best answer.

Um so, we need to make sure we're always

optimizing on quality while trying to

solve the other two.

Makes sense.

Any questions from the room? Yeah.

integration between Data Science and

Reducto?

Reducto's um

operating in our environment against all

of our content. We have bill- billions

of pages of content. We have very big

spurts of content when bakers are

dropping in large data sets, potentially

up to a terabyte of data can get dropped

in on a moment's notice. Uh so, we have

an ingestion pipeline that takes all of

that, passes it through Reducto. We get

that back. We We leverage Turbo Buffer

as our vector database, so we're passing

it off to Turbo Buffer.

Um and then we build our graph rag on

top of that. We 're executing all of our

graph rag queries

using Prop Python code against all of

that.

Yeah.

we're we are actually customers of Data

Science.

curious about Reducto. So, we're sitting

on a transaction

like, we have a file system with a ton

of transactions, and we want to make

that available to agents, but there's a

way of obviously it's MNPI. We want to

anonymize that before it makes its way

into something like Reducto.

this, so

very helpful conversation.

That being said,

giving it to the agents

You You mentioned before that you built

a custom eval.

And so I I'm curious about how

Y'all went about this eval.

Um but like

curious about how you guys

So our custom eval that's related to We

have a few custom evals, but the one

that's most relevant to Reducto is what

we call VDR bench. We haven't open

sourced it. We're actually thinking

about doing that, thinking about

partnering with OpenAI on a paper around

it. Um what we're doing with VDR bench

is we basically take a common virtual

data room of hundreds of documents, um

and we ask a few hundred questions

against that data room with the known

set of answers against those.

Um we run that every time we make

changes to to pretty much anything. Um

the scores on that we expect to be

higher than any of the uh frontier labs

directly. So if you were to, for

example, drop that same set of files

into cloud projects or OpenAI projects,

we need to make sure that we're beating

that. Um and a lot of the reason that we

beat that our data science team

benchmarks of our recall rate at, you

know, improving 22% because of our

Reducto parsing. Um

you know, a lot of that's because of the

the way we pull out the content. It's

actually not the other side of it. A lot

of people think that sort of all the

work needs to be done on the other side,

the answering side, and most of the work

actually is on the extraction side. You

get that right, and everything else can

follow.