Fuel Your Agent Swarms with Orchestration and First Party Data

Julia Nimchinski:

Thanks. Jonathan Carford, Head of GTM Growth, and Jordan Nettles, Senior Sales Engineer from One and Only Momentum. Welcome to the show, and how are you doing?

Jonathan Kvarfordt:

Good, how are you, Julia?

Julia Nimchinski:

Excited to dive into this!

Jonathan Kvarfordt:

Yes. I’m always excited to go into it. It’s awesome.

Julia Nimchinski:

You know what, I’m gonna ask.

Jonathan Kvarfordt:

Tell me what you’re gonna ask.

Julia Nimchinski:

So, there are a lot of, you know.

transformation, futurism, and it just… it’s been a heavy year in terms of the transition to AI, identifying everything. But curious, what are you seeing there in the most cutting edge, one of the most cutting-edge companies and hottest tech out there, Momentum, your customers, I mean, your team internally.

In terms of ROI and some case studies and, you know, specifically the concept agents forms, have you been playing around with that? What about your customers?

And yeah, curious to hear.

Jonathan Kvarfordt:

Yeah, I, I mean, you know that I’m kind of an AI nerd, so I’ve been definitely playing around with it, and then with the team, we do various different things with swarms, but, the concept’s kind of still new, I think people are still applying it, you know? Like, there’s… people are still figuring out what… what is deep research, which is like a swarm-like concept when you have multiple agents doing the same thing for one… one factor, and then… I just served this other company who is launching a swarm of deep research agents, so it’s like a swarm of a swarm, so it’s crazy, the things you can do pretty quickly. I think people are still figuring out, though, where you can aim that power, and where you can trust it, just like any other AI technologies, because… it… you can’t just apply it to everything and have magic happen.

It has to be applied in the right context and reason, you know? So for us, I mean, I’ll let Jordan talk in a second, but for us, a lot of the times, any AI capability is best used aimed at the fundamentals, just because most people don’t even have the basics, like the… automating the sales process.

We just released a report last week where we talked about How… McKinsey talks about there’s 78% of people have done some sort of AI adoption, which usually means, like, a co-pilot license, or cursor license, or ChatGPT, but very few have done what we call operational AI, so we did our own study on our own sales calls over the last 6 months of 1,000-plus calls. people of 50 people teams all the way up to 10,000 plus, and it’s like 7.6% of all people we talk to actually have what we call operationalized, or where they’ve automated some part of the process. So it’s like… the capabilities of AI is so, so far, but the reality is that most people are way behind the capabilities, so a lot of times, just doing some of the fundamental basics of time savings is, like, a huge lift for teams.

And to me, it’s kind of essential before you can start doing some of the fun stuff, because then when you get to the fun stuff, like swarms, a lot of really, really cool stuff can happen.

So, what do you think, Jordan?

Jordan Nettles:

Yeah, I think that’s a pattern we’ve seen really across our customer base, where it’s a slow burn. It’s not something that you immediately get to, you know, you buy a new tool, or you start to really invest in your own internal builds. We don’t see that changing overnight.

It’s not a… It’s not a switch you can flip and then reach value, it’s understanding, like you said, the fundamentals of your business. What are the problems we’re seeing?

What are we actually running into that’s stopping us from achieving X result?

okay, today I’m going to take apart this process and get this data in place. You know, one of the things that’s interesting about swarms is they’re so reliant, and we’ll talk about this a little bit later, they’re so reliant on actually getting the data in the right places, and that takes time, and… What we do see whenever you have a customer who’s gone through that maturity model, they’ve taken the time to get the right information in the right place to solve the right problems. at that point, that’s when real value is met.

We have very rarely seen someone go through that journey and not reach true value articulation. So that’s something that we’re really excited about, is partnering with customers as they go on that journey.

Julia Nimchinski:

Super excited to see it in action, but before I do that, Jonathan, I can’t help but ask you, why release a book? Tell us more. What’s the premise?

LinkedIn, yeah.

Jonathan Kvarfordt:

Full of that. So, it actually was not my idea, it was our CEO’s idea, so one… one night in June, he was up at, like, it’s gotta be 2 or 3 AM his time, he was geeking out with AI, and so he slacked me, he’s like, hey, are you up?

And of course, I was geeking out with AI, so, like, yeah, of course I am!

So, we just talked about this idea he had, and I didn’t know what he wanted, because I thought originally he wanted me to write the book.

I’m like, that’s very, very nice of you to ask this, but when am I going to have time to write a book? So… And then on Monday, we chat, he’s like, no, no, no, I don’t want you to write the book, I want to interview people who can then help us write the book.

So it’s kind of like the concept of chicken soup for the soul, but it’s AI for go-to-market.

So we talk to people we admire, respect, who people we know for being a customer and advisor, they’ve done really, really cool things.

There’s 20, 30 people, something like that, 25, 30 people who we’ve interviewed. So he interviewed some, I did too, and then I took it all and found patterns.

So I listened to all the interviews.

Obviously, I used AI to have AI’s opinion on what the patterns were, and we came up with this concept called, intelligence architects. So it’s really understanding the premise of the book is really about AI’s not here to replace humans.

AI does things that only AI can do, and there’s things that only humans can do, and it’s really understanding from a leadership and architect’s point of view of, like, where do you aim the power of humans, and where do you aim the power of AI in the best way possible? And I feel like the principles there are pretty, the contributors did a really good job of talking about, even if all this crazy thing starts to happen in the next 2 years with swarms and all sorts of stuff, what are the consistent principles that need to apply regardless?

So that’s what we kind of wanted to focus on, so it’s not going to be outdated in 6 months, it’s a good… core foundation of what you can look at for any AI strategy, which is pretty cool.

So, anyways, we’re really excited about it, love the contributors’ thoughts, and I’ll send you a bucket, Julia, so you have to tell me your address, I’ll send you one.

So, yeah.

Julia Nimchinski:

Awesome. We’re gonna make sure that we’re gonna send it to our community members as well, and the link… Jordan, let’s dive into it.

Jordan Nettles:

Yeah, Jonathan, you want to go ahead and cover the first… the first slide, and then we’ll… we’ll kick it off.

Jonathan Kvarfordt:

Yeah, yeah, yeah, let’s do it. So I’m gonna go over a couple of concepts to kind of warm up, everyone to the, to the, obviously, the category.

But as we get into it.

A few things we want to make sure we are aware of, which makes sure, hopefully, my lovely thing will work. You see my screen? That’s the wrong one, Julia, not that one.

Sorry about that, everyone. I promise I’m not technologically incapable, I promise. Okay, hopefully this will work.

Alright, see my screen okay?

Julia Nimchinski:

Yep.

Jonathan Kvarfordt:

So our… our thesis is we believe the most viable data that anyone has lives in the daily interactions with prospects and customers.

Like, the research I just talked about came from Momentum Zone Data, listening to our sales calls. And I bring this up because when you talk about swarms, all this information and data sits within some sort of CRM, your calls, your emails, all this sort of stuff.

And so, Momentum, at its core, uses, I guess you could call it, like, a swarm concept, so that every single call that happens, you have, like, this grouping of agents that are filling in individual data points.

So, just as an FYI, there’s something like 38 different unique types of fields inside of Salesforce that a human has to enter in for an opportunity.

Most technology can only do… half, usually, of the fields possible. We do almost pretty much the entire set. So the human never has to touch the data going into Salesforce, which is nice.

You have that side of it. And then the other side of it is we have, on, like, signal agents that go over and then communicate in multiple places based on whatever the topic is, like a competitor, or a churn risk, or whatever the topic might be.

So, there’s this concept that we can feed all this information simultaneously, and then orchestrate it so it all gets consumed in one place, so it’s not just… random messages flying everywhere.

It’s like it’s, again, like the term orchestration, the music’s playing, or the right people are getting the right signal at the right time for the right reason, which is fascinating. The next concept, which Jordan will talk about, is this one, where it’s a behind-the-scenes, so it’s not the, I would say, probably not the sexiest thing people want to talk about, but it’s really important, which is the multi-validation levels of data, so that instead of, like, what most people think, which is just running a transcript, get a prompt, and you get an output. Momentum goes through 8 or 9 layers of different concepts, so that the swarm in the back end is cleaning out the data, validating it, and making sure it’s what it should be, so it can then put it into CRM, because the last thing you need is a whole lot of data in your CRM that’s wrong, inaccurate, or incomplete.

So we take a lot of energy to make sure that the data coming into it is more accurate than it would have been before.

So that’s kind of like how we use swarms.

It’s a lot in the background, and most of the time, if you’ve used a deep research product, a lot of the swarm things you experience are in the background. Like, it’s… you experience it the same way like anyone else does, but the output’s way different. So, does that help kind of sit the ground a little bit?

Is that okay?

Julia Nimchinski:

Super impressive, yep.

Jonathan Kvarfordt:

Cool. Alright, Jordan, you’re up, bud.

Jordan Nettles:

All right. Well, thanks for that, Jonathan. I’m gonna go ahead and dive into some of the different areas that momentum affects.

Jonathan’s exactly right in how he thinks about The data infrastructure being valuable. You know, since we are true go-to-market specialists, we’re able to configure our infrastructure to solve a few core problems. So rather than every single time that we are, you know, integrating a new source of customer communication, a call, an email, service ticket, anything of the like.

Rather than just taking that in and then repurposing that content and sending it somewhere else, we’re able to perform a few processes. You know, how can we make sure that every objection that ever makes its way in front of your team gets actually put together and jotted down as a data point? You know, how do we make sure that every single time a… A customer is giving you praise and saying, hey, this is amazing, I love exactly what you’re doing, don’t change a thing, this is exactly what the market needs.

We take that information and we jot it down, and what that allows us to do. Is every single time we want to send information to somewhere in your business, we’re able to pull together, not just a transcript, not just an email. But a highly validated, dense dataset that we can draw from for whatever we need.

So literally, behind the scenes, we have thousands of agents that every single time we have a new transcript or a new email come through, we’re running it through several top-shelf models to say, hey, are we sure this is an objection, or was this just… an allusion to, you know, a holiday plane that went south, or are we sure that this is a competitor, or are they just talking about scanning the market, or a friend that works in the industry?

And because we’re doing all of this behind the scenes at the infrastructure layer, we’re able to get to some interesting things, and we’ll deep dive that now in kind of a demo.

I’ll go ahead and share my screen. I’ll walk through a couple of concepts in Salesforce, could just as easily be Snowflake, could just as easily be Databricks, it’s wherever we need the information flowing. And we’ll cover a little bit from the Salesforce side, we’ll cover a little bit of the Momentum app, and, you know, what we’re doing behind the scenes, we’ll spend some time in Slack, and showcase where this information ends up.

So, high level.

Think about Momentum being able to pull in context-rich information to your org. everyone’s got a sales methodology, it’s usually something like MedPick, or Challenger, Command of the Message. This is where we usually start.

Now, because we’re pulling this information in automatically as infrastructure.

A few things change. One, every single opportunity my team is working, if I’m a CRO or a sales manager and I need to get in and understand where my pipeline is at, I can actually see what metrics we’re solving for at every single deal. like, an analysis on my entire pipeline, because I actually understand what problems we’re solving for, how good we are at, you know, pulling in the right types of champions that we’re trying to target.

But it goes beyond that.

If I don’t have to rely on human as a data source, I can not just get information that they might have been responsible for capturing prior, but I’m actually able to pull in that information that I couldn’t get from them.

Right? So, if I’m able to see that You know, in 7 of the 10 opportunities that a sales rep is working, that there’s no decision process.

It no longer becomes an issue of, well, are we just not writing this information into my system? I can actually see that we have a gap in understanding decision process, and this is something to attack. Now, momentum behind the scenes, in order to get to this point, has to take in all of my customer-facing communique, which is done at the infrastructure level.

It has to pull in all of the different validation steps and run this through the data refinery, and that also happens at the infrastructure level. And then we have to go and capture all this information.

Some customers have as many as 100 of these, 100 of these data points being collected from every conversation. Now, in order to get to this point, that’s thousands of agents we’ve deployed to actually clean the data and get it where it needs to go.

Now, from there, you’re able to do some incredible things.

Some customers are just giving us their entire sales process and saying, hey, can you automate my sales stages? Right, so if in order to be in stage 2 of an opportunity, I need to be listening for, in every conversation, my exit criteria to get to that stage. Well, okay, great, do we have a pain point we can solve for, an actionable next step on the calendar, and a pathway to a champion?

Well, great, I’m in stage 2 of my opportunity now. And so when we go downstream, we can actually see something like, okay, great, well, in order to be in stage 5, here are my next best actions according to my own sales process.

So think about just Momentum constantly reviewing all of the context from a customer-facing communication, and marrying that with information from your CRM, and putting that together with your sales process to make sure that the right people are alerted in the right ways.

I’m capturing that data and moving it along. one of the things that, you know, we want to focus on is concurrency, right? How do we activate hundreds of agents to go and perform all these tasks?

You know, in parallel. One thing I can do to highlight this is showcase how we and how other customers are using this in Slack. So the same conversation might need to make its way to several different altitudes in my organization.

The product needs information about what’s happening on the front line so they can make good strategic decisions. Enablement and sales leadership want to train their reps on how to perform better in the field. Think about all of this information being from the same conversation, but having many, many different purposes.

Well, momentum. It can listen to every conversation, and it can tell you Hey, product, we have a new product request that just came in. Here’s the 30-second snippet where they actually talk about what would move the needle for them as a potential customer.

What’s going to get them over the line and make sure that you know, they’re enabled for, you know, a partnership together with the customer.

So not only are we taking the conversation, we’re actually preventing you from having to go and source out this information.

My account manager isn’t bottlenecking this information anymore, it just sources to the right place. You know, and from there, what we’re able to do is actually Send, from the same conversation. Hey company, here’s an example of where, you know, the customer’s extremely excited, they love working with us, they say, hey, this demo was amazing, I expected a lot, and after seeing the technology, I’m even more impressed than I thought I’d be.

If I’m in marketing and I need to understand how people are finding their, you know, how different customers find their way to us, I can actually make sure that I understand the funnels, right?

Every single time someone’s on a call and they tell me, oh, I joined because of a referral from X company, or this partner was actually a big part of me landing with you all as a potential customer. If I’m in enablement or sales leadership, and I want to, you know, actively score how we are… how good we are at pitching momentum as an overarching product, and, you know, how are we doing with our elevator pitch, maybe that’s a big point of focus for us this quarter.

I can automatically score this information, input it to my system of record, my BI tool, so I can visualize trends over time. But I can see, actually, it looks like I was pretty poor yesterday in structuring my approach.

You know, I gave an elevator pitch in something like 10 seconds. Now enablement knows what to come after me on, and say, hey, by the way, what you need to do is work on this, this component.

All of this is sourcing information to the right place at the right time. All of this can be from the same conversation. This is happening in real time, 10 minutes after the call gets over.

Each stakeholder in my business gets exactly what they need. They don’t have to source it out. An agent delivers each different altitude in my business exactly what they need to see.

We’ll cover a couple more things, one being, you know, so far we’ve talked about getting data points and visibility into an opportunity or into an account, but… you know, how do we start to see general trends, right? If I’m a CRO, how do I get what I need served up?

If I am a manager, how do I get what I need served up?

What I’m able to do is go a little bit beyond what we’ve shown so far.

Okay, once a week, deliver me a review that takes me through every single early- stage opportunity we’re working and how it’s progressing. Tell me about Champion Strength and how we’re… how we’re doing identifying compelling events. Right, so as soon as I need to collect this information, go through and scan, you know, this data set that Momentum is putting together at the infrastructure layer, which relies on a swarm itself, and then put all of it together, go search out hundreds of different data points and calls, and then, you know, tell me how we’re doing late stage at getting in front of an economic buyer.

Tell me what we’re seeing in terms of risk in accessing these… These EVs. If I’m in product, and I want to be able to make a strategic decision.

Right? Which integration should we build? This is one of my favorite stories to tell internally, but we were looking a few months back, you know, should we be building Microsoft Teams, or should we be building HubSpot?

For a long time, we’ve been Salesforce and Slack exclusive, and we wanted to branch out. We knew we had to. And so we used momentum.

We said, hey, look at every opportunity that we’ve worked last quarter and, you know, comb through all that information and piece together, an analysis that helps me understand my… Helps me understand my pipeline. And so, within a few minutes, I have, you know, a thousand agents coming through every single opportunity, picking out different pieces of information every time that Microsoft Teams ends up being a blocker, HubSpot ends up being a blocker.

And effectively, that lets me get to strategy.

I can say, okay, great, well, I can see that, you know, there’s 135 deals that HubSpot ended up being a blocker on, but Microsoft Teams actually had less deals. But… the actual pipeline amount behind each of these products was significantly different.

In fact, even though there were less deals, there’s about twice as much pipeline behind Microsoft Teams. I can go after my IACP, I can make sure that I’m maximizing for pipeline, making strategic decisions, only because behind Momentum, I have all these agents that are running and running and running and gathering all the detail. And composing it in a way that makes sense.

Last thing I wanted to share today before we, kick it back to Jonathan and Julia is, you know, one of the things that we’re releasing right now is our deep research. We just, we just announced that last week. think about being able to ask in real time any sort of question, that’s relevant to my organization.

In this case, you know, help me understand why I’m losing deals at Stage 2. And Momentum’s able to comb through all of this detail.

It’s able to say, okay, great, look through all of my opportunities, and only tell me which ones are close loss, and then tell me, okay, which ones dropped off on stage 2 or Stage 3? Go through and understand all of the different cohorts, all the different sizes. And what this lets me get to is something that’s… that’s quantitative, right?

It helps me understand my pipeline. If I wanted to get, you know, actionable recommendations based on you know, how we are at qualifying in something like a BANT framework.

I’m able to get all this information, comb through thousands of opportunities, and understand how good are we at soliciting budget. How good are we at understanding authority and the needs of customers? And then from this, I’ve got a data set that’s actionable.

I can go through and I can see every single opportunity that’s in my system and have it be scored automatically, and, you know, understand, did we win the deal, did we lose the deal, what’s the amount? It’s a way for me to quantify problems. And again, with all of this, it’s only possible because I’m able to deploy hundreds of agents at the same time, as opposed to just one or two wrappers that goes through and feeds a transcript and, you know, gets detail back, or feeds an email and gets detail back.

You know, all of this, as I’m sure, you know, everyone watching has seen today, is really only enabled because we’re able to review thousands and thousands and thousands of actions at once using swarms. Jonathan, anything you want to add there?

Jonathan Kvarfordt:

No, mic drop, Ben. I’m gonna not say anything, that was great. Julie, if you’re talking, I can’t hear you, dear.

Julia Nimchinski:

Oh, thank you.

Jonathan Kvarfordt:

You’re welcome.

Julia Nimchinski:

Amazing. You can be the host. Jonathan, and Jordan, you’re really on the cutting edge of innovation and experimenting with so many tools.

Curious, what’s the, I don’t know, most impressive use case so far in terms of de- siloing GTM?

Jonathan Kvarfordt:

Jordan, you, man, you have good examples.

Jordan Nettles:

Yeah. Yeah, so one of the things that we noticed in the last, I don’t know, 3 or 4 months of taking this aggregation to market is everyone really only has 6 or 7 problems. Why are we winning deals?

Why are we losing deals? What are the product gaps?

What are the things about our products that make us win?

Why am I losing between Stage 2 and Stage 3 competitive research? Basically, every customer is asking one of these 6 or 7 different questions, and what’s exciting about that is you can build your technology around it. So if I can say, hey, here are the 10 problems that all go-to-market teams face, or all go-to-market leaders want to know the answer to.

Then I can say, okay, let’s build deep research specifically around answering these types of problems. Because when I’m able to do that, I’m able to make sure that I’m meeting the market where it is.

Now, these things evolve over time, you know, the problems that we’re solving today, you never could have imagined AI could solve 3 years ago when it was just as simple as, you know, give me visibility into a conversation my sales rep just had.

So it’s exciting that it’s always evolving, but right now, the challenges we’re seeing in the marketplace are really, you know, one of those 6 or 7, because everyone wants that insight.

No one could get it before, and it’s really only possible because of this sort of technology that’s just meeting the market today.

Julia Nimchinski:

Jonathan, how about yourself?

Jonathan Kvarfordt:

I mean, Jordan did a better job than I could talking about this, but, Yeah, I think we don’t understand truly yet what power we have at our disposal. I remember a couple years ago, there was a guy from one of the AI companies who said that it would take us 2 years to understand what GPT-4 at the time would do. That was back in 23 when it first was released.

I feel like we’re in the same place now where we have so much capabilities and possibilities, we’re still trying to figure it out, so I think… The wisdom is understanding and being willing to try, and just ask the question, I wonder if I could try this with AI, whether it’s swarms, agents, or automation of some kind. Having a company who’s willing to do that, obviously in a safe environment, will be the cutting edge and will push things forward, which I love and appreciate about Momentums, because we’re constantly doing that, just trying to figure out where we can go, and unfortunately, we’re very blessed to have customers who think the same way, so we have a lot of them coming to us saying, hey, I wonder if we could try this thing, and we’ll try it, and it’ll work awesome, you know? So, I think being willing to see the capabilities and then push forward is where a lot of the magic can happen, which just takes… The difficult part is it requires someone who understands their business inside and out, so that AI can be, applied to the business specifically, instead of the business trying to be applied to the tech.

Does that make sense? There’s a big difference between those two things. So, that’s what I would look for, is that.

Does that help answer the question, hopefully?

Julia Nimchinski:

Absolutely, and speaking of the trials, we tend to showcase, you know, success stories, the results, the… all the best and brightest, but curious, what are your lessons you’re allowed to share from some failed deployments and, I don’t know, unsuccessful, agentic swarms?

Jonathan Kvarfordt:

You said unsuccessful?

Julia Nimchinski:

Yep.

Jonathan Kvarfordt:

Oh, good. Joni probably knows it better than I do, too. I’ll let you talk.

Jordan Nettles:

Yeah. What we tend to see be a failure is if we don’t have a clear outcome in mind.

So, Jonathan, at the beginning of this conversation, was being.

Jonathan Kvarfordt:

Yep.

Jordan Nettles:

you know, gave some… gave some wisdom around, you have to start with the business fundamentals. What am I solving for?

What am I looking for?

you know, I can’t throw an AI… an agentic swarm at a problem that I don’t know Really, the definition of. So as we, you know, look to solve different business use cases with AI, we want to make sure that we’re focusing in on, okay, what is the actual problem we’re looking to solve?

Great example of this… That we hear all the time, can you help build me a, you know, an opportunity, opportunity score? Right? Something simple that says, hey, how, how likely is this customer to.

To buy our product. What ends up being difficult is… You know, these models are built up of a few different factors, but if you have no idea what the positive factors are, we can start to get at some of this detail now with deep research, like, tell me what my ICP actually wants from us.

But historically, if you come in and you say, hey, I just want the AI to Tell me if it’s some… if it’s going… someone’s going to buy or not.

That’s when I see big red flags from any kind of deal cycle, that I’m working, where it seems like it’s just not possible to get to, you know, a positive solution.

Jonathan Kvarfordt:

I’d say the same thing, that there’s a lot of people who feel like because of the promise of AI, like, it’s… it seems easiest to get an NAN playbook on some LinkedIn post, and then to apply it, and then make sure this magic happens. Like, it’s not as easy as you think, and there’s… most of the people, and we kind of got this from the book as well, most of the people who get really true power of AI are doing a lot of what we call unsexy work. Writing down the documentation of process, making sure the data’s in place.

It’s that stuff that AI and swarms and everything else you can think of when it has anything to do with AI, thrives on, is having that, but it requires the humans to guide it, and if you don’t have that, and you expect AI to just be plugged in and fix everything like that, it’s just… it’s not going to work that well. So I think a lot of the failures, kind of like what Jordan said.

misunderstood expectations of what is required to make it work well. And when you do know those things, and you’re willing to do the work, then, to me, I always tell people it’s like a cat, like a… like a catapult, like a… what’s the word?

Slingshot. Like, you feel like you’re going backwards, because you’re pulling, pulling, pulling, and you’re literally going backwards, but as soon as you let go, you just skyrocket and fly really, really quickly, but it requires that feeling of… I think I’m doing the slow, unsexy work of, like, what’s our documentation? What’s our process?

Where’s the data? But once you have that, everything just falls into place.

Julia Nimchinski:

So, Jonathan, I can’t help but ask, what’s in your tech stack?

Jonathan Kvarfordt:

In ours, internally?

Julia Nimchinski:

In your, personally, yeah.

Jonathan Kvarfordt:

Me, personally?

Julia Nimchinski:

Yeah.

Jonathan Kvarfordt:

Oh, jeez, I have a really, really long list. So I mess with a bunch of stuff.

Oh, yeah, top 10. Let’s get a hard one.

So, I’ll tell you 4 or 5 from… I use Momentum that I love, and then 4 or 5 from Personally.

So, I’m a big fan of Relevance, Genspark, Manis, I use those like crazy.

Of course, the Classics, I’m a big fan of what Claude’s done with new skills. The skills thing is, like, awesome. Like, I’m still mind-blown with skills.

And then on Momentum’s side, we use… gosh, Jordan, we use a lot of stuff.

We use, Apple Markets, one of our tools for the SCR team. I use, Peak, which is an AI platform to track different things on AI search. gosh, there’s so much stuff I use Vector, Vector.co for our website and other automation, so there’s lots.

Just depends on what factor you want to go into, but for me, relevance, of course, Zapier, Mind Studio’s another one, Mind Studio’s awesome, and then, Madison, Jen’s work, so…

Julia Nimchinski:

Jordan, how about yourself?

Jordan Nettles:

You know, it really depends day to day. Like Jonathan said, the task. I think as much as I can do using something in… I’m a big, big fan of claw, Jonathan knows this, so anything that I can do using… Caught in the day-to-day is where I spend most of my time.

He gets a little bit more of the… You know, go-to-market engineer side of our company right now, so…

Jonathan Kvarfordt:

They would know better than I.

Julia Nimchinski:

As always, great having you here, and what’s the best next step for our community members to engage with you?

Jonathan Kvarfordt:

Momentum.io, come talk to us. And we’ll, you know, as you get up, sign up for a book, we’ll get you a book.

We have a prompt library there with 200-plus prompts that I personally wrote that has, they’re made for, kind of, the GTM use cases, so it’s not meant to… I mean, you can use them inside of Momentum, but I built them so that go-to-market experts could use them in their own world, sales, marketing, enablement, rev ops, so those are for free, that’s on the website, too, so go there, and we have a plethora of items for you.