The Building Blocks of GTM Orchestration — Arman Vaziri, Ramp

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>> Yeah, I really appreciate everybody

showing up. Uh,

as Madhu mentioned, my name's Armon. I

lead our product and sales-led growth

engineering teams at Ramp.

Um, and today I'm going to talk to you

about, uh,

the building blocks of go-to-market

orchestration. And

um,

to kick it off, like, what do I mean by

go-to-market orchestration? Effectively,

like, what we're building towards is the

ability to just describe a motion,

right? Whether it's like playbooks or

experiments or like evergreen campaigns

that you want to run. And how those get

like distributed across the channels

through which you actually execute your

go-to-market, right? Whether it's

outbound or ads or web or whatever.

Um,

we want the ability to kind of describe

this and automate that output. And this

really started a few years ago where we

kind of noticed that uh, there's a ton

of great ideas, you know, like everybody

across product and data and engineering

and go-to-market have like really good

ideas for things that they want to do.

And the bottleneck is kind of like

everything after that, right? How do you

go pull an audience to go and target?

How do you go and convince a bunch of

people to like um,

abide by whatever strategy that you've

come up with or playbooks or enablement

materials. Um, and we wanted to try to

aim to uh, reduce that coordination

cost. So, there's parts of this where we

could see it as like an engineering

problem, even like a few years ago, just

go and create like a consistent data

substrate, go and like federate that

across the different systems through

which you, uh, run your go-to-market.

And obviously in the last few years,

agents have really like deepened our

ability to go and like push the level of

automation that you can do on behalf of

operators like as close as possible to

those points of execution.

Um

So, like really specifically, uh I'm a

golfer. Suppose I want to offer golfers

at uh

East Coast construction companies an

incentive to like try Ramp, talk to

sales, whatever.

Uh and we want to be able to go and spin

up an audience of uh golfers at East

Coast construction companies, spin up

like an incentive. Let's go offer like

some Pro V1 golf balls to uh these

people, go create like outbound

sequences, generate the copy, generate

uh creative for paid ads and for web,

maybe show some in-app notifications for

your customers, and do all of that

seamlessly by just describing the

intent, right? And probably more than

just this one sentence.

Um

So, a few years ago, we kind of

identified uh a few fundamental

challenges here. Um As was previously

mentioned, uh the necessary data for

this was just messy, inconsistent across

systems, right? Everybody's operating

off of a different uh source of truth,

and that makes it like effectively

impossible to go and distribute some

coordinated action across these

different go-to-market teams and

channels.

Uh The next is that like reps were just

buried in busywork, right? Even if like

you have the best intentions, I want to

go and like run this campaign, uh I want

your help doing it. The reality is that

like uh our sales teams are in

back-to-back-to-back-to-back meetings

all day. They're outbounding, they're

selling, and um

the operational burden of like doing

everything in between sales was just

really high, uh which made

kind of like really scaling out

experimentation and creativity

challenging.

Uh And similar to that, just the

coordination and distribution are

expensive, right? If you're like, "I

have this idea. I'm going to go write

this like proposal, this enablement

material. I'm going to go try to like

convince a bunch of people to go and use

all of this." That's just like a really

challenging thing to do on any like

pace that's not on the order of like

months.

Um

So,

um

over the last few years we've been

trying to solve this problem from the

ground up, right? How can we start with

that uh ingestion and consistency

problem uh and data quality, which is

just like,

you know, on the road map every quarter.

Um

how can we then go build those vertical

efficiency and growth levers, uh saving

people time uh in like managing

operations and execution, uh as well as

like how can we improve conversion

rates, make people more performant by

being able to kind of scale some of

these more like uh informed and

personalized and creative strategies.

And then how can we extend this

horizontally, right? Teams have very

common workflows at some level, right?

Everybody wants to outbound, everybody

has meetings.

Uh how can we go take the patterns that

we build for one team and start to just

mirror it to others?

Uh and now kind of where we're at is

like this distribution and coordination

problem, right? How can you go and

execute across multiple channels

simultaneously through just like the

description of intent?

So, yeah, I'll get into the building

blocks.

Really broadly, uh

go-to-market agents are complicated.

Um

in order to do this effectively, right?

Your agents have to understand pretty

much the entirety of your company, how

you go to market, why products are

useful,

uh how to kind of like segment your

buyers, your prospects, your customers

from people who have like never heard

about you and you have like no

information on them and they have no

information on you, all the way to like

customers who are actively using your

products who have like a totally

different set of um

you know, problems that you have to work

with.

Um

And to just start to get a little

technical here,

um

we

started with like this consistent data

foundation uh problem. And if you're

looking at this and you're like, that

looks like a CDP." Uh yeah, you're

you're right. Uh we effectively went and

built um

an internal customer data platform at

Ramp uh where we're effectively doing

your very traditional things. We're

going to take CRM data, product data, uh

enrichment data, um web data, buying

signals, you know, whether it's things

that are internally modeled like um

I don't know, we think that this

customer has a high propensity to attach

to procurement or treasury uh all the

way to things that are like external

signals like funding announcements,

um as well as like interaction data,

right? Emails, meetings, calls, uh page

views, um and on the

signal side of this, right? We have some

set of real-time events that are coming

in, uh things like emails, you can go

and pipe them onto a Kafka topic,

consume them, uh and then funnel them

back into uh both like we have like a

Postgres database that backs all of

this, it enables us to maintain like

transactional guarantees, referential

integrity between the entities that

exist and the different entities that

exist, right? Between your CRM, between

your product, between third parties,

um and attribute everything to the right

level of detail, which we found to be

like a pretty important problem, as well

as all the associated metadata around

capturing like where did this come from?

When did it, you know, come in?

Um

as well as starting to embed a lot of

this data, right? So much sales data is

just inherently

um

unstructured, right? You have like call

transcripts, you have emails, you have

notes, and the ability to kind of search

across that is really valuable.

Uh we have a set of online batch jobs,

which are

really just calling a lot of APIs uh for

the most part. Uh Ramp's addressable

market is pretty much like the entire US

um and now expanding internationally.

So, being able to kind of like

pre-compute, pre-process, pre-ingest

like all this enrichment data about who

we can sell to and who we're already

selling to

is um

really important for us.

And then um as previously mentioned, a

ton of work has gone into the offline

piece of this with uh DBT, Snowflake,

pulling everything into our warehouse,

doing a lot of offline batch compute,

and then piping that in via reverse ETL

back into the same layer.

Uh next, more tactically, the way we

tend to approach these problems is solve

for one team first, then scale

horizontally.

Um

as I mentioned before, you have like a

very overlapping set of problems that

exist, right? Everybody wants to do

automated outbound. Uh everybody wants

to prepare for meetings. Uh whereas

certain teams may have like

problems or like things that they do

that are isolated to them, like QBR

generation.

Um

and

to get into an example, like one of the

things that we shipped is like

pre-meeting briefs, right? Um

for AMs, AMs are like count account

account managers.

Uh they kind of manage the customer

relationships that exist, trying to

ensure that customers are using Ramp

uh as best as possible.

And um

there's a lot of like important context

that goes into like

uh a meeting, right? It's like what are

we talking about? Who are we meeting

with? Um

what is the AM trying to do? Like what

are the

product usage information? What are the

account vitals? What's the agenda that

we want to tackle? And similarly, like

what is the customer trying to do,

right? Do they have open tickets that

they're trying to address? Did they like

email us saying that there is like a

specific thing they're trying to talk

about? And how can we pull this together

for AMs so that they can go in prepared

uh and kind of manage the

uh operational piece of just being in

back-to-back-to-back meetings all day.

Um

again, technically, uh the place to

start with this is obviously if we're

trying to generate a pre-meeting brief,

we need to know what these meetings are,

uh so we can pipe in meeting events, uh

do some hydration, map uh

things like attendee emails, meeting

titles

uh back to the accounts that we're

meeting with.

This is like a sneaky hard problem at

Ramp because you have the same emails

that can work on behalf of multiple

businesses, so it's kind of like a fuzzy

match, and we can go and persist that,

so that way every downstream consumer of

like, "Hey, I care about this meeting."

doesn't have to go and like recompute

this from the ground up.

And also, as mentioned in the previous

talk, uh we've also built a system

around durable execution, right? That's

pretty agnostic to the trigger that

comes in.

Everything is represented as a durable

thread built around Temporal,

representing each tool call and model

call as an activity. That way, if uh

you know, like a worker goes out for

some reason, it can resume uh execution

from where it left off, uh pulling

together all the state that had

accumulated at that point in time,

instead of starting back from like the

beginning of the thread and trying to

reprocess everything, which would be

very inefficient and slow.

Um there's also like great

out-of-the-box capabilities for things

like config-scoped tool calls, uh

different agents are going to have

access to different uh sets of tools,

which give them access to different

information, different integrations, uh

and different skills that might be

necessary to actually perform the work.

And similarly, there's things like uh

human-in-the-loop uh

tooling to just pause execution, get

input, resume.

Um

And then getting to the uh unstructured

piece of this, as I mentioned, like

unstructured information is probably

like the most valuable thing you're

sitting on uh within your um

warehouse or your notes or wherever you

store this today.

Uh so, we have some set of real time

data coming in, um, meeting transcripts,

emails. We have some sort of like uh,

batch jobs that are kind of pulling in

like enablement materials, product

knowledge, playbooks, um,

chunking them, embedding them, putting

them in Turbo Buffer, and allows you to

kind of or allows agents to go and

search like what do I care about? What

am I trying to answer right now? And

doing some combination of like uh,

vector search, attribute search, keyword

search in order to pull information

scoped to like a specific account, for

example, uh, without having to pull in

like the full raw corpus into agent

context, um,

which would also be very inefficient,

very expensive.

And similarly, we've gone and built a

skill library to allow people to

customize their agents, right? Getting

back to the meeting brief example,

different people have different formats

that they care about. They have

different information that they care

about, um, and allowing them to kind of

represent that, uh, in text, giving that

to the agent to pull it together,

uh, has been like very valuable for

getting adoption.

And putting all this together, you get

an operational background agent, right?

You have like every night we're going to

go and generate these things, fan out a

set of agents that are going to go and

compute, uh, per account, uh, meeting

prep, uh, which gives, uh, or which use

some set of tools giving them access to

like uh, that online CDP in Postgres I

mentioned, the vector database, uh,

meeting prep skills that we own at the

system level, as well as like custom

instructions that users are providing

themselves.

And getting into the extending the

blocks,

um,

the goal is for these foundations to

speed up the next thing, right? Meetings

are super important. We want to be able

to generate things like post-meeting

follow-ups and things like automatic CRM

updates, right? Which can pull in the

transcript and say like, "Hey, we

discussed this potential expansion

opportunity. Let me go and pre-fill all

the information needed to create that

opportunity, get a thumbs up from my

rep, and just make it happen.

Um

and similarly, we want to extend it

horizontally to other teams, right?

Which is mainly an exercise of creating

specific skills, data integrations, um

and like just data ingestion itself,

where we can say like, "Okay, email,

call transcript embeddings, custom

instructions, generalizable, but if

we're building this for AEs, we're hand-

handling like

pre-sales, um opportunities,

we need to go and focus more on like

third-party data instead of a bunch of

product data that we have already, and

that needs to be uh incorporated into

our customer data platform. The skills

need to go and reference kind of like a

different set of uh information that we

have on the people that we're trying to

sell to.

And similarly, uh we've built this in a

way where employees have access to the

same tools and skills that are being

used for the background agents that

we're creating, right? We set up a what

we call like our GT MCP, uh and this is

basically just like

uh a window into the same exact tools

that we've set up for these background

agents, so that way the things that we

build are just kind of automatically

federated out to people who want to go

and build their own agents. They want to

go chat with the information that we're

setting up, uh and build their own

automations.

And they're building a ton of them. Uh

this is just like

uh a glimpse into some of the analytics

that we've uh done taking the reasoning

generated by

uh the MCP uh tool calls, you know, that

we've uh

that are being executed uh remotely.

And

this compounds because like when people

go and build their own thing and they go

and connect to our MCP, they're

basically telling us like, "Here is a

problem that I have. Here's how I'm

trying to solve this problem." And we

can go and work with them to be like,

"Okay, we can just go and productionize

this, uh distribute this to everybody

who probably has similar problems." And

they give us the prompts and the skills

and the like, you know, even

applications that they're vibe coding,

uh

to just like really simplify our ability

to just go and productionize

um

like these use cases.

So now

you're probably wondering uh

what about that golf example that I had

mentioned at the beginning?

Um the orchestration problem.

Um

the the point that I'm trying to convey

by talking about all these specific

things that we're doing

is that these vertical builds that we're

creating are the foundation of like

uh

multi-team, multi-channel like

distribution.

Um if we want to be able to say like

here is a playbook. Here's how you sell

procurement. Here's how you sell to

construction. Or here like wacky

experiment ideas that we have uh like

offering Pro V1s to golfers, which is

actually like

uh it works really well.

Um

we need to be able to say like

uh take in that corpus of information of

things that people are trying to do and

federate that out through the background

agents that are actually creating these

artifacts that people are like using to

operationalize like go to market and

execute.

So

for my Pro V1 golf example,

um

the goal is to funnel this into ramp

revenue, uh the internal application

that we have built um

and go and like effectively like funnel

this into some of these vertical

solutions that we've created, right? So

you can say like for SDRs, we want to go

and create an audience of here are the

golfers that we want to send things to.

We can go and generate like personalized

copy and sequences that they can go and

send. Maybe we want to go and create web

landing pages and spin up the uh images

and the creative that will point these

uh email sequences to. And we can do all

of that through just like the

description of like

here's my intent. Get the people who own

these channels to review them and sign

off. And really allow us to just like

move a lot quicker in how we

uh ship and like scale creatively.

Um

across all these different go-to-market

channels.

So,

the goal of this is to ship faster, ship

safer,

um scale our teams, become more

efficient,

and

um with these campaigns, we can go and

execute them across like multiple

channels with consistent audience

targeting, um

agents can go and hold context on

multiple things that are like options,

right? We can go and execute this

campaign or that campaign or that

experiment and balance the like

traditional multi-armed bandit problem

of like exploring like new possibilities

versus like being safe and like going

into just known returns.

Um

and then we can build in guardrails as

well to go and um

effectively like manage compliance

rules, rules of engagement, and being

context aware, making sure we're not

doing the same thing over and over

again.

Um

and yeah, just do this on behalf of

everybody.

And those are the building blocks of

go-to-market orchestration. Thank you,

everybody.

>> [applause]

>> We have probably time for one question.

Hey, there we go.

>> Hey.

>> [clears throat]

>> So, just curious um if how would you

approach building something like this

for a smaller company or for a company

that's that's just getting started?

>> Yeah, I think a few people before have

like mentioned something similar, but I

would go and like find the very specific

use cases that you can build automation

around and just like solve really

specific problems that exist first. Um

like 3 years ago, there was two of us

and we were building like automated

outbound, right? So, like

we're just trying to figure out like how

can we go and use GPT 3.5 and like put

personalized copy uh into some sequences

and go and like pull data from uh

wherever to go and generate that.

And by doing these things and solving

these problems, you get like a really

good understanding of how this works,

how it could extend to other teams.

Um

and solving like real problems as you

go. The reality is that like you can't

spend like a year going and building

like some really complicated system

architecture that like is perfect. So,

you have to like piece together the

vertical solutions

and then stick them together.

>> [music]