I can’t believe we built an AI employee in 62 mins (Cursor, ChatGPT, Gibson)

1. HMHarish Mukhami0:00

   Today, we'll be building an AI customer success agent, which is gonna solve problems for you without any humans involved. We're gonna be using Gibson AI database for database backend. We'll be using Cursor on Claude Sonnet 3.7 for coding. And finally, we'll be using CrewAI framework for AI agents. We're building this in three parts. So part one, we'll build a SaaS tile dashboard app to provide insights and recommend actions. Part two, we'll add AI agents to analyze these insights and recommend actions. Part three, we'll actually make this agent autonomous and take actions on users' behalf. This is the dashboard. This is a result of part one. This is the customer success AI agent dashboard only, so we're focusing on all the way from design, development, deployment, management, and scaling of databases, which coding tools don't do today. So when we started building in Gibson, we used a web interface. Now we're actually building tables right from Cursor IDE a-and deploying the database. You're not building a prototype anymore, so you are backed by a very significant production-grade database. It's already deployed in the cloud, so you publish this, and you get 10,000 users tomorrow.

2. AGAakash Gupta1:05

   What other AI employees could people create?

3. HMHarish Mukhami1:08

   SDRs are a very popular use case. There is also meeting preparation agent, executive assistant. You can chain together all of these agents to, to create a specific role that is tailored for you.

4. AGAakash Gupta1:22

   Really quickly, I think a crazy stat is that more than fifty percent of you listening are not subscribed. If you can subscribe on YouTube, follow on Apple or Spotify podcasts, my commitment to you is that we'll continue to make this content better and better. And now on to today's episode. All right, Harish, welcome to the podcast. What are we gonna be doing today?

5. HMHarish Mukhami1:46

   Yeah. Today we'll be building an AI customer success agent, uh, which is gonna solve problems for you without any humans involved.

6. AGAakash Gupta1:53

   All right. Let's just get into it.

7. HMHarish Mukhami1:56

   Yeah. Uh, let me share my screen. So we're gonna be using four tools today. Uh, we're gonna be using OpenAI o3-mini for planning. We're gonna be using Gibson AI database for database backend. Um, I can do a quick intro of what Gibson is. Gibson is a, a AI-powered cloud database. You can build, deploy, and manage your database at lightning speed. Uh, so it's, it's, it forms the database backend. And we'll be using Cursor, uh, on Claude Sonnet 3.7 for coding. And finally, we'll be using CrewAI framework for AI agents. So that's the tech stack that we'll be using today. Um, so, um, we can, we can go on and start building.

8. AGAakash Gupta2:37

   Let's do it.

9. HMHarish Mukhami2:40

   So how are we building this? Um, we're building this in three parts. Uh, so part one, we'll build a SaaS tile, uh, dashboard app to provide insights and recommend actions. So this is something that most of us are very familiar with, so which is a dashboard with metrics and insights, which we have been using for a while now. Uh, part two, we'll add AI agents to analyze these insights and recommend actions, essentially automating out, uh, the first part. So now AI agents will be looking at the dashboard, looking at the data, and analyzing, um, the data, uh, identifying churn, uh, and mitigation reasons, and providing actions to users who can approve or take those actions. So this is like a part two is the human in the loop, uh, with an AI agent. Uh, part three, we'll actually make this agent autonomous and take actions on users' behalf. So at the end of it, we'll have a fully functional AI agent for customer success. So let's dive into part one. Uh, so for part one, uh, we'll start with planning phase. So we know that we wanna build a dashboard for AI customer success agent, how exactly dashboards should be, what metrics should we focus on. So we're gonna ask, uh, uh, ChatGPT, in this case o3-mini, to provide us some guidance here. So I have a quick prompt ready to go. Uh, so I'm gonna copy-paste this here. Um, fairly straightforward prompt. "Act as a business intelligence expert to design a comprehensive dashboard for Bulk Trade," uh, that is, that is a company that we're focusing on, "a B2B e-commerce platform." Uh, you can imagine this could be for any other business, but today we're, we're gonna focus on the B2B e-commerce. Uh, "As a customer success manager, I need dashboard that integrates data from our CRM," uh, in this case we're using HubSpot, "Google Analytics, or an application database. Uh, the goal is to empower our customer success team with actionable insights that will optimize customer funnel, improve engagement, retention, reduce churn. Uh, please provide a dashboard with four tabs, sidebar needed to ensure that we achieve our goals." Um, I'm spelling this out because this is the one that kicks off everything in motion.

10. AGAakash Gupta4:47

    Okay.

11. HMHarish Mukhami4:47

    So I'm gonna ask this, uh, and wait for, um, or using it in reasoning mode, so it came back with pretty, pretty quickly came back with a few, few, um, recommendations here. So, uh, funnel performance, engagement and usage, customer health risk, um, retention and churn. So four tabs like we asked, uh, based on what we, um, what the goals are here.

12. AGAakash Gupta5:14

    So why did we use o3-mini reasoning mode?

13. HMHarish Mukhami5:18

    So, um, of all the models that I've f- uh, that I've used, I've found o3-mini is great for planning, um, especially it's much faster. Um, so, uh, it has worked out well for me so far. For coding, I use Claude Sonnet. That has been the king, I guess, for the past year or so. So those are the, the two models that we're gonna be using today.

14. AGAakash Gupta5:39

    Sounds good.

15. HMHarish Mukhami5:42

    Um, now we, we have the dashboard design. Uh, the second and, and even more important thing to focus on is what is the makeup of your database that is powering your dashboard? So I'm gonna just ask o3 to see, um, uh, to sh- tell us, like, what data should we use? Um, what are some of the components here? So what data powers this dashboard?

16. AGAakash Gupta6:09

    So now it's thinking about it and helping us plan what data inputs we'll need to create.

17. HMHarish Mukhami6:15

    Exactly. Um, so it, it is funnel performance, engagement, and usage. So one of the things it just did was it did exactly like the dashboard organization. So it gave, for each dashboard, um, these are all the data fields that you need. What I do wanna ask is, like, organize this by data source instead of tabs.

18. AGAakash Gupta6:43

    Makes sense.

19. HMHarish Mukhami6:50

    So now we have a much more specific, um, response around like, "Hey, from HubSpot, these are the, these are the fields that you need." I'm gonna copy-paste this whole thing.

20. AGAakash Gupta7:02

    It's very much like a data definition.

21. HMHarish Mukhami7:06

    Exactly. You don't even-- I, I guess you don't even have to have this specific specificity, uh, um, with, uh, from a database perspective. Uh, you got a bunch of these. So I'm gonna copy this, and then, uh, we'll go to Gibson and model this database.

22. AGAakash Gupta7:27

    Okay. And so what role is Gibson playing here?

23. HMHarish Mukhami7:32

    So Gibson is the database that is powering this entire application. So, uh, first step when we're building the dashboard, um, the front end is actually looking into the Gibson AI database to, uh, to show that data in the front of the dashboard, in the dashboard. One of the cool things about Gibson is it's an AI-powered cloud database. So you could imagine, um, if you wanna build this analytical back end for, for like, um, um, customer success dashboard, you need to have a database that is, um, easily scalable, can, can ingest data from multiple sources and handle, like, hundreds of thousands of transactions, if not millions. Uh, Gibson can autoscale with that, so that's why I picked this.

24. AGAakash Gupta8:14

    Okay.

25. HMHarish Mukhami8:16

    So let's go to Gibson. So Gibson is available on, on the web, or it's also available in your favorite tools via MCP server. Um, so we're gonna, we're gonna see both. So I'm gonna start off with the web, uh, version of the Gibson AI platform. So I'm just gonna sign in. It's free to get started. Anybody can join. You can use your Google Auth or log in with your email. I'm just gonna use my email to join in. And then this is the Gibson AI workspace. The first thing I'm gonna do is, uh, type in "Build me a database for B2B e-commerce customer success agent." So I'm just gonna type and, and provide a simple prompt. This is not the actual prompt that is gonna build the database but initializes the project and, uh, sets the context on what the user wants to build.

26. AGAakash Gupta9:19

    That's our high-level goal, and then we're gonna come in with the data definition later, I assume, from O3 Mini.

27. HMHarish Mukhami9:24

    Yep. Yep, absolutely. So, uh, the one we copy-pasted, I massaged this a little bit to make sure that, like, it's a little bit more cleaner. Um, we're still gonna have those three components to it. Um, here is your project window. So these are all the projects that you're working on. They show up on the left side. Uh, so the first step is it initialized your B2C-- EB-- B2B ecommerce customer success agent, um, and it gives you two options. I can either design and generate the entire data model for you at once, or you can build the data model step by step. So you would choose an option one if you weren't using O3 Mini, you're just asking it to, like... And you can work with it to design and brainstorm. Since we have already planned in O3 Mini, so I'm gonna go choose the option two here.

28. AGAakash Gupta10:13

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29. HMHarish Mukhami11:45

    E-exactly. And here, um, the goal is we're more s- uh, more d- uh, directional and targeted about what we want Gibson to build. It does give you some options, but since we already have it, so I'm just gonna copy-paste that. So...

30. AGAakash Gupta12:03

    And do you have several models working in the back end here? What is the-- What is powering this experience?

Episode duration: 1:02:03