The Sales Brain: Autonomous Revenue, with Expert Judgment

Julia Nimchinski:
And next up, we are joined by Brett Crane, VP of Solutions at Vivin. And today, Brett will demonstrate the sales brain. How exciting! Brad, how are you doing, and what’s new?
Brett Crane:
Good, how you doing, Julia? It’s good to see you again. I know, kids aren’t sick, things are going well, we have a quiet period next week as a company, so things are looking up.
Julia Nimchinski:
But, our agents sick?
Brett Crane:
Sorry, no, no, Agents are never sick, my kids are not sick, all things are good. Should I, should I transition over to presentation mode? Is that okay?
Julia Nimchinski:
Yeah, let’s dive into it.
Brett Crane:
Sounds good. Well, great. For those of you who don’t know me, I’m Brett Crane, I’m the VP of Sales and Solutions over at Vivint.
Over the next half hour, I just want to talk to you a little bit about how we’ve built a sales brain, and also walk you through some of the workflows and the experiences that we’ve had in talking to our customer base and leveraging our product ourselves as well, too.
The interesting thing is that we’ve seen a lot of different spectrum of where people are in the process of automating and leveraging AI in their sales workflows, and so I wanted to talk to you a bit about what we’ve seen and give you a taste of what our product looks like as well.
So with that, kick over to, kind of this view of the world where there’s this gap that I think exists in a lot of the conversations we’ve talked to sales leaders. So being a sales leader myself, I’ve personally felt it.
Our product obviously helps along the way, but one of the things that our sales leaders that we talk to tend to tell us is that they’ve been given these tools. LLMs have really started to help their teams, but there is a bit of this, hey, I got Claude as a company, go make use of it, sales.
That’s kind of out there in the zeitgeist, and so… We’ve seen that teams have started to get some of the benefits of productivity, maybe doing things they didn’t have time to do before, maybe getting better things like account planning, deeper research.
CRM capture, some of that stuff, that’s been some of the promise of some of the tools that have been out there.
But largely, we’ve seen people that are either very early in some of their adoption of these processes, or they’ve actually gone and had to hire a bunch of people to pull them in, or build externally to make these things more sophisticated.
to get to some of these things over here in sort of the promised land of what’s been, sort of touted as the thing that a lot of these tools in the AI space are trying to give you in sales, which are things like higher velocity, faster ramp times for your salespeople, higher win rates, bigger deals, things like that. And so.
what does that really look like?
And this is just sort of a journey that I’ve seen a lot of customers go through, and I see it as being, like, this adoption path over time, and it’s not like it takes… Months or quarters or years to go do, but it’s played out over time, and sort of the starting point of the race is different for everyone that we speak to.
And, the first one, as probably all of you started out with ChatGPT or something like that. For some people in the business world, this didn’t start for, you know, maybe it was, like, 12 months ago for some people.
It was probably a little earlier for a lot of the folks on the phone that are here in, you know, Julia’s summits, but… The thing that we see is that as you get into the simplest first step, there’s obviously this difference of quality between people, and this is where we started to get into these things about.
kind of prompt engineering and things that got popular maybe, like, a year ago on LinkedIn and things like that. And what ended up happening is the unlock for that was, hey, how do you, like, cull that variation between users? And so we saw people starting to save what worked.
That’s where you started seeing these posts on LinkedIn from the vendors that, you know, tap into these LLMs that said, hey, we got a prompt library for you, right? And then people started to pull these things in.
And that’s what a lot of our sales leaders now have, at the very least, is something that their sellers, you know, their go-to-market teams can leverage. to ensure that not everything is generic, right? You point the models at the right things that it should be going after.
Obviously, it has this large corpus of information that it could go after, but if you’re a salesperson, you don’t care about, like, the life sciences side of the model and what it’s actually… trained on, you care about the sales stuff, right?
And so, this is where you started to see things like, you are an expert salesperson, and you do X, or you do Y. And that does point the model at the right places, but one of the things it started to do is it gave more specific answers, but not with the right context. And so we saw that really giving rise to things like connecting context.
So, you saw some of the bigger LLM players building things like pre-built integrations, you saw Anthropic put out MCP, right? And that became really the standard for a lot of the way that things are evolving today.
in that it actually gave agents or AI tools a way to talk to other systems in ways that’s really useful for users that use these tools in, kind of, the prompt box or in these workflows. And so. That was great until it realized, like, it doesn’t necessarily know the best way how to do these things, even if it’s connected to these places.
So, yes, you could say you are an expert salesperson, but what does an expert salesperson produce at my company, Vivint, on a sales team? And so that’s where you started to get the rise of skills, right? And so some people are starting to get here with us.
I’ve seen… you know, a variety of folks that have built some really cool stuff on this, and others that don’t really know what that means, and I think one of the easiest things I’ve heard, if you’ve sort of followed this stuff on YouTube or other places, is, like. find a problem, if you do it more than once, create a skill.
And just tell Claude, tell ChatGPT, create a skill, go do it again, and then you can reuse it. Skills then evolve, and they get better and better, but what skills didn’t necessarily have is always access to the right tools to act on the right things at the right time.
It didn’t really have a good understanding of fuller workflows that might go from, you know, like, an audit to an actual function to then a review or something like that.
And that then gave rise to, somewhat recently, plugins, this concept that it packages up skills and has more context about what it should be doing and when within these broader workflows. But I think plugins, although it gave the ability to have multi-skills stacked and pulled together and made it easier on users.
Not everyone’s there, I’ll just sort of say that, right out of the gate. And on top of that, it still needs to be asked how to work. I think the previous session talked about autonomous GTM workflows, and those are still coming to light.
You get… things like OpenClaw, Hermes, and others that are sort of autonomous operators in more of the development world, but we’re starting to see some more of that. How do we have our agents act on our behalf? in a way that’s still customer-facing and customer-ready for sellers to feel comfortable with.
And so, that’s what we’re trying to tackle, this problem of an AI sales teammate. You know, it wasn’t about more tools. Yes, this is going to progress over time, but we actually found a lot of people coming to us with too many tools that have too much overlap.
And didn’t really have the right ability to operate in the moment that they needed in sales, which is different than other moments, like in the development world, or in, you know, maybe, like, finance or things like that, that might be, different workflows.
A little bit more of a science than an art in some of these cases, and that’s where we’re trying to step in to provide this sales brain to the picture.
So… You know, regardless of where you are on the journey, what we’ve seen is that in this void, there tends to be problems like you don’t have tools for one of the number one things you do in sales, which is being on a call like this, but with a prospect on the other end of the line.
So how does it keep up with you and actually help you do the things on the right, you know, on the sales call when you’re live? And no sales call is the same, there’s a lot of similarities to sales calls, right?
You can be… Trained on all the best things, but invariably you’ll be asked very different things, or very difficult things that are variations of the same thing, and you need something there to help you, and know the context of the deal that you’re working, needs to know what a deal is, needs to know the past history of the deal, and have connectivity to the other systems that hold deal context.
And it also needs to capture that workflow, like we talked about with plugins a moment ago. It needs to know what to do in different steps of the sales process, and do those in a connected way that’s really helpful for your team. So that’s why we built what we built.
We’re here to have a live call assistant, so if somebody is on the other end of the line asking you detailed product questions. pulling you down a rabbit hole you didn’t plan for on the call. We’re there to provide sellers live call assistance, real-time answers, real-time guidance.
That’s grounded in sales reasoning and grounded in this concept of turnkey workflows for sales. It needs to know how sellers sell and help them sell in those moments that other tools don’t.
So we’re trying to bridge that gap to get you from, LLMs, which are super, super capable, if you’re not using one, it’s kind of crazy, you need to be, obviously. We’re using Cloud ourselves internally, we’ve seen a big shift, as of recently, for a variety of reasons, it seems like, away from Copilot, Gemini, and ChatGPT towards Claude.
It’s not necessarily the right choice for everyone. That’s what we use internally, and it’ll be a part of what I showcase today, that… how we use our own product. So, with that said, like, how the heck did we do this?
Well, one of the things we did is we had a study that actually said, hey, like, if you had something that needs to ground an LLM in sales knowledge, what would that do, and what would that accomplish? And the graph on the left… I’ll show you a couple other kind of nerdy slides here, but I’m not intending to be that way.
It’s just to show you that, and you probably felt this as a user, that over time, you would chat with ChatGBT, Claude, whatever it might be. it would start to get stuff wrong, right? It would hallucinate. It would confidently tell you the wrong things.
Models have gotten better, a lot better over time, but models have also still started to do things like build an interactivity to say, hey, I don’t know the answer, let me ask the question.
Now, the interesting thing about that is what that’s actually doing is giving it context, because it’s got a fork in the road, and it doesn’t want to make the wrong choice, right? And so what we’re trying to do is prevent, in a sales decision.
like answering a prospect’s question live on a phone call in a matter of 2-3 seconds, there’s actually probably 10 to 20 decisions to be made at that moment, because you could go down any of those forks, and if you take any of the wrong forks, you go down the wrong road, basically. And so, what we did is we put a knowledge graph on top of this.
To say, hey, before it goes the LLM, let’s give it all the context that it needs, and give it to the point where it has an answer that it can convey. And we nearly use the LLMs as a better mouthpiece, a way to present the text, present the words to the person on the call that needs the help in that time.
So you don’t get things like, LMs built for simple tasks that can’t answer a complex question, or suffers from kind of a generic answer, when what you really need is how you sell, what you sell, and why you’re giving that answer to that particular prospect in that moment that’s in an evaluation.
So, that’s what we’re trying to do here with our product. Is really give it a… give it a layer over the top that says this is sales-specific, and it knows how you sell at your company to give you this stuff right in that moment that matters in a matter of seconds.
another nerdy slide, on purpose, but an average customer of ours, there’s a knowledge graph that looks something like this. The thing on the left is, like, the overall zoom out, the thing on the right is the zoom in. And what you’re looking at with all these dots, is just basically memories and knowledge components of the graph.
So colors indicate things. These are things like stakeholders, products you sell, features you have, competitors you have, customer case studies, sales process stages, all those kinds of things are nodes within the graph. If you drill into any one of those nodes, it’s interconnected with tons of different things.
And so, going back to my example of being asked a question on a sales call. Imagine you get asked the question of, hey, like, why is feature X better than competitor Y, right? That happens all the time to us in sales. You actually need to know all these things, right? As a seller, what you’re doing in your head is saying. Who asked this question?
What are their use cases? Do I know the competitor? What does our product do? What are the capabilities of our product? Is there a customer case study that I can cite here? You’re going through all these things as a seller. Again, general purpose LM can probably give you a decent generic answer.
But it doesn’t have all the right context that you as the seller needs to have, and that’s what we ingest into our knowledge graph. Said a little differently, when you onboard our product, it has to figure out how you sell.
You point us to the ways that you sell, things like your product knowledge, things like your sales process, things like your competitive battle cards. And as you have interactions with your customers on deals.
phone calls, emails, external Slack messages, things like that, that additionally hydrates the knowledge graph, so that it creates those connection points, so that when, you know, Jane Doe asks you the question on the call about your competitor’s feature and why you’re better, it actually takes all those things into account and gets you an answer in a matter of 2-3 seconds on that call live.
That’s a little bit under the hood of how we operate and why we’ve done the things that we do, and why it’s so hard to actually build something like this context layer, you know, into the LLM, because it’s not something that’s turnkey for sales teams, it’s typically something that’s provided to sales teams, and then you’re doing your best with what you have by going through that journey I talked about earlier.
So, said differently, again, it’s kind of the easy button for sales in our mind, where a workflow could look something like this. If you’re not familiar with our product, it’s called Hero. That’s this, this teal logo you see here. Sorry, the logo itself white in the teal box. And so it could look something like, prior to a call.
Our tool has all the sales context you need, because it knows how you sell what you sell against who you’re selling in that moment, who you’re going to meet with, it has all that context, and can help sellers sell… voice-based or doing research.
If you prefer a different tool, like Cloud, ChatGPT, Copilot, whatever it might be, we also have MCP connections, so you can do that there and pull in the brain of our product into that process, so that it actually hydrates it with the context you need to have a better prep call or a better prep session.
while you’re on the call, we’re the interface that’s there feeding you that knowledge directly. And so, as a seller, you’re getting that.
As a manager, as an enablement person, as a RevOps person, you’re building out the systems and processes and messaging and conflict resolution, all the things that your salespeople need, this is what’s going to feed into those call suggestions that are happening live on the call to your sellers.
And the nice thing is, because this is all real-time transcripts, all this stuff is real-time, too. You don’t have to sit for 2 hours or an hour waiting for the call to process.
You can actually, even by the end of your call, as it’s still going on, start to create some of these things, like updating Salesforce, sending a message to your team on Slack to update them.
at mention our agent over there, draft an email, whether using us or using Claude, use Claude or other tools to do things like update your sales deck so that you have the most current information based on what we just learned from the call. Do things like update your deal room, or whatever it might be, and other tools you’re using.
So, this is the kind of sales flow that we typically power for our customers. Again, we create that easy button for sales teams to just go through the flow and have the right context at the right moments of their calls. But, I thought that what I would do here is switch over to a demo, so I can show you how this plays out.

Some of this will be from… a little bit from the eyes of the manager, most of it, but it also will just showcase, like, how it shows on the call for the seller, as an example, too, so… With that, I will, switch over to, to our product. I’m gonna take one quick sip of water, though. All right.
So, couple of interfaces I’m going to show you today, because we have a web-based version of the product, we have a desktop version, I’m going to show you some cloud stuff, too. But, here’s an example of a call you might be on. And, and what you might want to be doing there, obviously, is, like, presenting your product, pitching your customer.
Going through some complex concepts, and what we’re doing in the background is we’re transcribing that all in real time, and we’re showcasing what we call whispers. So whispers are any kind of guidance we would give to a seller as it happens. That’s all grounded in the way that you are supposed to sell, as I mentioned earlier.
So, when Ryan asked the question about, you know, what’s a day-in-the-life experience look like for the product, this is grounded in how we expect the sales rep to answer. Short, concise, sort of short and sweet, so they can actually say these things on the call, because it needs to arrive fast and be something a human can actually say back.
It’s also grounded in the way… what it knows about the company. So, as an example, Ryan asked a question about, hey, like, what would the demo look like for our leaders? And we know that we’re actually selling to SMB in this case, and so it grounds it in the fact that mid-market and SMB is our target, and that’s the answer that we provide.
objections might come up, like, hey, we might have other tools that kind of have some overlap here, like, in this case, Gong, and how do we position against a competitor is right there for us as well, too.
So just simple examples of things that we’ve actually given Hero as the centralized knowledge set that it should be leveraging, so it can provide that in real time on the calls as those things arise. How do we do that?
Well, from a trainer standpoint, excuse me, from a, from a leader standpoint, I work with, you know, myself, the people like the enablement folks, the RevOps folks. maybe SE leaders. And we have an area of our product where our product gets trained on what it means to be a seller at your company.
So things like product knowledge, things like process knowledge about how you run. Do you run Sandler, Challenger, MedPick reviews? How do you actually run your deals? And so that all happens here, in this area of training. It also comes with this ability to generate skills.
So we talked about skills earlier, and this idea, like, for our company, what we do is we have a what we heard slide, and we have a value pyramid that we run. So, sort of common tactics in the world of sales, but we want to make sure it’s buttoned up and clean, and so for our sellers.
You can do it a variety of ways, but, you know, I’m just… I kind of go the nerdy way, but, like, the what we heard slide, you just do a forward slash, and it can run for you. Right? Or you can automate this as well, too, but the idea is it runs, and it gives you back the challenges that we heard directly from the mouths of our prospect.
It gives verbatim quotes from the people that said it, so that we know now, again, grounded in the things that matter the most to us as what we sell. The things that are important for us to bring into our slides. So what does this actually go do for us? Couple of things.
If I show you the desktop version of our product, I bring it up for a couple of reasons. One, as you can see, that it’s actually live transcribing the meeting, so it’s already happening.
you, you have all the meetings that are being transcribed live, and it can work across a variety of modes, so if it’s Zoom, if it’s an internal huddle, or whatever you want to do, we find our customers actually have started to record more of these calls, because we can set to auto-record.
And then these things will get auto-organized and leveraged in the way that your sellers start to sell, plan, do follow-ups, and a lot of those internal conversations around strategy are just immediately leveraged in building decks, building follow-ups, and things like that without a lot of back and forth.
Once I’m here, though, these skills actually generate documents. They’re called artifacts.
And so, for the case of the value pyramid, the reason why artifacts matter for us is because you’ve probably heard these words in, again, Claude, ChatGP, and other places as well, too, but artifacts are things that can actually outline more deterministic outputs, meaning I need this thing to look like that, because Claude’s gonna go do something with it.
And so it produces that from the skill in our product, so that Claude doesn’t have to be the brain, we’re the sales brain that gives the output that Claude needs and then goes and leverages.
So what does that actually mean, is that once I have the value pyramid, or I have the what we heard, or what have you, that’s all right there for Claude to then go use. So I’m going to kick over to Claude. You’ll notice it’s Claude because it’s dark mode, and I have it in that, just to differentiate the products.
But over here, that’s where you can have your skills, right? So you probably have some skills built out. Those of you that are further in the journey, you might have things like plugins built out. We have one called the Hero Deal Deck, as an example, that we use internally.
And all I have to say is this to Claude, so I just say, for the customer name, right? And then off it goes, and it starts to figure out what’s the right thing to go do, based on the skill and what’s in the plugin.
And so what it’s doing is it’s finding the tools that are appropriate for the job, and so it’s querying out to figure this out, and look, there’s Hero’s logo, right? It’s listing out all the artifacts for that particular customer.
So I’ve built this whole process with my team to make sure that anyone on my team that goes and does this they kind of don’t have to think about it.
They just have their calls, and they know where they are, and this thing can actually run autonomously to go leverage this skill that’s inside of our plugin that packages up all the skills that we use, and it goes and it gets the artifacts that are needed to go build out this deck.
So while this is happening, it’s, you know, gonna create some slides and visualize, and we’ll come back to this in a second.
I do want to point out that I said this earlier, Claude can connect to Slack, we can connect to Slack, there’s, like, this web of systems at this point with MCPs, but as an example here, like, I might want to go update my team in Slack, I can just click to send it to my Slack channel for the deal, so we’ll give the update from our product over to Slack, you don’t have to go do anything and write that up for the team.
The other thing that our customers have pushed us towards, and there’s a couple of ways to do this, some of them do it with Claude, some of them do it with us, but there’s this concept of My Deals, and My Deals looks like this, where you just have a bunch of deals.
And in the setup of our product, you tell us what CRM you use, what fields you care about, how those fields should be proposed from our agent. And so as calls happen, as emails flow in, as slacks take place, or if you just want to ad hoc check this. our agent will then propose those kinds of updates for… for them as well, too.
So for me as a manager. I care a lot about this being accurate because of the forecast call, right? So, we actually have date-stamped, initials, who said it, what they said, and someone like Drew, in this case, can just go update Salesforce right here.
From the suggestion, or more likely what he does, he reviews this in bulk and updates those all at once. So, that can all happen sort of simultaneously, and more autonomously, and with fewer sort of human inputs and clicks.
But for us, we do actually review these things by hand, just to make sure that, you know, we have everything right and captured, and the way that we, you know, Drew expects to see it on the forecast. He doesn’t let this thing run solo, because he likes to review it. If we kick back over to, sorry for skipping a few screens here, over to Claude.
Oh, here we go. It didn’t know something, right? So, there are some things that I’ve actually left in here, and it says, who’s presenting this? Well, this is Eric Steele, and, you know, we’re actually further along in our mutual action plan, so we’re going to say that.
And also, we’ve put into this skill that I want to upload a value ROI case, if we have it, which I don’t need to do here. But what this will then do is kick into the flow of actually going through and creating the deck. Right?
And so now it knows it’s Eric, it knows everything about the context of the deal, because it looked through and found the artifacts. It scanned through and looked over all the other sources as well, too. While this is kicking off, I did want to show you just really quickly.
that the sources that we leveraged here are things like Docs, Slack, Drive, Salesforce, HubSpot, whatever you might have. That’s what our system is pulling in to make sure it gets that artifact right.
And, and over in the world of Claude, that’s what then feeds to make sure once it has those artifacts, it can produce the right output for them in the output here as well, too. Of course, doing a live demo with Claude when things aren’t deterministic.
It looks like it’s going to do a quick review, and I’ll kick over to my finalized slides here in just a minute. Here I am using Sonnet, so it should go Pretty quickly. If it takes more than the next 10 seconds, I will go to an example that’s already been output, just in case something ran awry here.
So here we go, looks like just in time, the, the slides came up. And, And what it did is, we have a template that… I don’t want anything to change other than the specifics that are, like, kind of flagged to change.
So, for instance, Eric is the rep, that gets inserted, and we have some slides down here that go through and look like that value pyramid, right? So our artifact from Hero populates the artifact, in Claude.
with all the information it needs to present the value pyramid, same thing on what we heard for the verbatim quotes, so all that’s done for us, and then Eric just makes some minor tweaks, and then that’s his deck that’s ready to go. So that’s the flow that we have internally.
I did just want to kick back over to the sides for one second, and we’ll open it up for questions. So, my point was, regardless of where you are in the flow, a lot of sales leaders like myself. You might be here, hopefully you’re a little bit further up. A lot of people are starting to approach the skills, getting to plugins.
Some people are hiring people from the outside or from their sales team to be AI kind of gurus on the team. Regardless of where you are and the things you’re trying to do, we try to be that tide that rises all boats, basically.
So if you’re early in the process, or if you’re later in the process, we add that brain, we add that on-call support, and we add that layer of sales expertise and domain expertise to all the things that you’re doing, regardless of where you are along the AI adoption journey.
We have a bunch of customers, great customers, and I’m sorry, Julia, taking off mute. This is my last slide, I promise.
But, folks like ADP, Dayforce, DocuSign, they’re doing things like saving time, adding dollars to their deals, and, and doing things like improving win rates across their, their customer base, and sometimes it’s because they can sell more of the thing that they don’t typically sell on their platform.
Sometimes it’s that they can move faster on calls, but that’s all the things that we help provide to them to ensure that they get deals bigger, faster, and people ramped more quickly. And if this is interesting to you, we do have a free version of the product. I’m gonna leave this up while we, we stop for questions here.
You can scan it, try it out. Of course, contact me and contact my sales team if you want something a little more advanced, but this’ll get you started for free if you want to try the product for free.

Julia Nimchinski:
Amazing presentation, thank you so much, Brett. Let’s address a couple of questions we received here from the audience, and… The first one is speaking to the just growing gap between product teams, ship faster than, obviously, sales teams can sell it.
So, how does hero stay aligned with the latest product knowledge without creating a governance headache?
Brett Crane:
Yeah, it’s a great question, and one that we had to tackle. We heard people loud and clear probably 6 months ago about this. The nice thing about the MCPs is it actually allows us to connect to where those sources live.
Some people tell us those sources are Slack, some people tell us it’s Confluence, some people say it’s… you name it, linear, whatever it might be, where this stuff is sourced, we actually can tap into those through MCP. And the great thing about the memory model we built and the knowledge graph that we built. is it’s there to learn.
It’s there to learn from the MCP, it’s there to learn from your phone calls, so as these things change, whether it’s a customer phone call, or whether it’s a product release, we keep up with that knowledge base. And that’s actually what our product does.
It’s a little more complicated than I’m making it, because there is some… Caching and some other magic to make sure that, like, a new product release is something that can be conveyed on a phone call in, like, 2 seconds when the question comes up.
But we do make it really, we make a concerted effort to connect to these sources, and these sources are so disparate.
that we have a really large catalog on our MCP, and we’re actually rolling out a couple each week at this clip, because we built all the protocol support and authentication support that we need to roll these things out really quickly.
And then if you want to MCP and connect into our product from other products, that’s already done, so you can go do that. You know, we’re on the Anthropic and OpenAI marketplace, for instance, if you search for Hero. it’s in there, and you can make the connection. You obviously have to use our product to do it, but… But it’s there for you.
Julia Nimchinski:
Brett, our next question, speaks to governance. And so, what customer data can hero access, what stays private, and is any of that information ever used to train shared models?
Brett Crane:
No, so our model never trains any, you know, OpenAI or Anthropic or any other models, or any custom model we’re tuning that’s used by multiple customers. we are actually… the knowledge graph is unique to you.
Every customer has their own unique knowledge graph that’s… all the source material, all the training, all the things that happen there to actually make that your own are your own. It never leaks anywhere else, no customers can access other customer stuff. We don’t train from that, it’s all yours.
Julia Nimchinski:
Good to know. Thank you so much. And, yeah, I guess the next best step is to just scan, their QR code, or where should our people go?
Brett Crane:
Yeah, yeah, that’d be the way to do it. Feel free to go to Vivin.com, where we have some research, or meethero.ai, which is, I think, where this scan will bring you, to go check out the product. Again, you can scan here with your phone. check it out, sign up for free, sign up a couple of team members if you want to.
Again, I’m here, my sales team is here, so if you want to get in touch with us, and we can talk more detail about you evaluating the product for more of, like, an enterprise need, you know, let us know. We’re always, we’re always here to take sales calls ourselves.
Julia Nimchinski:
Amazing. Thank you so much.
Brett Crane:
Thanks, Jillian, appreciate it.