The Building Blocks of GTM Orchestration — Arman Vaziri, Ramp

What go to market orchestration means

00:12Yeah, really appreciate everybody showing up. Uh, as Mao mentioned, my name is Arman. I lead our product and salesled 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.

00:20And 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 emotion, 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?

00:47Whether 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.

01:13And 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?

01:18Um, 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.

01:42And 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.

Golf balls for construction golfers

01:59Um 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.

02:07Spin up like an incentive. Let's go offer like some prov1 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 inapp notifications for your customers.

02:25And 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 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

Three bottlenecks: data, busy work, coordination

02:55uh source of truth and that makes it like effectively impossible to go and distribute some coordinated action across these different goto market teams and channels. Uh the next is that like reps were just buried in busy work, right? Even if like you have the best intentions, I want to go and like run this campaign.

03:10Uh I want your help doing it. The reality is that like uh our sales teams are in backtoback to backto-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.

03:26Uh 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.

03:41I'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. Uh, um, over the last few years, we've been trying to solve this problem from the ground up, right?

03:57How 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.

04:27And 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?

04:36Uh and now kind of where we're at is like this distribution 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.

04:56Um, 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, uh, 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.

05:28Um, and to just start to get a little

Building an internal customer data platform

05:37technical 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 CVP, uh, yeah, you're you're right. Uh, we effectively went and built um an internal customer data platform at RAMP.

05:56Uh, 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.

06:19Um 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 Postgress database that backs all of this.

06:37It 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?

07:03Um 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.

07:18Uh 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 premputee, 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.

07:42And 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.

08:07Uh next, more tactically, the way we tend to approach these problems is solve for one team first, then scale

Solve one team, then scale sideways

08:14horizontally. 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.

08:33Um, and to get into an example like one of the things that we shipped is like premeating briefs,

Pre meeting briefs for account managers

08:42right? uh for AM. AM are like 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?

08:58It'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?

09:10Do they have open tickets that they're trying to address? Did they like email us saying that there was like a specific thing they're trying to talk about? And how can we pull this together for AM so that they can go in prepared uh and kind of manage the uh operational piece of just being in backtoback meetings all day.

09:26Um again technically uh the place to start with this is obviously for trying to generate a premeating brief. We need to know when 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.

09:53This 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 recomputee this from the ground up.

10:12And also as mentioned in the previous talk uh we've also built a system around durable uh execution right that's pretty agnostic to the trigger that comes in.

Durable threads on Temporal

10:22Everything 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.

10:47Um there's also like great out-of-the-box capabilities for things like config scope tool calls. Uh different agents are going to have access to different 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.

11:06And 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.

11:24Uh so we have some set of real-time data coming in u meeting transcripts, emails. We have some set 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 turboper and allows you to kind of or allows agents to go and search like what do I care about?

11:47What am I trying to answer right now? and doing some combination of like vector search, attribute search, keyword search in order to pull information scoped to like a specific account for example without having to pull in like the full raw corpus into agent context.

12:04Um, 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 uh meeting brief example, different people have different formats that they care about.

12:18they have different information that they care about. Um, and allowing them to kind of represent that uh in text, giving that

A skill library so people set their own format

12:27to 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 and Postgress I'd mentioned the vector database uh meeting prep skills that we own at a system level as well as like custom instructions that users are providing themselves

13:02and 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 postmeating follow-ups and things like automatic CRM updates, right?

13:11Which can pull in the transcript and say like, "Hey, we discussed this potential expansion opportunity. Let me go and prefill all the information needed to create that opportunity, get a thumbs up from a rep and just make it happen." Um, and similarly, we want to extend it horizontally to other teams, right?

13:33which 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 AEES who are 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 skill need to go and reference kind of like a

14:05different 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?

14:18We set up a what we call like our GTM MCP. Uh and this is basically just like 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.

14:34They 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 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.

14:49And 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.

15:15and 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

Back to the golf example

15:37orchestration 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- channelannel like distribution.

15:52Um, 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 are like wacky experiment ideas that we have uh like offering uh prov1's to golfers which is actually like it works really well.

16:08um 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.

16:28So for my prov1 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 we'll point these uh email sequences to.

17:00And we can do all 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 goto market channels.

17:17So 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?

17:33we can go and execute this campaign or that campaign or that experiment and balance the like traditional multi-arm 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 guard rails as well to go and um effectively like manage compliance rules, rules of engagement, being context aware, making sure we're not doing the same thing over and over again.

18:05Um and yeah just do this on behalf of everybody and those are the building blocks of go to market orchestration. Thank you everybody. Awesome. We have probably time for one question. Hey there we go.

Q&A: where a smaller team should start

18:32Hey, 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.

18:50Um like three years ago there was two of us and we were building like automated outbound right so like we were 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.

19:07And 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.

19:25So you have to like piece together the vertical solutions uh and then stick them together.