Building AI Agents for Sales and Retention

Julia Nimchinski:

Welcome to the show, Ethan Kinnan, Linda AI. Ethan is the Head of Partnerships and With him, we have Marvin Aziz, Head of Community. How are you doing?

Ethan, you’re… Not on mute, but for some reason, the sound is not. connecting here.

Ethan Kinnan:

Can you hear me now?

Julia Nimchinski:

Yeah?

Ethan Kinnan:

Oh, sweet. Ahsan, great, great to see all you guys, and thanks for… thanks for having me. Marvin’s unfortunately woke up pretty sick today, so it’ll be just me, but that doesn’t mean it’ll be any less exciting.

We have a lot of great stuff planned for you guys.

Julia Nimchinski:

No worries. Well, excited to have you here, and as you know, I’m gonna ask you the hard question right off the bat. Lindy AI, lots of, good PR, lots of good engagement and traction, obviously.

Curious, what are some, you know, ROI stories, case studies, anything tangible, from your multi- agent deployments?

Ethan Kinnan:

Yeah, totally. Great question. I think first, you know, I think whatever… whatever use cases follow patterns, like, those are the ones that you’re gonna have, like, the highest ROI on today.

So, like, some of the ones that we see work really, really well, particularly customer support, because you can, with Lindy, pretty much integrate it to any sort of system you already have, integrate it to your CRM, integrate it to, say, if you’re using Zendesk, email, inbound text, phone, and so a lot of these, like, multi-touchpoint customer support agents are really great.

Because you always, you know, they follow the same patterns, you can always escalate to a human.

So depending on how you’re building it, those are ones… that’s one case that I think is, like, really, really good.

others, like, even, I think, even taking our own team, for example, are, for example, our Lindy, our sales leader here at Lindy, she, you know, kind of confusing, she’s a real person, her name’s Lindy, but she… she obviously sells a lot of agents, and she has, like, the most cracked agentic note-taker that, you know, manages all of her follow-ups, it updates our CRM, it logs, like, a spreadsheet with, like, hundreds of qualitative attributes that the AI is parsing. And all those things, so it’s like, any sort of things.

I guess those are… I keep going for the whole 30 minutes, but those are… those are two that we see working really well.

Julia Nimchinski:

Super cool. Let’s dive into it, Ethan.

Ethan Kinnan:

Yeah, totally. Alright, I’m gonna share my screen real quick, and, Alrighty, can everyone see my screen?

Julia Nimchinski:

Yep.

Ethan Kinnan:

You see my screen? Perfect. Alright, so it’s gonna be pretty, you know, hands-on.

I’m just, like, really going to want to go into the platform and show you guys, like, how easy it is to actually deploy agents, because I think a lot of people, you know, you feel… agents kind of feel reserved for engineers, but, like, I promise it’s really getting a lot easier. So I’m going to show you guys… I’m actually going to build two agents live for you today, and we’re going to see the results live.

So obviously, you know, I’m Ethan, I run our partnerships and ecosystem, and, like, really excited to be here.

So what are we gonna talk about, kind of, in this workshop?

And then we’ll wrap up with some more questions if we have any.

First, really just kind of grounding ourselves on what is an agent. Then from that, I’m going to build an agent in three steps, just like a simple email agent. After that, we’re going to just quickly run through the easiest way to build agents on Lindy’s platform.

Then after that, I know, you know, a lot of people are interested in sales enrichment, which is maybe one use case I forgot to mention at the beginning, but sales enrichment and Extracting a ton of information and figuring out how to communicate with people at scale is something we see works really well. And then lastly, just want to give you guys access to everything we built in the session today.

I’m just gonna let you know how to access that. Alrighty, but diving into it. So, what is an agent?

You know, so I like to think about the spectrum of automation to AI agents, and so if you think of automation, it’s kind of traditional rules-based, you know, if X happens, then Z happens, you know, everything’s really fixed. Whereas, like, AI agents all the way on the other side, where, you know, the agent is picking and choosing the steps to get there, whereas, like, in between an AI workflow is somewhere in the middle, where you give the AI some control in certain steps, but a lot of things are still fixed. And so, Lindy, we build primarily in the AI workflow and the AI agent side.

You know, don’t need to memorize everything on this chart, but I think it’s just helpful to kind of set this basis before we dive into the product. And then what is Lindy?

It’s truly the world’s easiest way to build AI agents, and so we build a lot of things on the AI workflow and the AI agent side, and so kind of those two areas are where Lindy thrives. And it really is that easy, you guys will see here in just a second.

So with that, I’m gonna build an email agent in 3 steps.

It really is that easy. Then maybe we can pause for… see if anyone has questions after that, but let’s just jump right into it. So I jump over here… We can see, you know, so this is our… Our Lendi platform, this is our home screen.

So there’s ways, you can actually just build agents by describing them, which is really powerful, but I’m gonna show more of that later.

I’m gonna show how easy it is to build from scratch, so I’m just gonna open a new agent. So, if we start from scratch… Now, if you have built you know, on other automation platforms, or, I don’t know, maybe some people like Zapier or kind of other things, like, this kind of flow editor interface is probably, familiar.

If not, you know, I just encourage you to really think about the work you’re doing in patterns and steps.

Like, really, it’s like everything’s a pattern, we’re configuring it into a step. And so in this case, I’m gonna build an email assistant.

So I always like to name my agents off the bat.

So again, this is our flow editor, so this is where if you’re configuring any agent on Lindy, you’ll just get taken to a screen like this, and then very first and most importantly, we need to give our agent a trigger, which is basically just telling our agent, hey.

Like, this is… this is how you wake up. You know, there’s a lot of ways to make your agent wake up, but in this case, I’m gonna build an email assistant for myself, and so I need the agent to wake up whenever it receives an email. And so with that, I’m gonna click on Gmail.

Email received. So this is our action picker.

By the way, we have over 5,000 integrations, so odds are, if you are, you know, using something, it integrates with Lendi. But in this case, gonna keep it pretty simple.

Email received. So this means my agent’s gonna wake up anytime that it receives an email.

I know I’m just kind of throwing you guys in, I always like to just show how easy it is, and then we’ll kind of backtrack and explain a little more what I’m doing. So for thinking in steps, it’s really going to be that simple.

I want, every time I receive an email. I want to label it, and then I want to draft a reply. So really, I promise you guys 3 steps.

So then next, I’m going to add another action.

And if you think about, you know, email, label, reply, so I’m just gonna add the email action, or the label.

See, add label. And then I’m gonna add one more action.

And that’s gonna be sending a reply.

So you can see I added these three steps onto our flow editor, just like this.

And now… Configuring the steps, so this is much more if you think back to kind of the spectrum I just talked about from automation to AI agent.

Like, we’re building right now an AI workflow, so it’s like, we’re defining the steps, but with that being said, there is AI involved in each step, and I’ll show you how in just a moment. So, for example, when I click on to add label, you’ll see here, there’s these different fields.

Like, fields are basically just, like, blanks to fill in, like, what that step is doing. And the magical part about Lindy, which, like, clicks for people, especially if you’re used to these platforms, is AI is embedded into every field and every configuration.

So actually, if I just leave it like this, there’s 3 ways to fill out any field.

So we have, like, manual, which is more traditional, auto is, like.

the AI just magically autocompletes it for you, and then prompt is, like, I’m actually talking to the AI to fill it out. So, for example, in this label field.

I’m gonna instruct it how to go, so I’m gonna turn it into a label. So please do this real quick.

Give me one moment. It’s gonna copy in a prompt. Just like this.

Now I’m done.

Like, my emails are gonna get labeled between urgent, Action, Opportunity, FYI, and spam. And then likewise, if I go to send reply, gonna leave this just as is.

And I’m going to… Prompt my body.

and I’m instructing the AI of how to actually reply to the email.

So this is where it’s like, if you have, like, a specific style to reply to your email, you know, go in and make this your own style, but I copy and paste what works for me, which is always, like, concise for professional, keep it brief.

And just like that, I’m gonna save.

I know we’re flying through it here, but just really want to show you guys how easy it is, like, bam, we have our agents. I know it may have been fast, but I want to explain what we did Once again, real fast, then we’re gonna watch it run, here live. So, I built more of an AI workflow in this instance.

Every time I get an email. I’m gonna add a label to that email, and then after that, I’m gonna send a reply, or draft a reply. And so now… If we look at our tasks.

Or, sorry, flow editor, so you can see email, add label, send reply, and the nice thing is AI is infusing all these steps, and so AI is actually drafting the response. AI is assigning the label based on the prompts I just pasted in. So flow editor is, like, where you configure your agent.

Tasks is where your… you can see, kind of, the run history of your agent. So if I send myself a quick email right here, I will see it come in, if you give me one moment.

sorry, just a second… Send. Maybe I’ll refresh this real quick. Let’s see.

Then we’ll see the task appear on the left here. So you can see I just received an email, so my agent just woke up because of that first block.

And then now, on the second block, so you can see it was a pretty simple email, you know, I really enjoyed the recording, or I really enjoyed today’s workshop, could ask, can you send me a recording?

So you can see… The AI then went to the second step.

and it looked at… it added a label, so now my label… my email in my inbox is labeled Action, based on the criteria I selected.

And just like this, I already have a reply, so this reply is drafted in my inbox, and I can just, like, one-click send.

You can see, hey, thanks for reaching out, I don’t have access to the recording at the moment, could you please check where it’s hosted? You know, in this case, I do have it, I am the host, so I would go ahead and edit my draft and then reply. But where this is super nice is every single inbox email I get, you know, hundreds per day, are all labeled and all has a reply drafted, so if I want to reply, I can just one-click send.

You know, so if we head back here… 3 clicks, we had an agent, like a working email assistant, that already saves me a couple hours each day. Alrighty, so now let’s head back over here. So that was, like, a pretty simple example.

Like, that was a pretty simple example. What else can you build in Lindy?

And so I want to show you guys, like, that’s kind of a base case, like, we have 3 steps, and we’re just… I just automated my inbox, just like that.

This one is, like, a lot larger agents. You can imagine, you know, when you have these 50 steps and, like, more agentic functions in there. This is actually the one I mentioned.

This is Lindy, our sales leaders meeting notetaker. So if you ever hop on a call with her, you know, and her agent handles dozens of follow-ups, it updates the CRM, it sends her, like, sales coaching tips, it enriches… we have this spreadsheet with, like, a ton of attributes.

that the agent then populates based off of different things in the sales call, like, how did they frame pricing, was this mentioned before this time? So it’s like, we’re collecting so much rich data from the meeting, just off of Lindy’s Notetaker.

So it’s just like a teaser of, you know, we showed you the basics, want to give you a little bit view of kind of the ceiling.

And so with that, I know, like, our time here is pretty short today, so I’m trying to, you know.

give you guys as much value as I can in kind of these 30 minutes, and so I’m gonna give you kind of, like, the TLDR version of just some fundamental things in Lindy, and what we just did, as well as show you how to do text agents. You know, typically we do these in, like, a 90-minute workshop, and we all do it together, but trying to, you know, just show you guys, and then I don’t… I’m also gonna send you all these agents after the session. But really quick, like, so whenever you’re building on Lindy.

If you remember, there’s AI workflows, and there’s AI agents, and so what, like, what are some of the basic building blocks for actually building within Lindy?

So everything that we built is, like, everything that we added there was a step.

So you saw we had our step of, like.

Email received, we had our step of add label, we had our step of send reply. And these are all, like, AI-based actions.

And there’s two types of steps.

So we have triggers, which, you know, they just tell your agents when to wake up.

And then this could be, like, a time-based trigger, like, hey, I want my agent to wake up at every morning at 9am and just tell me everything I need to know for the day, and so forth.

It could be event-based, like, hey, when the email arrives, it could be chat-based, which is more like a custom GPT, so if you, like, send a message, you’ll receive it back. Kind of like, you can talk to a Lindy within a Lindy platform, kind of like a custom GPT, if you guys are familiar with those. And then there’s actions, which are basically the steps that actually do the work.

So if you think of my send reply step and the add label step.

You know, those are actually steps going out and doing work. And… Within each step, we have to configure it, and so we have to, like, within these steps, we had to tell the agent, hey, here’s how I want you to decide my labels, or hey, here’s the style I want you to reply to my email.

And so we tell these, instruct these configurations through these things called fields, and there’s 3 ways to fill in every field.

So you saw, like, label name was a field, like, email subject is a field, email body is a field, and so we have to… you have to configure these so that the agent knows what to do on each step, and… yeah, fields are basically actions, or every action and step has blanks to fill in, and there’s 3 ways to fill in every field.

One is just leaving on auto, and so you’ll notice in that build, I kind of ignored most of the fields on the left. I was like, honestly, I’m just gonna let the AI figure it out, and this is because Lindy’s agents remember everything that goes on. So say I’m on, like, step 20 of, like, a crazy email agent.

The Lindy’s gonna remember the previous 19 steps, and by doing that, it can infer and fill in things on the last step. You can prompt it, so it’s like instruction, instruction-based, so you saw, like, we instructed the style, or you could set it manually, which is kind of just fixed, you know, it’s going to be that every time. And then the last thing I wanted to highlight of kind of what I think is the most powerful step within Lindy is an agent step.

And so if we think of our spectrum of, like, AI workflow to agents, these are… AI, the agent’s actually, like, independently figuring out what to do, and so you’re just going to give it a couple of instructions, and with that, it’s going to go out and figure things out.

And so I’m actually going to show you guys just this… a run of this in action, because I think it’ll make more sense first.

So if we do this, let me bounce around… So here is an example of an agent step.

So notice for our email agent, we were very precise with, like.

you know, email received, email label, send reply. With agent steps, I simply prompt it, like, hey, your objective is to enrich, like. anybody and everybody.

Like, so I’m gonna give you a name, and I want you to go on the internet and find as much information about this person as possible. So I tell the agent what to do.

I give it tools. Like, I like to use the analogy of a smart intern. So I’m basically telling my smart intern, just go research.

I could say, like, Julia, for example. And then I could say, hey, Julia, or to do this, you have a few tools.

Like, you can have access to LinkedIn, you have access to a couple, like, Google, you have access to Perplexity, which is like a web enrichment thing, and then I tell the smart intern when to stop. Like, you can stop when you generate a full background. And so what this looks like in action, just want to show you guys real quick, is if I… I’ll just type in Julia, put in a couple names here.

Let’s see, Nimchinsky… I think that’s correct, yeah.

So we’ll see.

Now our agent’s off and running.

So I didn’t define any of these steps, I just told it to go research Julia.

So you can see now, the agent is choosing to do these steps on its own, so the agent is searching for Julia’s LinkedIn.

Searching Perplexity, and I’m gonna kick off another one just of myself. As well. Until we head back… You’ll see kind of the end result.

Here’s one of me from yesterday. But… You can see eventually we get these, like, really rich backgrounds, because the AI, like, figured out… and it figured out all the way to get to the point.

I didn’t… I didn’t tell it how to do the steps, and so this is, like, way more on the agentic side of the spectrum. And so this is why lead enrichment is also, like, a really popular use case on Lindy, because you can imagine just doing this for every, like, sales lead or person you come in contact with to help prep you for a meeting, to help pipe info to your CRM, to give you an understanding of how to sell to them, or how to write cold emails that they open, all these things.

And so while that runs, we’ll let it run for a second.

I know this is running, and I’m going a little quick. Any questions popping up while this is coming in?

Julia Nimchinski:

Yep, we got some questions, but before we do that, Ethan, I’m just curious. Any customer story that excites you the most, in particular? Because… Obviously, with Lindy, you have this, you know, de- siloization with every… within every go-to-market function, sales, marketing, CS.

So, what’s your favorite use case?

Ethan Kinnan:

Yeah, that’s a good question. I’ll give… I’ll give two answers. One that I thought was, like, the most… one of the most impactful for a customer’s business, and then one that I thought was just, like, creative and really exciting.

One that I thought was really creative and exciting was, to generate signups for an event. We had a list of people, we enriched them like this, and then we built them, like, a one-on- one custom landing page. And so, if I was inviting you to the event, you would have gotten, like, a custom website with, like, information about you, like, asking you to, like, invite you to the event, and then Lindy, like, sent them an email.

Manage the back-and-forth communication, like, convince them to come. So that was one that was, like, super exciting.

Another, like, really creative one was a lot of, like, moving companies, per se, don’t have, like, websites.

So think of, like, all these people on Facebook, and so we saw a customer have a Lindy agent go find all of these customers saw websites, it actually built them a website, like, tested it end-to-end, and then… and then sent them the website, and was like, hey, like, do you want to talk?

Like, we built this website. So those are more, like, I don’t know, I kind of nerd about… about the creative ones.

Some of the ones that I think are most impactful, like, honestly, I think with AI, you can’t go wrong with sometimes, like, the boring and, like, the stuff that you just know you need to automate is, like, we see as most impactful, and so we have, a lot of customers on Lindy for their customer support. I think that’s, like, one of the most reliable and just, like, steady use cases.

And so we have some customers handling, you know, in the realm of, like.

thousands of tickets per week with Lindy, and so that just helps them be more efficient and stay on top of their things. And where I think it gets super awesome is we have, like, an AI engineering team that goes in, instead of it just being like, hey, I’m replying to you, like, we actually build the agent to take more action.

So, like, it can go process a refund with connection to your system, or it can update the customer status in HubSpot, or all these things.

Like, I think another just, like, really awesome use case that I think saves, like, our team a ton of time, and what we see with some of our customers, is, like, use Lendi for CRM operations a ton, like. you know, we always have that person on our team kind of nagging us to, hey, like, fill out your HubSpot information, like, do all this. Whereas, like, Lindy’s actually gonna get way more information, it’s accurate, and it’s, like, all recorded in real time.

So I’d say those are two of, like, the ones that I see, like, saving people the most time.

I’d say, like, I see those two things, which maybe are less exciting, and then some of the other exciting things that gives you a glimpse into the future is kind of the other end.

Julia Nimchinski:

Super cool. And our folks are asking here.

How do you ensure agents stay accurate when handling customer-facing tasks?

Ethan Kinnan:

accurate and customer-facing tasks. Now, there’s a few ways to do that, so you can actually, add a human in a loop anywhere.

So, say, before I send a customer email, I can always set Lindy to have human in a loop, where I get to review the output, and then click, hey, like.

Yes, this is good, go send. So I build in Human in the Loop, so a lot of my agents will, like, check me with Slack before taking action.

So, like, just naturally building that in.

Also, it just comes down to… Like, how you build and construct the agent. So it’s like, you want to give the agent the right information to do the best job.

And so, we have a lot of people on our team that are… help… kind of help people configure these so that the agents have all the right information to be accurate.

And I always just, like, I like kind of forking my agents, so it’s like, if you’re confident, do it.

And if you can validate it, do it.

And, like, if you’re not, like, alert a human.

It’s like, really the human in the loop is, I think, the most powerful.

Julia Nimchinski:

Ethan, how long does it take to actually build a functional sales or CS agent from scratch?

Ethan Kinnan:

Yeah, sales… I’d say, so you saw me build one in, like, 5… 5 minutes here, a couple agents here, today. It depends, so I’ve seen agents, like, some of our most useful agents, like, are really simple, like, you know, 20 minutes, just, like, meeting note-taker, then it’s going and updating things in a CRM, and it’s, like, keeping it all tidy. For customers with more complicated workflows, and back when I was on kind of our floor-deployed engineering team, and we have, like, a lot of excellent agent engineers on our team.

I’ve seen them spend, like, you know. 20 hours on agents, and those are getting, like, really nuts and bolts, but those are, like, 100-step agents, and they’re, like, really bespoke to people’s businesses, so it kind of just depends on the complexity you want.

Julia Nimchinski:

Well, background check.

Ethan Kinnan:

Yeah. Alright, so you can see what our agent pulled on Julia, and again, I didn’t tell… I didn’t tell the agent what steps to take. The agent just decided on its own.

And so you can see here, you know.

all this information, just like that. And so this is why this is super helpful for, like, a sales use case that I like, is I’ll take this information and have, like, a really awesome prompt, turn it into, like, a… almost like a psychoanalysis profile, so I know, hey, if I’m sending Julia an email, like, this is how I should, like, word the subject line so she opens it.

Or if I’m sending, if I’m hopping on a meeting with Julia, like, here are some things I can, like, keep in mind, so I’m just, like, best prepared. But yeah, I don’t know, is this… does this look accurate?

Julia Nimchinski:

I guess, I’m more curious if and how do you scale that, and, I don’t know, like, how do you scale that in, per se, in outreach?

Ethan Kinnan:

Yeah, you’re saying, how do I scale, like, this specific instance into outreach?

Julia Nimchinski:

Yes, like, integrating some insights or, you know, relevance. Like, what’s your approach to outreach, by the way?

Ethan Kinnan:

Yeah, so, like, what I usually do is I take this, and I’ll, like, log it into, you know, the easiest ways. Say I had a lead list of, like, a thousand people. You know, I could run this for every person, and so I ran the more expensive version.

This was still… This was 50 cents. I’ve seen agents do just as good for, like, 15, 15, 20 cents, if I told it to not be a little bit less relentless. So what I would do is I’d enrich, like, all those 100 people at once, and then… and then from that, once I have, like, 100 backgrounds, I can use AI to write individual copy, and maybe that’s, like, another 10 cents.

But at least what we see in, like, the reply and open rates is… is definitely worth it.

And then usually for that, I use SendGrid, like, within Lindy, Intergo SendGrid, so I use, like, Lindy to enrich these people, and then send out emails with our… with our company, SendGrid.

Julia Nimchinski:

And… What’s your pricing model, by the way?

Ethan Kinnan:

Yeah, so our pricing, so for self-serve, it’s all, credit-based. So, credit-based, you can think of one credit as roughly 1 cent, for as little as, you know, our business plan starts at $300 for 30,000 credits.

My email automation, I just… or my email agent, for example, that probably cost me 3 credits. This run cost me 50 credits, so our business plan starts at $300. If you want to work more directly with our team.

We have… We have AI engineers, so people that are honestly, like, five times better builders than me, and they actually help deploy agents in your organization, and we’ve done that already for hundreds of customers, and within that, the AI implementation engineer helps actually not only deploy agents for you, like the first couple, but helps upskill people on your team so you guys can build more yourself.

Julia Nimchinski:

Super cool. Curious, Ethan, what are you allowed to share in terms of, you know, the roadmap?

Ethan Kinnan:

Roadmap? Yeah, I think so. What’s super exciting that we have coming up.

is… what we have coming up is a lot of people, it’s, like, hard to gauge, like, hey, how are my agents doing, or how can I make a change and know that this was the best, change? So we have a lot of really exciting stuff coming around, you know, agent monitoring, and I’ll kind of leave it at that, but there’s going to be a lot of ways to, kind of manually build ways to assess your agent’s performance, as well as, like, just optimize them automatically.

So those are two things that I’m pretty excited for.

Julia Nimchinski:

And folks are asking, what’s the simplest way to start building their first agent within Lundy?

Ethan Kinnan:

Yeah, yeah. Best way to start doing it is opening up an account. You can click on New Agent, and you’ll see that there’s this Explore Templates.

So we have, like, we have hundreds of templates, hundreds of templates, and then we also have, if you head to home, you can also create agents off text.

So if I had started to explain in here, you know, I don’t know if we don’t have time to demo it live today, but I could just ask for, like, an email agent, and we’ll just see it pop up, through the conversation right here.

Julia Nimchinski:

cervical. And in terms of 2026, obviously, such an intense year with all of the events and, you know, transitions into agentic everything.

What’s your prediction, Ethan, for GTM and AI?

Ethan Kinnan:

Yeah, I think we’re gonna see… I don’t know. I think a lot of people are skeptical of agents, and, like, rightfully so, because online there’s just, like, a ton of hype and noise, and so I think, like, what we’ve seen is, like, the trend of, kind of, through 2026, is, like, a lot of the things that are more pattern-filled, like, honestly should be given to an agent right now, and just, like, a lot of people don’t.

I think as we see the models get better, you’re gonna see the agents handle things that are less pattern- focused, like, a lot better.

So you see that step as an example of something that has, like, a broad task that is more flexible.

So I think we’re just gonna see the you know, if things that needed a pattern to be automated today, things are gonna need a little bit less of a pattern to be automated next year.

I mean, we’re gonna see that cut in pretty significantly. I still think the biggest bottleneck is honestly just… you know, people have these, like, workflows and patterns and processes in their head, and if you could just draw it out on a piece of paper, like, you can turn it into an agent, and I think that skill is still growing for a lot of people.

Julia Nimchinski:

How do you approach that at Wendy?

Ethan Kinnan:

Yeah, honestly, when I need a new agent, I literally just… I draw it out on a piece of paper, and then I go build the agent, or I’ll take a picture and give it to AI to turn into a prompt to give to the agent builder. So, like, that’s how I’ve kind of exercised that muscle, and just, like, I’ve really… I’ve always thought in kind of, like, patterns and workflows.

Julia Nimchinski:

Awesome. Well, thank you again, and for everyone watching, what’s the best way to engage with you and, I don’t know, just test drive this?

Ethan Kinnan:

Yeah, totally. Could I share my screen real quick?

Julia Nimchinski:

Sure.

Ethan Kinnan:

There’s two ways to engage with us. I’m gonna have… Two ways to engage with us. So let me just pull up this.

Can everyone see my screen? Sweet. Yeah, so if you’re interested in working more directly with our team, when I talked about our Lindy Enterprise Plan, you get to work with a dedicated Lindy AI engineer.

I would say these people are, like, 5 times smarter at building agents than me at this point, and they help deploy them on your team so that you’ll have a couple of really solid use cases and help enable your team. So you can see some kind of cool ones on the side here.

We’ve already done this for hundreds of customers in most industries, so there’s a ton of… there’s a ton of ways to get going there. And then, you know, if you want to head to this link, go.lindy.ai slash hardskill, this is where you can actually schedule a free discovery call with our team that’ll walk through and find, hey, like, you know, we’ve been there and your business is in your shoes, like, what are the best ways we see?

So that’s, like, the easiest way, you know, if you want the more white-glove approach.

If you want to get started yourself, all the enrichment and the email agent I built today, I’d love to just give those to you guys.

So if you just connect with me on LinkedIn and DM me just skills, my agent will be able to send you all those templates and a couple credits to go get started.

So those are the two best ways.

Julia Nimchinski:

Thanks again, Ethan.