speaker-0: Probably have over 100 agents running inside of our instance of Clarify. You can see the vast majority of them are actually enabled. So we are doing this for real. One of the first things I built started in Claud Code, did a lot of analysis. The output of that was a Notion Doc. Just gave that Notion Doc to Rep here and it built the agent in minutes.
speaker-1: Did you show us any delicious?
speaker-0: No, rather than deletion, I would refer to it as consolidation.
speaker-1: Welcome to GTM Vault. Every GTM engineer runs the same quiet sprawl, an enrichment workflow here, a follow up agent there, a Zapier chain. Nobody remembers building. Each one solved the real problem. Together they are a second job. Michael Bartimer's job is different. He is the GTM engineer at Clarify, the autonomous CRM, and its stack is an experiment in deletion. How much of the standard GTM automation layer disappears when the CRM itself does the work? Today we are watching it. Welcome to GTM Vault, trusted by twenty seven thousand plus founders and operators building the future of revenue. This is Show Me Your Stack, the series where operators screen share the systems they actually run. This show has spent the past few months watching operators vault agents onto CRMs that were never built for them. My guest today works at the company making the opposite patch. Clarify is the Autonomous CRM, an AI native system with a built-in agent that handles meeting briefs, data entry, and pipeline updates without being asked. The company raised $22.5 million in its first year with backers including USVP Gradient and Madrona Madrona. Michael Bartimer is Clarify's GTM engineer, a self described RevOps, Swiss Army knife, and today he's showing us the most credible tests any tooling company. How Clarify runs on Clarify. Real workflow, real data views, anything confidential is blurred, not staged. Let's see it. Michael, welcome to the show.
speaker-0: Awesome, thanks for having me, Rick. I appreciate that.
speaker-1: You're booking so today you want to talk about GTM auto automations and agents. Sorry about that. Noise. Check this then. Okay, so Michael, what specific workflows, tools, and agents you do you want to take us down the road today? Yeah.
speaker-0: to to share my screen in a second. I'll just give you little little more more context and again appreciate the the thoughtful intro. We i I'm gonna show a few examples of how clarify runs on clarify. And when I share my screen here in a second, I'll just be in our our desktop app. But I mean we've we probably have over a hundred agents running inside of our instance of Clarify in in our own workspace. The few I'm gonna show today, if we have time to get through all of them are probably a little bit more geared towards I would say the GTM engineering slash RevOps audience, more so than the traditional like sales leader audience. But you know, we we obviously have numerous agents and and workflows running that are, you know, more core deal lifecycle pipeline related type of stuff. But the few I'll run through today is one website visitor outbound. I'll walk through that agent. And then maybe more on the the grow side for like PLG businesses, a a PQL scoring and and routing agent. and then happy to to talk about if we have time, kind of more enrichment stuff like free mail enrichment is another good example. that that I'm running but yeah more specific to PLG businesses. Right. So hopefully that's some helpful helpful context. But let me go ahead and move this over here and share my screen. All right. You should be able to see my screen. Yep. Awesome. So again I'm in here in our in our desktop app just in the the agents section here and and this is just all the agents in our workspace right just to scroll down like there's you can see the vast majority of them are are actually enabled. So we are are doing this for real. And and there's a lot there, but I'm just gonna go into this first one I wanna talk through is this website visitor outbound. And at the bottom here, just high this. At the bottom here we have rep, which is our built in AI that can do basically anything in in the entire system. So I use it heavily and not gonna kind of go through this agent in detail, but rather I'm just gonna jump in here and ask rep, what does this agent do? Please explain it to me. And I'm just gonna let let rep where's whisper below popping up. I'm just gonna let rep here summarize kind of exactly what this agent does. And anytime I'm I'm building an agent or workflow in Clarify, I'm just giving rep either I'm just talking to rep, either just kind of doing a knowledge dump. like rambling with WhisperFlow or I'm just pasting in a linear ticket or I'm pasting in a Notion doc for something a little bit more kind of complex or thought through. I'll give it like a PRD style Notion doc that I've I've worked with. And it will go and actually plan it and and draft the agent or workflow for me. So this is just giving me a a much higher level kind of step by step of what this agent is doing. So we're we're Pretty simply just using a vector for website de anonymization on our website. And then this is just a a webhook that's ingesting the vector payload. So the first step is it's just parsing the data from from vector. So things like, you know, name, title, LinkedIn, company name, et cetera. And then I'm doing a lot of like disqualification up front, right? So disqualifying. employees that are coming to our website, certain competitors, suppressing customers, and then we're getting into the classification of different segments and ICPs and and that's really what's happening here in step three. And then in step four, actually once they pass the ICP kind of company level qualification, then we're doing the the person level a persona resolution. So have basically I think six different personas in here. And which we'll see in in step six. But first in in step five, just writing this data to clarify from the the vector payload. And also maybe I'll I'll talk a little bit about how I'm solving the what I would refer to as like the stale data problem from a lot of these data vendors. Actually just just simply just using the LinkedIn profile from the vector payload and actually looking them looking at their profile. And that's all happening via this agent. Right. And so it actually understands what what company they're currently at. Because I've I'm sure many folks have seen numerous examples from all the data providers out there that oftentimes the data can be six, twelve, if not, I've seen many instances of 24 plus months out of date and looking at their their previous role. So this agent is is solving that that stale data problem. And when it finds like the the true like current operational company, is it what I have it referred to here? Is it just runs them through the entire workflow here again and actually writes those updates in in Clarify so we're getting the the correct data and and resolution. And then right now I'm happy to talk about this more, but using instantly right now for marketing led outbound. I think Clarify campaigns are are good for sales outbound, but certainly have gaps at the moment that we're working on for marketing style outbound. So currently just you know automating enrollment to those persona specific campaigns in instantly. And then writing data back from from instantly to clarify for for you know meetings booked or or certain outcomes for reporting. And then lastly, just posting to Slack, more of a enrollment confirmation in the thread. So we have a Slack channel called Website Visitors. You know, everyone who goes through this workflow is written into there for visibility and yeah, for enrollment confirmation specifically, j and a few other use cases, just threading and replies automatically for tracking. And here you can see at the bottom by the numbers, yeah, a lot of Lot of manual minutes saved. I'll I'll pause there. happy to to continue on with this one. Maybe we can look at a specific run if you want.
speaker-1: Yeah, yeah, that'd be great. Show us the run and if you wanna tal talk us through the results after the run. Yeah.
speaker-0: So I'm here at the sure at the top, just on the overview tab, I can always jump into the activity and I can click into any of these. So let's just, you know, click into the most recent one. And this is just gonna give you the the run summary here at the bottom. But if I scroll all the way top, I can actually see, you know, the the the thinking and what actually happened step by step, right? I could drill down and click into any you know, any of the tool executions, any of the reasoning. and see what the agent did here. But for, you know, verification it's you know, the run summary at the at the end is gonna be the the most useful.
speaker-1: And and how easy is this to install this onto your say your your website?
speaker-0: I would say pretty easy. I mean I think we had it s t I mean once yeah, once you go and buy the vector in this case, I think we had it stood up in a day or a couple of days.
speaker-1: That's great. And so this is great because you know the sales team it's hard for the sales team to dynamically in real time identify who's coming to the site and then create a personalized email or even send a LinkedIn connection request, maybe in parallel. And with this automation with this webhook, companies are able to install that kind of a signal based outbound automation. Whereas traditionally it wasn't really possible, I want to say like two t two years back or so, it started becoming more and more accessible through these webhooks. And it looks like now you've made it a lot easier to install for any size company. Yeah.
speaker-0: Yeah. Yep. Absolutely. and I I mean I think this is just whether you want to call it a a a play or or or or whatever. I think this is just the first like basic simple use case that any team can stand up, you know, within a few days, within a week. Even just to I think there are tons of companies out there that just don't have visibility into folks coming to their website and just streaming that into a Slack channel just so you can see visibility is I think a great step one for folks. and then you can kind of do outbound to you know, certain people. And and just to give you some sense, maybe not specific numbers, but I I think I think most companies would be very surprised, like if they listed out their top four, five, six personas within their couple I C Ps. I think the vast majority of companies out there, a lot less of their website traffic would be in inside of those ICPs and personas than they would expect if that makes sense. So
speaker-1: That's a good point. I mean at the same time though, don't forget, like unless you're you have like a paid advertising budget, your website's not getting a lot of traffic to begin with for I want to say ninety nine percent of the B to B websites out there. And so you know, you need to have certain motions in place before automations like this really take effect.
speaker-0: Yeah, that's a great point. That's a great point. that's actually a great segue. Let's talk about like PLG and and PQL scoring as the the next agent specifically. So I'm just gonna do the the same thing and just gonna clear this new chat. And you can see it automatically pulls in the the agent I'm on as as the context. So I don't have to tell it to look at a specific agent. And I'll just ask it the same question. What does this agent do? So this is an example of something one of the first things I I I think I built at Clarify when I joined about four months ago. And this was one where you know I've started in I mean, just in in cloud code, it did a lot of analysis. You know, the output of that was a notion doc that I used for collaboration to get feedback from the team. You know, after a few quick like turns on that doc, just gave that notion doc to to rep here and it it built the agent in in minutes, right? And This is one where, you know, using a lot of product usage data, and like I would say half of these data points are a tool called out to post hog to to get that usage data, and the other half are just in clarify and in properties. So really you can kind of build it however you want or or use whatever tools you want. And in this case, just firing on certain types of of deals that are PLG deals, right, through website signups. And just using kind of seven signals in here, all different, you know, product usage data points to just get to a a score of one hundred. And yeah, pretty pretty simple I would say here.
speaker-1: So what does that score do? Does that sync with the C R and then notify the sales rep to identify and then start a conversation? Yeah.
speaker-0: Exactly. This one is just scoring all the, you know, all the website signups, the PLG website signups. And then this next agent over here is the one that actually does the routing. So let's just say, what does this agent do? So this one, yeah, does the routing into I think three or four different tiers. the first one is immediate immediate reach out by sales. The next one is yeah, so here are the four paths. And this is where we we actually have a human in the loop step. And currently the the sales team just wants to do a quick review. I mean, they do literally a a 10, 20 second review of the account and just one click enroll them in their sales campaign out of Clarify. So that's that's path one based on some firmographic data points. and then path two is all about you know trying to get them to that PQL score of sixty or greater. in order for them to be classified as sales ready. Right. So this is really, I would say, a lightweight V1 of what like a product led sales. PLS motion would would look like on top of PLG data being like very easily accessible by by agents.
speaker-1: Yeah, this is great. Traditionally you'd have to layer in product analytics and then figure out a way to route that to like your your main CRM, which in most cases can be like a Salesforce or a HubSpot. And then yeah, I mean connecting those two together it was a lot of steps and a lot of work, whereas it looks like you've really boiled this down to a very simple embed here.
speaker-0: Yeah. I mean the the thing that I love about like being, you know, a a GTM eng engineer, like slash rev ops background at Clarify is like I I can use Clarify for the pipes of basically anything I'm doing. And you know, where i we can maybe talk about campaigns a little bit. I mean that's outbound is obviously a big focus of of GTM engineers and something I'm I'm working on at the moment. and as I kind of alluded to earlier, like it's Cl campaigns in Clarify much more suited at the moment for sales led outbound rather than marketing led outbound. But this is, I mean, one of the reasons why I I love doing this, not just because it's so easy to build and iterate and speed to deploy, I think is a is a really key advantage. But I'm also able to like you go out and use the tooling and vendors that you know most folks are using and build use those to build workarounds into Clarify and then work with our product and engineering teams. to to actually close the gaps. So yeah, I would say super exciting. And in the last, you know, four months since I've been at Clarify, like it's been really eye opening to me in terms of how easy some of this stuff can and should be, where historically even six months ago, yeah, even six months ago, like this stuff was I was doing this outside of Clarify in HubSpot and And, you know, various workflow automation tools, and obviously cloud code, but so much of this being natively consolidated in a single system, specifically in the system of record, makes building and speed to deploy and iteration cycles so much faster and easier.
speaker-1: Yeah, absolutely. Michael, thank you for showing us the real system, not a demo environment. The actual stack running live, deletions and all. Well did you show us any deletions? Not really, right? Right,
speaker-0: No, no.
speaker-1: you didn't. Right. I'm gonna scratch that out, true.
speaker-0: Okay. I mean, these were like V1s of of different use cases. I mean, for website visitor outbound, I think our team was using something else like last year before I joined, but it didn't really work too well. But it's rather than deletion, I would refer to it as like consolidation of workflow automation in general for for what I mentioned a minute ago in terms of ease ease to build and and speed to deploy and iterate. Right.
speaker-1: Right, right. I like that w way of putting it. Michael, thanks for showing us how Clarify consolidates different workflows and tools into one unified interface, really making everything run smoothly and a lot more efficiently. Clarify's YouTube channel runs deeper walkthroughs for anyone who's interested, go check it out. And if this episode changed how you think about the automation layer, subscribe to GTM Vault. Share with your network. And join us next week. Build systems, not noise. All right.
speaker-1: Did you show us any delicious?
speaker-0: No, rather than deletion, I would refer to it as consolidation.
speaker-1: Welcome to GTM Vault. Every GTM engineer runs the same quiet sprawl, an enrichment workflow here, a follow up agent there, a Zapier chain. Nobody remembers building. Each one solved the real problem. Together they are a second job. Michael Bartimer's job is different. He is the GTM engineer at Clarify, the autonomous CRM, and its stack is an experiment in deletion. How much of the standard GTM automation layer disappears when the CRM itself does the work? Today we are watching it. Welcome to GTM Vault, trusted by twenty seven thousand plus founders and operators building the future of revenue. This is Show Me Your Stack, the series where operators screen share the systems they actually run. This show has spent the past few months watching operators vault agents onto CRMs that were never built for them. My guest today works at the company making the opposite patch. Clarify is the Autonomous CRM, an AI native system with a built-in agent that handles meeting briefs, data entry, and pipeline updates without being asked. The company raised $22.5 million in its first year with backers including USVP Gradient and Madrona Madrona. Michael Bartimer is Clarify's GTM engineer, a self described RevOps, Swiss Army knife, and today he's showing us the most credible tests any tooling company. How Clarify runs on Clarify. Real workflow, real data views, anything confidential is blurred, not staged. Let's see it. Michael, welcome to the show.
speaker-0: Awesome, thanks for having me, Rick. I appreciate that.
speaker-1: You're booking so today you want to talk about GTM auto automations and agents. Sorry about that. Noise. Check this then. Okay, so Michael, what specific workflows, tools, and agents you do you want to take us down the road today? Yeah.
speaker-0: to to share my screen in a second. I'll just give you little little more more context and again appreciate the the thoughtful intro. We i I'm gonna show a few examples of how clarify runs on clarify. And when I share my screen here in a second, I'll just be in our our desktop app. But I mean we've we probably have over a hundred agents running inside of our instance of Clarify in in our own workspace. The few I'm gonna show today, if we have time to get through all of them are probably a little bit more geared towards I would say the GTM engineering slash RevOps audience, more so than the traditional like sales leader audience. But you know, we we obviously have numerous agents and and workflows running that are, you know, more core deal lifecycle pipeline related type of stuff. But the few I'll run through today is one website visitor outbound. I'll walk through that agent. And then maybe more on the the grow side for like PLG businesses, a a PQL scoring and and routing agent. and then happy to to talk about if we have time, kind of more enrichment stuff like free mail enrichment is another good example. that that I'm running but yeah more specific to PLG businesses. Right. So hopefully that's some helpful helpful context. But let me go ahead and move this over here and share my screen. All right. You should be able to see my screen. Yep. Awesome. So again I'm in here in our in our desktop app just in the the agents section here and and this is just all the agents in our workspace right just to scroll down like there's you can see the vast majority of them are are actually enabled. So we are are doing this for real. And and there's a lot there, but I'm just gonna go into this first one I wanna talk through is this website visitor outbound. And at the bottom here, just high this. At the bottom here we have rep, which is our built in AI that can do basically anything in in the entire system. So I use it heavily and not gonna kind of go through this agent in detail, but rather I'm just gonna jump in here and ask rep, what does this agent do? Please explain it to me. And I'm just gonna let let rep where's whisper below popping up. I'm just gonna let rep here summarize kind of exactly what this agent does. And anytime I'm I'm building an agent or workflow in Clarify, I'm just giving rep either I'm just talking to rep, either just kind of doing a knowledge dump. like rambling with WhisperFlow or I'm just pasting in a linear ticket or I'm pasting in a Notion doc for something a little bit more kind of complex or thought through. I'll give it like a PRD style Notion doc that I've I've worked with. And it will go and actually plan it and and draft the agent or workflow for me. So this is just giving me a a much higher level kind of step by step of what this agent is doing. So we're we're Pretty simply just using a vector for website de anonymization on our website. And then this is just a a webhook that's ingesting the vector payload. So the first step is it's just parsing the data from from vector. So things like, you know, name, title, LinkedIn, company name, et cetera. And then I'm doing a lot of like disqualification up front, right? So disqualifying. employees that are coming to our website, certain competitors, suppressing customers, and then we're getting into the classification of different segments and ICPs and and that's really what's happening here in step three. And then in step four, actually once they pass the ICP kind of company level qualification, then we're doing the the person level a persona resolution. So have basically I think six different personas in here. And which we'll see in in step six. But first in in step five, just writing this data to clarify from the the vector payload. And also maybe I'll I'll talk a little bit about how I'm solving the what I would refer to as like the stale data problem from a lot of these data vendors. Actually just just simply just using the LinkedIn profile from the vector payload and actually looking them looking at their profile. And that's all happening via this agent. Right. And so it actually understands what what company they're currently at. Because I've I'm sure many folks have seen numerous examples from all the data providers out there that oftentimes the data can be six, twelve, if not, I've seen many instances of 24 plus months out of date and looking at their their previous role. So this agent is is solving that that stale data problem. And when it finds like the the true like current operational company, is it what I have it referred to here? Is it just runs them through the entire workflow here again and actually writes those updates in in Clarify so we're getting the the correct data and and resolution. And then right now I'm happy to talk about this more, but using instantly right now for marketing led outbound. I think Clarify campaigns are are good for sales outbound, but certainly have gaps at the moment that we're working on for marketing style outbound. So currently just you know automating enrollment to those persona specific campaigns in instantly. And then writing data back from from instantly to clarify for for you know meetings booked or or certain outcomes for reporting. And then lastly, just posting to Slack, more of a enrollment confirmation in the thread. So we have a Slack channel called Website Visitors. You know, everyone who goes through this workflow is written into there for visibility and yeah, for enrollment confirmation specifically, j and a few other use cases, just threading and replies automatically for tracking. And here you can see at the bottom by the numbers, yeah, a lot of Lot of manual minutes saved. I'll I'll pause there. happy to to continue on with this one. Maybe we can look at a specific run if you want.
speaker-1: Yeah, yeah, that'd be great. Show us the run and if you wanna tal talk us through the results after the run. Yeah.
speaker-0: So I'm here at the sure at the top, just on the overview tab, I can always jump into the activity and I can click into any of these. So let's just, you know, click into the most recent one. And this is just gonna give you the the run summary here at the bottom. But if I scroll all the way top, I can actually see, you know, the the the thinking and what actually happened step by step, right? I could drill down and click into any you know, any of the tool executions, any of the reasoning. and see what the agent did here. But for, you know, verification it's you know, the run summary at the at the end is gonna be the the most useful.
speaker-1: And and how easy is this to install this onto your say your your website?
speaker-0: I would say pretty easy. I mean I think we had it s t I mean once yeah, once you go and buy the vector in this case, I think we had it stood up in a day or a couple of days.
speaker-1: That's great. And so this is great because you know the sales team it's hard for the sales team to dynamically in real time identify who's coming to the site and then create a personalized email or even send a LinkedIn connection request, maybe in parallel. And with this automation with this webhook, companies are able to install that kind of a signal based outbound automation. Whereas traditionally it wasn't really possible, I want to say like two t two years back or so, it started becoming more and more accessible through these webhooks. And it looks like now you've made it a lot easier to install for any size company. Yeah.
speaker-0: Yeah. Yep. Absolutely. and I I mean I think this is just whether you want to call it a a a play or or or or whatever. I think this is just the first like basic simple use case that any team can stand up, you know, within a few days, within a week. Even just to I think there are tons of companies out there that just don't have visibility into folks coming to their website and just streaming that into a Slack channel just so you can see visibility is I think a great step one for folks. and then you can kind of do outbound to you know, certain people. And and just to give you some sense, maybe not specific numbers, but I I think I think most companies would be very surprised, like if they listed out their top four, five, six personas within their couple I C Ps. I think the vast majority of companies out there, a lot less of their website traffic would be in inside of those ICPs and personas than they would expect if that makes sense. So
speaker-1: That's a good point. I mean at the same time though, don't forget, like unless you're you have like a paid advertising budget, your website's not getting a lot of traffic to begin with for I want to say ninety nine percent of the B to B websites out there. And so you know, you need to have certain motions in place before automations like this really take effect.
speaker-0: Yeah, that's a great point. That's a great point. that's actually a great segue. Let's talk about like PLG and and PQL scoring as the the next agent specifically. So I'm just gonna do the the same thing and just gonna clear this new chat. And you can see it automatically pulls in the the agent I'm on as as the context. So I don't have to tell it to look at a specific agent. And I'll just ask it the same question. What does this agent do? So this is an example of something one of the first things I I I think I built at Clarify when I joined about four months ago. And this was one where you know I've started in I mean, just in in cloud code, it did a lot of analysis. You know, the output of that was a notion doc that I used for collaboration to get feedback from the team. You know, after a few quick like turns on that doc, just gave that notion doc to to rep here and it it built the agent in in minutes, right? And This is one where, you know, using a lot of product usage data, and like I would say half of these data points are a tool called out to post hog to to get that usage data, and the other half are just in clarify and in properties. So really you can kind of build it however you want or or use whatever tools you want. And in this case, just firing on certain types of of deals that are PLG deals, right, through website signups. And just using kind of seven signals in here, all different, you know, product usage data points to just get to a a score of one hundred. And yeah, pretty pretty simple I would say here.
speaker-1: So what does that score do? Does that sync with the C R and then notify the sales rep to identify and then start a conversation? Yeah.
speaker-0: Exactly. This one is just scoring all the, you know, all the website signups, the PLG website signups. And then this next agent over here is the one that actually does the routing. So let's just say, what does this agent do? So this one, yeah, does the routing into I think three or four different tiers. the first one is immediate immediate reach out by sales. The next one is yeah, so here are the four paths. And this is where we we actually have a human in the loop step. And currently the the sales team just wants to do a quick review. I mean, they do literally a a 10, 20 second review of the account and just one click enroll them in their sales campaign out of Clarify. So that's that's path one based on some firmographic data points. and then path two is all about you know trying to get them to that PQL score of sixty or greater. in order for them to be classified as sales ready. Right. So this is really, I would say, a lightweight V1 of what like a product led sales. PLS motion would would look like on top of PLG data being like very easily accessible by by agents.
speaker-1: Yeah, this is great. Traditionally you'd have to layer in product analytics and then figure out a way to route that to like your your main CRM, which in most cases can be like a Salesforce or a HubSpot. And then yeah, I mean connecting those two together it was a lot of steps and a lot of work, whereas it looks like you've really boiled this down to a very simple embed here.
speaker-0: Yeah. I mean the the thing that I love about like being, you know, a a GTM eng engineer, like slash rev ops background at Clarify is like I I can use Clarify for the pipes of basically anything I'm doing. And you know, where i we can maybe talk about campaigns a little bit. I mean that's outbound is obviously a big focus of of GTM engineers and something I'm I'm working on at the moment. and as I kind of alluded to earlier, like it's Cl campaigns in Clarify much more suited at the moment for sales led outbound rather than marketing led outbound. But this is, I mean, one of the reasons why I I love doing this, not just because it's so easy to build and iterate and speed to deploy, I think is a is a really key advantage. But I'm also able to like you go out and use the tooling and vendors that you know most folks are using and build use those to build workarounds into Clarify and then work with our product and engineering teams. to to actually close the gaps. So yeah, I would say super exciting. And in the last, you know, four months since I've been at Clarify, like it's been really eye opening to me in terms of how easy some of this stuff can and should be, where historically even six months ago, yeah, even six months ago, like this stuff was I was doing this outside of Clarify in HubSpot and And, you know, various workflow automation tools, and obviously cloud code, but so much of this being natively consolidated in a single system, specifically in the system of record, makes building and speed to deploy and iteration cycles so much faster and easier.
speaker-1: Yeah, absolutely. Michael, thank you for showing us the real system, not a demo environment. The actual stack running live, deletions and all. Well did you show us any deletions? Not really, right? Right,
speaker-0: No, no.
speaker-1: you didn't. Right. I'm gonna scratch that out, true.
speaker-0: Okay. I mean, these were like V1s of of different use cases. I mean, for website visitor outbound, I think our team was using something else like last year before I joined, but it didn't really work too well. But it's rather than deletion, I would refer to it as like consolidation of workflow automation in general for for what I mentioned a minute ago in terms of ease ease to build and and speed to deploy and iterate. Right.
speaker-1: Right, right. I like that w way of putting it. Michael, thanks for showing us how Clarify consolidates different workflows and tools into one unified interface, really making everything run smoothly and a lot more efficiently. Clarify's YouTube channel runs deeper walkthroughs for anyone who's interested, go check it out. And if this episode changed how you think about the automation layer, subscribe to GTM Vault. Share with your network. And join us next week. Build systems, not noise. All right.
