The GTM Context Graph Your AI Is Missing

Julia Nimchinski:

And next up, we have Seth Marrs, CSO at Sandler, James Roth. CRO at ZoomInfo, and Victor Oliveros, VP of Product Management. Super excited to have you here, James, not to steal your thunder, but I know you’ve made An exciting acquisition. Can you tell us more about it? And, yeah, excited to get into this.

James Roth:

Yes, we will touch on that, but a couple of acquisitions over the past year, just around, you know, the product that we are bringing to market, what we’re calling GTM1, which we’ll discuss today, and the companies that we’ve acquired to help build out not only the talent to build it, but the products to build it as well. So, yeah, I mean, our… Primary focus today will be on GTM one.

And I think most importantly, the underlying context layer that that ZoomInfo is is now delivering. So Julia, it’s great to see you. And Seth, always good to see you.

Seth Marrs:

as well.

Julia Nimchinski:

Awesome. Seth, take it away.

Seth Marrs:

Yeah, I mean just to start things off. I want to make sure, victor, also good to see you again. Long time no talk.

Victor Oliveros:

Yes, yeah.

Seth Marrs:

Yeah, I want to give you guys time to talk through GoToMarket1, and then… then we’ll do some Q&A after, and just talk through it. I mean, obviously, this is foundational for what everybody’s doing.

It really seems to be that you are… expanding and totally… I mean, James, you and I have been on these several times, and I’ve almost seen the journey play out from, how you’re supporting your customers, but also how you’re implementing this Yourself, within your organization.

So, I’m super excited to see where you’re going next, and how you seem to be becoming the place to go if you want a consolidated, unified context layer, and not just third party or second party, but now you’re even expanding deeper into first party and bringing it all together for your customers. So I’ll turn it over to you guys to talk a little bit more about it.

James Roth:

Awesome. Well, thank you, thank you, Seth, and well said. We appreciate that intro. So yes, I’m James Roth, I’m the CRO at ZoomInfo, joined with, today with Victor, our VP of Product. And, you know, what we’re going to be talking about today is really the underlying context layer, which is the foundation of You know, what we’re calling go to market one, from a product standpoint.

And I think just as a quick high level, and I want to give Victor plenty of time to demo the product itself. You know, ZoomInfo, we are a third party, you know, the third party go-to-market data provider. And, you know, we’ve got about 37,000 customers, we’ve got over 80% of the Fortune 500. We’ve also got 27 of the top 30 AI native companies that are very large customers of ours as well.

And the reason I say that is, is less to have a brag moment, if you will. I think it’s more so that we get to see the underlying go-to-market and the go-to-market motions across the very largest companies, the greatest AI-native companies, as well as, you know, 36,000 other companies.

And so I think from a point-of-view standpoint, in terms of what we see, what’s working well, what might not be working so well, you know, has certainly been a strong foundational point of view in terms of What we’re building and how we’re building it. So with that, I think.

Context graph, context layers certainly become kind of a buzzword in go-to-market land, but, you know, what we saw, call it 18 months ago, every board was saying, do more with AI, go buy AI, do AI, please, AI, AI, AI. I think anyone who’s ever been in a board meeting over the last 8 quarters has probably heard that and is tired of hearing that.

And I think what everyone went out and did is they bought AI tools and that, you know, early days when you deployed an AI tool, you got the wow moments. The LLMs are amazing. No, no diminishing that. But I think what we’re starting to see is folks have been token maxing for the last year or call it six quarters.

It’s more difficult now when you go to that board meeting and they say, okay, you’ve run up this massive bill. What exactly are you getting? And I think folks are gravitating towards, well, we’re doing more with less. Great. That’s, that’s certainly not a bad outcome.

But I think in terms of the actual outcomes, whether it’s increased conversion, increased ACV, inflecting growth. you know, at least for a lot of the companies that we see, it hasn’t necessarily met that promise yet.

And what we see, and I think why that is the case, is that folks spent all of their time and money going to deploy, you know, the AI tool itself, or the LLM itself, and they kind of forgot about the most important part, which is the underlying data that that LLM is running over. You know, scraping the public web, it’s amazing. I remember first, you know, getting ChatGPT personally a couple years back.

And it is like, oh my God, this is the smartest thing I’ve ever seen before. But now the ability to bring additional context into those conversations, I do think that is the edge that a lot of folks are missing. And there’s flavors of it, where you can bring CRM data in. If you think of the world of context overall, folks usually start with CRM, because they look at that as where most of their internal data lives.

But I think anyone on this call and anyone on planet Earth knows that the data that’s inside their CRM Probably not going to get you there. And so what we really see is this, this underlying context graph is, is not just one integration or MCP or hey, we can go headless with this particular product.

It is bringing all of that structured and unstructured data across first, second, and third party into one place. that then you have the LLMs run on top of. And so, you know, on this slide, there’s just a handful, you know, there’s probably hundreds. Our internal version, you know, we built a product called Mesh that’s basically Claude on top of all of our data.

We’ve got over 100 integrations into mesh, but this is just a higher level where in GTM land. You know, you’ve got all of your product utilization data. You’ve got all your first party data, your emails, your conversation, your calendar.

All of those have to tie together in order for, you know, whether it’s signal to action or whether it’s an account plan or any of the key go to market themes that we’re all trying to solve for and make better. have to sit on top of as rich a context graph as you can get. And so, as we keep going, one of the things is we were building towards the go-to-market one. product.

As we get into the very large enterprise, where they have tremendous amounts of resources, they’ve got hundreds of data science, data engineer folks, they’ve got basically unlimited resources and unlimited spend, you know, we look at some of those customers that consume huge amounts of our data.

But ultimately, they’re going to build their own context graph because of all the aforementioned comments in terms of resources and wherewithal and know-how. And I would say Ramp is one of those. They just, you know, announced their new valuation at $66 billion. They’re one of the great private companies out there. They have built their own customer data platform. They built their own context graph.

And I think they’re one of the, you know, probably 10 companies that has truly unlocked the real power of AI. And they’re a massive consumer of our third-party data, but they’ve probably stitched together the best context graph we’ve seen.

Now, as we were starting to build towards go-to-market one, we saw this opportunity to say, okay, most companies on planet Earth are not going to have those resources, they’re not going to have those engineers, they’re not going to have those data scientists, they’re probably not going to be able to build this out like RAMP has.

And so, we see a tremendous opportunity for the folks that might have 3 or 4 folks in RevOps, maybe with a go-to-market engineer here or there, maybe they don’t have a Snowflake or a Databricks, and they’re trying to get to those same outcomes.

How can we democratize, if you will, that context graph that either Claude can sit on top of, or some other AI LLM can sit on top of, that they can build agents on top of that context graph. That was really the genesis of Go-to-Market One.

So as we jump into this, and I promise I’ll be quiet soon and let Victor really show it, you know, one of the reasons, I think, is everyone is going out with some version of a context layer If you think about ZoomInfo’s business and the data that we have ingested over two decades, and you think of things like entity resolution or identity resolution, I think what folks start to come to the realization on is that third-party data is incredibly challenging.

You’ve got things like hierarchy, ultimate parent, parent-child, subsidiaries. If you think about a go-to-market where maybe my go-to-market, the parent account is owned by an account manager, but I might have new business reps that go and target subsidiaries, you think about the amount of noise or potentially complexity. in those entities and where they go to, and then the people within those organizations.

I think that’s where you can ingest all of this data, you can ingest all of your first-party data, but if you don’t have that source of truth, if you will, to say that, okay, Uber is the parent company, we’ve got Uber Freight, we’ve got Uber for Business, we’ve got, you know, Uber Eats, and then you’ve got Travis Kalanick that’s over here.

The LLM itself, if you just put it over publicly available data while you think, okay, Uber’s a public company, it’s probably going to get a pretty good result. There’s so much complexity to that. And that is really where we see our strongest value proposition is to be able to say, okay, across these entities, if you look at the people within these entities, how can you basically make that agent ready?

And so, you know, this is just in the world of signal where you have unstructured data, you have your first party signals. What was said between Seth and I on a call. If that’s Seth at your previous company. or it doesn’t necessarily have your new role, that signal becomes effectively useless.

And so I think that is where the power… I think there’s been an arms race for more signals, more data, more AI, if you will, but I think until you can truly compartmentalize and simplify that context layer. you’re not going to get to the outcome that you really expect to get to.

And so, I think once you have it built, I think everyone knows that, yes, once you have that context layer built out, then you can truly start building real autonomous go-to-market agents that are running things like champion tracking and account planning and You know, everything else in between that we probably spend a lot more time thinking about, but the thing I would leave and sort of pass over to Victor on.

Is that you have to get that foundation right. Having one of these that just sits on top of conversational intelligence, having one of these that just sits on top of CRM, starting to bring these things into the fold and getting that data foundation right is paramount to actually making AI work in go-to-market. So with that, I will pass it over to Victor, and he’ll show you what we’re talking about.

Victor Oliveros:

Thank you, James. Okay. This is GoToMarket 1, and GoToMarket 1 is ZoomInfo’s next generation agent-native application. It’s all things agent and AI native from end users, sellers, account managers, being able to ask questions about their accounts, all the way to RevOps, sales ops, being able to create agent orchestration, and agent native applications as well.

But the theme of this conversation right now is what we’ve found over the past 2 years in technology, AI is moving so quickly, is certain things are moving faster and getting easier. For example. Coding has gotten significantly faster and easier because of the frontier models. What we see now as the bottleneck for companies trying to change their go-to-market stack to be agent-native is context engineering.

So, let’s take a look at our context graph for a second. So, this is Uber for business, and the reason this is so important is this displays All the first third-party data signals unified under one entity called Uber inside the go-to-market context graph for ZoomInfo. Now, everything I’m showing you is available in GTM1, but it’s also available through our MCP server for all those go-to-market engineers out there.

You can actually get access to it right away, and start using it with Cursor, Claude Code, Codex, whichever you’re building on. But let’s focus on this for a minute, right? agents can only give as good insights as the context they have access to. So, let’s play this back.

If an agent has access to go-to-market insights, buying signals, intent, CRM data, emails, conversations, transcripts. moments in meetings, buying groups, all that, it’s able to provide great insights to sellers that actually move the needle for somebody’s business. But without the ZoomInfo context graph, which I’ll hide here. a company might start with their CRM data, right?

And if an agent… if this is all the data an agent has access to, it can tell you things like. what stage is this opportunity in? Or what’s the… what’s the likelihood of this closing based on the seller giving you their percentage here? But if then you layer on ZoomInfo. Things like firmographic data, go-to-market insights, like conversation intelligence, emails, loan statements, product reviews, competitive analysis.

When an agent has the same questions of. are we at risk of losing this deal? It has this universe of data that you can now answer.

And the GTM context graph is extensible, so you can connect your calendar, your email. even your content interactions on your website, and we get to the point where now we’re… what we’re trying to do, our vision, James and I talk about this a lot, is how can we get you the go-to-market context graph that has as much of the data that your agents need, so you can start building business use cases on top of it.

So, here’s what… here’s what this looks like. in our first-party application, right? This same context graph can be shown to users, where we can actually run our agents on it, next best actions, buyer engagement map, meeting prep, and so on. It can summarize important things to know about this company, employees to prospect into, and things like that.

But in addition to all that, right, ZoomInfo’s context graph has 100 million companies in it, and growing. So, same thing for John Deere, you get this rich context graph right away that your agent can build on top of. But let’s… let’s bring this down to earth into a use case that I think we’re all familiar with. Let’s talk about champion tracking.

And I actually want to show you a motion that we’re running internally right now. So. Every day, our sales leaders go into a champion tracking manager dashboard. And what it’s supposed to do is that it’s supposed to use agents to look at thousands of accounts. Find our best champions from those accounts, and see if they moved companies so we could partner with them again.

So these agents scan thousands and thousands of accounts, which is not possible to scale for humans, right? It ranks all the contacts in each account based on ZoomInfo buying intent, buying data, buying signals. And then even checks ZoomInfo job changes to see if this person had moved to a new company that could possibly be interested in our products.

But last but not least, it actually provides a grounded, no-hallucinations email and a silver platter for our sellers to reach out to these champions. So, 3 agents today. Champion Finder, which finds the champions, tailored email writer who’s trained on being grounded, zero hallucinations, and using the entire context graph.

And then what we call a self-healing agent that then checks the work of these two and provides feedback to them. So the more our sellers reach out, the responses they get, the better the champion finder gets, the better the tailored email writing gets as well. So here’s that in practice. So let’s take a look at… Atlantic Constructor. So, what we see here is that we have Jameson OnStop.

And, he works at Atlantic Constructors, and our agents figured out that This… this gentleman had worked at Churn Contracting Corp, and what’s great about that is he was an amazing champion for ZoomInfo in that company. And just that alone, hundreds of conversations, thousands of emails, having to know that they move jobs is a lot of context engineering that comes out of the box with a GTM context graph.

So let’s take a look at what that looks like here. I’m gonna toggle our context graph overlay so you can see, but just to find this champion alone.

The agent had to go through the entire context graph, from CRM data, conversational intelligence, emails, web research, contact insights, scoops, which is news about the company, and personnel movements. companies out there who want the same insight would have to build each one of these, or use the GTM context graph.

But I talked about the outreach email, so let me turn this off real quick, and we’ve all experienced a… avalanche of AI slob prospecting emails out there. And I want to come back to the humble email, because it’s still the best way to reach champions and reach prospects if it’s grounded. If it’s relevant, right?

So in this case. non, AI slop, non-AI slop subject, scaling modular pipeline Atlantic constructors, and if I turn on the overlay, check this out. Just that subject alone required CRM data, conversational intelligence, and previous emails. The first line It used, the agent used ZoomInfo’s personnel movements data to see that James had moved on.

The next paragraph recaps our success together in the previous accounts, and then, in addition to that, researches things about Atlantic Constructors so that we’re aligned with what they’re trying to do. Not about our products, about their goals. And then, a human call to action there.

Now. people may be able to do this one by one with Claude or ChatGPT, but scaling this to thousands of prospects, to hundreds of emails, and then being able to track whether their sellers had actually reached out and how that’s going is something that is great for agents, as long as they have access to the GTM context graph.

And that’s one use case out there that, I know that a lot of us are trying to drive for our sellers.

But it’s not… Everything… it’s just one use case, one really popular use case that we run into with our customers, that they use their context graph for, but… GTM1 allows you to create, on top of that context graph, things like… Daly briefings, close loss deal re-engagement, deal risk radars, and just clicking on these in GTM1 allows a user, an admin, to use this template, run this agent with the context graph out of the box. ready to go, so you can get to market quicker with your sellers in a cost-efficient way.

So… And that’s it, and happy to take any questions, or James, if you want to add something, feel free.

James Roth:

Yeah, Victor, I would just add one thing on the champion tracking in the email. I think one of the things that the context graph gives you power to do, and we use this internally. If you think of that email, and what we do usually in this world of signal to action, okay, champion move, that’s a strong signal, let’s go run a play, is those get funneled to SDRs.

And then the SDR, you know, that email might fire, and then the SDR, who’s a, you know, 23-year-old, will go follow up on it. I think when you have a well-built context graph, and if you think about Victor, you know, Victor came from People AI.

Let’s say I’m trying to sell to Victor while he’s at ZoomInfo, if you have the profile of Victor in your context graph, you’ve got his job history, you’ve got his previous emails, that’s ZoomInfo third-party data, and you see that James, the CRO at ZoomInfo. met with Victor 7 times when he was at People AI, that’s probably one of the most valuable bits of context that then you can run from a routing perspective or orchestration perspective.

You can say, rather than sending this to the 23-year-old SDR that sits outside my office, where even if it’s a better email, Victor still might just click, click, click, slop, slop, slop.

But if he sees James Roth come through, because we have proven in the context graph, real connections, we’ve been in person, you know, whether it’s in calendar invites, we had a dinner in New York City, we had 7 Zoom meetings, whatever it may be, that is just additional edge to say, okay, let’s get this signal to the right person with the right message, and frankly, get it to the person that has a relationship with Victor.

And I think in addition to the context graph, you’ve also got this relationship graph, if you will, and I think there’s… You know, the strongest signal still in sales today is that James and Seth have a relationship. And I would say, you know, in my Slack, a lot of it is like, hey, you’re connected with Seth on LinkedIn. Can you reach out to him?

It’s just a LinkedIn, I don’t think we’re actually connected, I don’t really know him, or I know him very well. So I have to spend that time, or I did, a year ago, to look through, do I actually know this guy, do I not know this guy? Nope, it’s cold, whatever. Versus you can just go ping that context graph and the relationship graph to say, yes, you met with Seth four times in 2022.

He was a former champion, XYZ123 and that. That extra bit of context, if you will, I think is so important, especially from a CRO standpoint, in terms of, like, what actually moves the needle on conversion, what actually starts to show those things ticking up.

It is that sending that to an SDR to send an automated email versus getting it out from James’s email, all that can be done through routing and orchestration within the platform, but I think using those signals the right way based on the right context, and Victor spoke to whether it’s churn risk, whether it’s health score, we’ve all been chasing those for years.

And everybody’s got a data model that says, okay, here’s a churn score, but they never really work. I think the additional context coming from all of these data points together, when you think of things like forecasting, when you think of things like churn risk and prediction models.

If you don’t have all of that relevant context, and you might have, you know, hey, product utilization is great, but then you have an anti-champion come in that, you know, basically made it their mission to get rid of your product at their last company, if you don’t have all of those signals and all of that context, your score’s just not going to be great. So… Those are just a couple additional points.

I think we’ve got seven minutes. If there is any QA, happy to happy to take it.

Seth Marrs:

So, so, like, James, I’m gonna jump in with a couple things, because I think there’s some… some… you touched on some really important parts that I think often get underrepresented. One is the importance of easy. Like, you talked about it, like, Victor showed it.

Everyone talks about, like, we’ve all bought AI, it hasn’t worked, and I think you can make a very strong Case for the reason it hasn’t worked is because I could do 85 connections in Claude, and you’ve got some person in your ops team that can connect it together once, runs it, breaks all the time. If I’m looking at what you guys are doing with GoToMarket One. you’re making it easy.

Like, there’s a lot of effort here to make it easy, and I mean, is it fair to say that Your success, and the success of anybody in this market, is going to be on your ability to take all this stuff, which everyone’s trying to do, but then put it into a format that simplifies it and allows people not to have to focus on keeping it up, but to use it.

Like, you talked about some of, like, the way the emails are crafted, the data that’s pulled in. Is that at the forefront of what you guys are trying to do with this?

James Roth:

Yeah, I mean, I think one of the… one of our North Stars is purpose-built for go-to-market, you know, and I think as you think of the Frontier Labs, you think of the LLMs, it does require some level of genius to basically build those, and frankly, some folks have built some amazing. You know, go to market, if you will, use cases. But our intent is absolutely that.

I mean, needing a team, a large team of engineers to build this out. I think the ability to get it done, purpose-built, like, here are the 20 to 25 plays that basically are top of mind for every go-to-market practitioner. Here’s the data that’s required. Here are the additional data points that are required from a third-party world, and then here’s all of your first-party.

Making that easy from, like, a drag-and-drop integration standpoint, making sure it’s hydrated with all the necessary third-party that you can get.

I think, building that while it is fun and cool to build, I mean, I love sitting in Claude and building stuff until the wee hours, like all the cool guys do, but I also think from a go-to-market standpoint, if I can templatize it and say, okay, these are the 5 plays that I need to run, whether I need more cross-sell and up-sell, whether I need to stop, churn, and down-sell.

You know, go-to-market is not as complex as we sometimes make it out to be. There are 20 core use cases across go to market land, and our goal is to get this ready made, spun up as quickly as possible, building out that context graph so that people can actually start taking action and taking the right action as soon as possible.

And I don’t know, Victor, if you’d want to add anything to that as the guy that actually built it.

Victor Oliveros:

Yeah, Seth, to your point, there’s so many things that frontier models are making easy now, right? And so the question we always ask is, what is the hardest thing that our customers have to go through in order to deliver these use cases to their own go-to-market team? And that really came down to what you mentioned, which is.

Connecting data sources for a demo is one thing, but having 99.9% uptime The data being fresh when agents need it, making sure that the context windows aren’t blown up by this data, which happens all the time, even with the best models out there. is something that we want our customers to not have to worry about.

They should focus on, I want to deliver champion tracking, I want to close more deals, I want to increase my top line growth. That’s what they should be focused on, as opposed to, did this integration break, and our agents are giving all the wrong insights to 10,000 sellers out there.

Seth Marrs:

Yeah, so it’s, I mean, let sellers sell, which is what they’re amazingly good at, not try to be side of the desk data scientists to make this stuff work. Absolutely. I have one more question for you around this.

Like, you brought it up, like, James, you showed it in the slide, those four components when you’re talking about hierarchy, you’re talking about I think that is constantly misunderstood or underrepresented, is your ability to apply context to an account, to a contact, to an opportunity in a way that it can be aggregated and provide insights. how do you get people to actually do it?

Because it’s boring to a lot of people, a CRO doesn’t think that it’s interesting, but none of this stuff really works well without it. So if you’re someone on this call, anyone who’s in ops would understand how that works. How do you help customers build the business case so they can understand it? Or how do you make it cost effective in your model so that when you buy it, you automatically get it?

Because without all that stuff, all these cool things just were everywhere rather than consolidated in key points, like key areas.

James Roth:

It’s a great question. I mean, I think we built a business based on like, let’s help get your CRM. Accurate and up to date. And that is still a huge business for us, and I think, you know, we’ve got 20-plus thousand Salesforce-connected customers, you know, similar thousands of HubSpots, on and so forth, and I think the value proposition years ago, while it landed.

You still had folks that were like, yeah, it’s just the CRM, it’s always gonna stink. And I think what… AI has really done is it kind of brings that to bear in a way that it… you start, you know, spam cannon. a bunch of, you know, decayed contacts and a bunch of folks that, you know, were at Cisco 15 years ago.

And so I do think it’s put a spotlight on the fact that, oh wow, to actually get these agents running at scale, we do need you know, better data and a stronger context graph, and so I think it’s starting to come to bear for those folks, again, that are, you know, call it 12 months into the journey, or 6 months into the journey.

I still think there’s going to be another tranche of customers that are kind of late to the game, and they’re very excited to go do all the AI stuff, and they’re going to be back in 3 to 6 months to say, help us build our context graph.

I think to your point on making it cost effective and easy, and I’ll finish on this point, I think, you know, there are there are plenty of of options out there that will basically say, hey, our AI will go pick up all this data from all these different places. It becomes incredibly expensive very, very quickly. If you’re thinking about running even that champion tracking use case.

Those tools just don’t go and find the people who have changed jobs. They basically have to pull data from everyone to then say, okay, Victor was at People, now he’s at ZoomInfo.

And so the amount of, basically, records or tokens that that’s going to take, I would say, is probably the thing we see most in some of these aggregators, or basically having Claude go chase these things down. is that if you don’t have a solid data foundation to begin with that says, gets you 75-80% of the way there, and you basically have to go run this across, not only are you paying the tokens to Claude, but then you’re also paying all of these, you know, aggregator points, if you will, to go pull those data from… pull those data points from different places.

And so I think from a cost-effective standpoint. We’re certainly seeing that, where I think, you know, three quarters ago, everybody wanted to get on all things consumption, because they thought it would actually be less expensive, because we’re just going to pay for what we use and what we need.

And now and, you know, coming off of Dreamforce with couple hundred meetings, we were in New York last week, couple hundred meetings.

This concept of, like, I wanna get back to a predictable budget model because these guys can run up massive bills very, very quickly, and so, you know, I think the way that we’re making that cost-effective is to say, okay, from a third-party standpoint, we’re gonna bring in the data that we may not have, but at least 80, 85% of the data we do have And it’s gonna be under management, you’re not gonna get charged every time you wanna go rerun this play, or rerun anything else like that, so at least we can take that component out of the overall token maxing equation.

And to your point on hierarchies and things like that, that is where, like I said, our business, the ability to resolve to the right entity, like, it’s all included in ZoomInfo, and I think the only thing that isn’t necessarily out of the box is your particular go-to-market motion, where you might want an account manager to own the overarching parent.

You know, you might want to say these subsidiaries, like, that’s… we’re never going to know the guts of your go-to-market in terms of how you want to change hybrid new biz, hybrid account management, but that’s like a last-minute tweak where at least we can give you the underlying hierarchies and the right entities to say, if you want to run this motion, this is how you’d run this motion.

These are, you know, this is basically how those companies operate, and then how you structure it is up to you. So, I think we’re at time.

Seth Marrs:

But one thing you said I just want to call out, and I’ll turn it over to Julia, is… I think what, like… Because you have the infrastructure. in the way this is set up, it actually allows you to be more efficient in the way you use your token, so this is going from, I have to spend money to do this game, to, if you do this right, I’m saving money.

James Roth:

100%.

Seth Marrs:

Interesting, really interesting.

Julia Nimchinski:

Phenomenal session. Thank you so much, Seth. Thank you, James and Victor. And where should our community go to learn more about GTM Context Graph?

James Roth:

So you can find Victor and I on LinkedIn, per usual. You know, you can go to gtm.ai, or you can go to zoominfo.com, so I’d recommend you check them all out, and feel free to shoot us messages directly, we will get back to you.

Julia Nimchinski:

Awesome. Thank you so much.

James Roth:

Thank you.