185 | Boost Sales With an AI Agent That Researches, Reports & Preps Sales Calls with Ashleigh Stearn

0:00Hello and welcome to another live episode of the leveraging AI podcast, the podcast that shares practical ethical ways to grow your business, improve efficiency, and advance your career. This is Isar Maitis, your host, and we have a really exciting show for you today. We've said many times since 24 and definitely in the first few months of this year that 2025 is going to be remembered as the year of agents, and this has turned to be completely true. Everybody wants agents. Everybody wants to deploy agents. Everybody wants agents are part of their workforce. The problem is the vast majority of business people don't know what agents are and even less know how to actually build them and what it actually means.

0:39What are the steps you need to take, which tools you can use and so on. So what we're going to do on today's show is demystify it and actually show you how you can build your own agents. But we're going to dive even deeper into that. We're going to show you a specific use case on how you can build a team of agents that can support your sales team, that can do research, that can grab information, that can update your CRM, that can do a lot of things that are going to help your salespeople do their sales job better. Now, the really cool thing about all of this is it's done without writing any code and in tools that literally anybody can use.

1:15And even better is that our guest today, Ash Stern, is a certified expert and partner of Relevance.ai, which is one of the platforms that a lot of people use to build these agents. And she has been developing and deploying agents for multiple companies in multiple industries in the past year. So she has been doing this for a living in the trenches, using relevance for a while. And she's an amazing expert and a great teacher that can explain exactly how she does it. So I'm personally really excited. And I know a lot of you are too. So Ash, welcome to Leveraging AI. In the next few years, AI technology will change our world dramatically.

2:01Whether you are a business executive trying to catapult your business forward, or just somebody who refuses to be left behind and want to advance your career, this is the show for you. I'm your host, Isar Maitis, a serial entrepreneur and an AI enthusiast. You'll hear invaluable practical tips from innovative business leaders, AI practitioners, and some of the brightest AI minds in our world today on how you can leverage AI in ethical ways to advance your career and grow your business.

2:36Thank you. Thanks so much for having me. Very excited. I am as excited as you. I think I'm actually more excited than you because you know exactly what you're going to show. And I'm like, I want to see, I want to see. And I'm sure a lot of people are the same way. And so great stuff. For all of you who are joining us right now on Zoom and or on LinkedIn Live, first of all, welcome. Thank you for spending this time with us. We're very excited to have you. Go introduce yourself. Say where you're from. Say kind of like where you are in your AI journey. What do you know about agents? So we know where you are and we can relate to that as we do our show.

3:07If you're not with us live, the question is, why aren't you with us live? We do this every Thursday at noon p.m. Eastern with amazing people like Ash. We're going to dive with each and every one of them into a practical AI use case. And if you're here, A, you can network with amazing people who are business people who care about AI. And also you can ask questions, which you cannot do if you're doing this while listening after the fact. So that's one item. The second thing that I want to say before we dive into this topic is that we have started the registration for the next cohort of the AI Business Transformation course.

3:39It's the course that we have been teaching for two years now. It's crazy that it's been two years, but since April of 2023, I've been teaching this course myself online on Zoom with cohorts of people. And so this cohort starts on May 12th. There has been hundreds of business people that literally transformed their businesses and or their careers with the knowledge they acquired in the course. And so the course obviously updates every single time we run it. We run it mostly privately for different companies and organizations. I'm actually teaching two private courses in parallel to this public course.

4:11but the public course starts on May 12th and we do the public courses only once a quarter. So if you haven't done any proper training for yourself, you owe it to yourself to do proper AI training so you can really transform everything that you're doing across multiple aspects of the business. There's gonna be a link in the show notes and then we're gonna drop the link also in the chat right now. And if you use promo code leveraging AI 100, so the name of the podcast 100, you will get$100 off just for listening to this podcast. How cool is that? So that's it. about the course. And now let's dive into agents, how they're built, how they're coordinated between themselves and how they actually are working.

4:50Ash, it's all yours. Awesome. All right, cool. So what I'll do is I'm going to jump straight into the platform and actually show you, but kind of explain everything as we're going. And I think that's the best way to do it. I do like presentations and slides, but there's nothing better than actually putting two and two together by actually going into the platform itself. So I'm just going to share my screen and we can get to that. Okay. So what we've got here is we're going to essentially start off with the team that we've got. Now our team currently is sitting under our manager, which is Manager Mitch.

5:26Now when we come down into, this is the agent dashboard, essentially this is what you're going to see as soon as you start creating your very own agent. Now, before we kind of get into, I guess, the details of how it's all set up, I'll essentially show you what it looks like. All right. So just some context here, this was set up and was connected to my Calendly link. So what would happen is that somebody would book with me through Calendly, they'd fill out a form, which included obviously these details that we've got up here, right? And then this would actually be triggered and sent directly to my agent to start the entire research process.

6:00So when we click, so this is the information, we click down and we can actually say our manager, which is who we're currently in at the moment. I'll pause it just for one second. For those of you who are just listening and not watching this, two things. One, there's a YouTube channel that you can find very easily by clicking in the YouTube channel link in your show notes. So if you want to watch this, you can do that. But if you're driving or walking your dog or doing the dishes or whatever it is that you're doing, listening to podcasts or yoga, or I don't know what you're doing, we'll explain everything that's on the screen.

6:26So in this particular case, we're looking at a screen in relevance. And on top, there's information that came in that triggered the agents to start working. So it's the email, first name, last name, meeting type, again, all the information that basically comes, oh, time zone, the information that comes in from a meeting that you set up on Calendly. Right, right, exactly. So with that information that we've got, our agent, you can actually see the process of our manager agent delegating this information to our sub-agents. Now we've got two sub-agents. Okay, you can actually see here that it's delegated to our lead researcher and then delegated to our CRM update agent.

7:08Okay, so essentially what our manager is doing is that it's communicating. It's being programmed in a way to communicate and delegate tasks to our agents. Our agents do what they need to do. So obviously our lead researcher is doing all sorts of research based on our lead and then we've got our update as well into our CRM. So this is the information that we get from all of that research. So information includes the job title, LinkedIn profile. We've got company information such as the website, LinkedIn page, a company summary. And then we've also got a pre-sales call report. Now, I really wanted a pre-sales call report written for me so that I was very well prepared before going into the discovery calls.

7:52Now, there's nothing more off-putting than going into a sales call and just not knowing who you're talking to and just how you can really serve them. So our pre-sales call report, there are two ways that you could do this. You could do it within Relevance AI, but at this time, when I did this particular setup, I actually had it connected to Make. Now, if you haven't heard of Make is an automation platform, it's a very popular one. So it's a visual builder and it allows you to visually build out workflows. Essentially, Make is you're selecting a whole bunch of different nodes with Make and they are connected, right?

8:27So you're connecting all of these different components that make up an automation. Now, when creating agents, you need to understand that automation is a part of that. And so I set up a little automation that added my pre-sales core report to a Google document. Now, the wonderful thing about Relevance AI is that they've now added in a whole load of integrations that you can now add to your agents directly. One of those is Google Documents. So essentially, you're not going to need any of these external integrations with like other platforms like Make and Zapier because like, one, that's annoying.

8:59Two, it's technical as well if you're not familiar with those platforms. But just to show you how the report was actually structured, this is what it looks like. Now this is actually being done within Relevance AI that will actually give me the link to my Google Doc without going through Make and stuff. So we've got top expert themes. We've got a pre-sales strategy. So rapport building elements, professional achievements. Then we've got pain points and solution alignment, value proposition, and then shared content and priorities. Now, I like this because I like to see how active my lead is on LinkedIn.

9:34And if they're active on LinkedIn, obviously, if they've got recent content, it really paints a picture of like, what are they interested in and in terms of business and what they're doing. So I like that. So essentially, this is what our report looks like. Now, if we go back into - Quick question on the pain point side, how does it know their pain points? Does it do research on its own? Is it based on what they wrote in the form? Is it a combination of these two? Like what is the process to get that? How the pain points are done is that you actually have within your particular agent who's creating the pre-sales core report, in this case, it's my lead researcher, they actually have my information.

10:11So my information is then combined with their information and just the research that it's done based on that lead so that it can perhaps find some pain points where my services and what I offer is going to be of value to them. I have found that sometimes people will actually post content on their pain points, like what are they struggling with in their business and stuff like that. So that is another indicator of pain points as well. But our lead researcher does all of that for us, which is really cool. And we're going to dive into that afterwards, right? Exactly to see what it's doing under the hood.

10:43Yeah, exactly. So we'll get into more detail about that. Right. So that is essentially our team, a little mini team of agents. Now, it's not a big team. Some of these teams can get quite complex. But one of the main things we need to keep in mind here, and what we'll do is we'll actually go into the lead researcher now, just so I can really show you exactly how it works. Now, within Relevance AI, you need to try and understand what's your workaround? How are you going to actually build agents? What's your interpretation of agents? How do you want to approach building agents? Relevance AI, as you can see here, is really based on prompt engineering.

11:21Now, this is one of the things that people need to understand is that if you're going to build agents, whether it's with Relevance AI or another platform, prompt engineering is a part of that. your agents are built on LLM models. So prompt engineering, just to set an expectation here, is a skill that you'll need to have when building any kind of agent. If you're using LLM models, you need to know prompt engineering. So relevant to AI, the agents here that we build, the brains of them are the core instructions. So the core instructions are essentially one big prompt. And in this particular case, what we use is a prompting technique called chain of thought prompting.

11:59So it is a very common way to prompt LLM models these days. But what it allows us to do is that it allows us to really help our agent understand the process it needs to go through in order to get the best output. So as we're looking at our lead researcher agent, we can go through this prompt and see how I've actually programmed it to work. All right, so we've got a role. So we're going to always give it a role. We then have an objective. Okay, we then have context as well. And now what's different with an agent prompt in Relevance AI is that you're going to have an SOP. Now, usually if you're just using this prompt with an LLM model, like ChatTBT or Claude or whatever else it might be, you're not usually going to use an SOP.

12:40You might use instructions, okay? And only just instructions. But in this case, because we're building an AI agent, an SOP is good to have in there. Okay? So we have that. And within this SOP, we're really getting quite granular on the different steps it needs to be doing, the process it needs to go through. So we've got our tools tagged in there as well, which you can add by just doing a backslash and it will actually add in the tool for you. And then we've got just some very brief overall instructions of just the overall tasks it needs to do for us. So I'm going to pause you just for one second.

13:16So how did you come up with this, right? Because you've been doing this for months and you have a lot of experience. If I'm just getting started and I want to build one of those, how do I get started from scratch? First of all, do they have templates or do I ask ChatGPT for recommendations? Like what's the best process if somebody just getting started, how to figure out, because we just named the topics, right? We said, okay, there's objectives and there's context and there's SOP and there's general instructions. But what actually goes in them? And we can obviously read this to everybody, but that's not the point because people want to build something else than what you built.

13:48So how do you get started if you don't know what you're doing? Okay. if you don't know what you're doing, then I would actually get started with what is a process you're currently doing for your business that you don't want to do. So if you have a process currently that you don't want to do and you want to streamline with AI agents, then that is the first starting point. That's where I started. And that's why I built this lead researcher. I didn't want to waste time researching leads. I wanted to actually take more time in preparing for my meeting and doing other things like building agents. So what I actually recommend to people is going through the current processes that they're doing and seeing what can actually be automated straight away with AI agents, and then go from there.

14:30If you've got the process in detail, right, even better, because then you're really getting quite granular on what is the process, what needs to be automated and how you're going to build towards that. Now, with your agents, you've got building your agent for one particular process. In this case, what we're looking at is we're researching leads. But within the lead researcher, you've got your tools. Now, your tools are what are your automations. And this is what I mean by understanding that building agents, you have to develop multiple skills. So if we take a look at, let's say, our pre-sales core report tool, which is one that is responsible for actually writing our report, you're actually seeing that this is an automation and it's very similar to what you'd see in another automation platform like Make or Zapier.

15:19And essentially, our tools are the skills for our agents to leverage and use to perform their tasks. But we have to build these. So when you think of a lead researcher, you think that's their role. So you think of a role in a human sense or a human department sense. Let's say, for example, what I was doing was content production or social media management. Within that social media management role, I had to write content. Then I'm going to build an agent that's going to write content. So it's very similar in this case where lead research, that was one entire role. I didn't want to do that. So I'm going to automate that with an agent.

15:56And then what are the mini tasks within that role? So we have research, we have generating the pre-sales core report, and then we also have verifying the leads email as well. Those little mini subtasks, you then have to create tools to do them. So I want to pause you for two things. One is I want to give some idea for people, because you said something that is brilliant and we didn't dive into that. And I think it's important to start by mapping your existing processes. And people are like, oh, my God, that's a great idea. But like, how do I do that? So the way I work with my clients when we map processes is I literally ask people to narrate what they're doing as they're doing it.

16:33So open your screen, open the tools you're using, hit record on whatever record platform that you want to use. You can use the usual suspects, but you can use Zoom. Like you can have a meeting in Zoom on your own and just run that and just record the screen and explain what you're doing as you're doing it. Do it three times. If there's multiple people doing it, ask each and every one of them to do it three times. upload all of that into your favorite LLM, could be Gemini, Cloud, Chachapiti, I don't care, and say, hey, I want you to create an SOP of this process. Here are transcriptions of recordings of six different occasions doing it, because then people do it a little differently at different times, and it will create an amazing SOP.

17:11Then you want to ask it to ask you questions to figure out if there's any gaps. It's going to ask you questions. You're going to answer them, and you have your SOP. That could be a very solid starting point for your agent. So that was what I wanted to add. what I want to ask about these tools is I assume like a section here that is creating these tools, right? And so can you explain what that means to create a tool? Show us a little bit more detail on that. Yeah. So when we come into our dashboard, this is Relevance AI, this is your profile, this is how it's all going to look when you're in here.

17:43You're going to come to your tool section and now you're going to see a whole bunch of tools that I've actually created. All of these tools are where they all live. Now you can build them from scratch, but you can also use templates. Relevance AI, when they first started, they actually only had six tool templates. Now they've got an entire library of both agent templates and tool templates. These are great to start using, especially as you're learning and getting started. And in fact, how I learned quick or quicker than trying to rely on documentation, the documentation back then and content to learn how to do this was not very good because it wasn't a very well-known platform at the time.

18:25But I had six tool templates and all I did was I clicked on one, let's say, for example, perform Google search. So I clicked on it. I then have to clone the tool to be able to see what's going on in the back end of it so I can understand how they actually did it. and then I would actually create a tool completely from zero, from scratch, and I'd just copy. So I just copied these steps. In our tools, we've got our inputs, and then we've got these different steps that we also have to add in there as well. Okay, now this one's a very small tool. It's basically like a smart flowchart, right? You start with an input and then what if or what steps you need to take, and that becomes a tool that then the cool thing is, I guess they're all reusable because any of the agents can use any of the tools that you created.

19:11So if you create them generic, then you can use them across other tools as well. So in this particular case, a Google research, well, you want to do research across probably multiple different kinds of agents. Exactly. And obviously a Google search tool can be used across all sorts of domains as well. So it's not tied to one domain either. So they can be used quite regularly by various different agents as well. And what something is really important here to know is that how do our agents actually use these tools, right? So we know that they get given information, okay, but they actually dynamically give or use that information that has been given to it, and it will actually fill out the inputs by themselves.

19:51We don't have to put in any information or anything like that into this area. Our agent actually does all of this for us in order to use the tools effectively. So they dynamically add the information to our tools for us. And it doesn't matter what type of input it is. It could be a text input. So in this case, it's going to be a Google search query. But then we've got various other different types of inputs. So numbers, we've got files, we have an options menu. So yes, no. And our agent will actually fill all of these different types of inputs out by itself. What I would generally do with this was that, okay, so I cloned it.

20:32I'd then go back into my dashboard. I'd go back to my tools here, and then I would create a new tool. And what this helped for me when I was learning how to build agents was that it allowed me to understand what needed to go where. So I would go back into the tool that I've cloned. I'd understand, okay, well, this is the title, right? And I'd simply just copy and paste. When I needed to add a step, which was Google step, I'd come into here, I'd add a step, and I'd click Google search. Okay. Now there are a lot of little nuances to it, including variables. So it does get quite technical. And this is another thing I want to set an expectation for as well.

21:10It's technical in nature. It is something you're going to have to learn and the learning curve can be quite steep. So in saying that, can non-technical people build AI agents? Absolutely. you're looking at one. I was writing blogs before I was doing this. So yes, but don't underestimate the learning curve because if you consider yourself a non-technical person now, well, by the end of all of this, you'll be considered a technical expert. So just want to set an expectation there because it's not something you're going to probably learn in two weeks, depending on your learning style. I want to add two things to what you said, because I think it's really important.

21:46Two aspects of what you said that I think that are critical. One, it's perfectly fine starting by copying others and others could be templates from the platform or people who share how they do their thing. And a lot of people do that. That's what we're doing right now, right? Ash is literally showing you the agent. Like you can watch the video, copy word to word what you wrote and you have a starting point. So it's perfectly fine to start by copying others because it allows you to understand the nuances and the processes and the little things that if you just try it on your own, will take you a lot longer.

22:14And two is the ROI is still there. Because yes, I'm like, oh my God, now I got to invest a month to learn this. Yeah, but this is going to save you a month every month. Then it's worth investing the first month in learning the platform. On the bigger, broader sense, from a company perspective, from a team perspective, you need one person who needs to know how to do this. You don't need everybody on your team. You don't need everybody in the company to know how to do this. And there's two ways to do that. One, you can hire Ash, right? You don't have to have the person in-house. It could be an external person that knows how to build that, that can build it for you.

22:48Two, you can get one of your team to be certified or trained on how to use either relevance or other platforms to build agents. And then that person can be the go-to person in the company to build that platform. That obviously means they need time and resources and other stuff. But I'm putting that aside for a second. The learning curve is worth it because the variety of agents you can build can help across almost everything in your company. Yeah. And I will just add something here as well with the speed of how everything is advancing at the moment. These are only going to become quicker, more reliable.

23:20They're going to get better and they're eventually going to be cheaper, hopefully in the future as well. So it is something that is constantly improving. And I've seen Relevance AI when they first started to where they are now and just the amazing leaps that have come in the incredible updates that they've done recently. They're making it, the platform is specifically targeting industry experts. They're not targeting technical experts. It is a platform that they're trying to build into something that anyone, any industry expert can come in, say they want an agent and have the agent built. They've actually got a new feature that was just released not too long ago where you can actually create, you can invent a tool.

24:01You can also invent an agent just by putting in a prompt. And it will write all the details and all the steps and all the other stuff. Right. It's going to write the core instructions. Now the core instructions and the prompting that goes into that is actually a very creative side to agents when you're building them within relevance AI. And I always say to people and recommend experiment with it just because my way has worked for me doesn't mean that another way of writing the core instructions is going to work or it's not going to work. There's no right or wrong here. And I think that just goes to show that the platform is flexible and the agents are intelligent.