Dan (4:56)

Yeah, that makes a lot of sense. Um, I think some, some color I can give from, from my seat because I, I don't see nearly as much as you, but I do see a lot, um, on the top down front, the CEOs that are actually doing, not just saying this, but actually doing it. Um, like those are the companies that go the furthest. Um, you, you will probably go as far as, in terms of AI adoption, you'll probably go as far as your CEO has gone. It's not something that the CEO can delegate. The ones that are really far ahead. It's like they're in chatgpt, they're in cloud code, they're trying new stuff and being excited about it. Toby from Shopify is a good public example where he's just hacking on stuff on the weekends. You don't necessarily have to be that far. But Shopify, its culture is a lot different and will be way different in a year because of that. And I think that's really important. So that's the sort of top down and then I think the bottoms up. The thing that you're alluding to is inside of any organization there are people who are just natural early adopters. And your job as an executive who's leading your org is to identify those people and spread what they know and elevate their status so that they can kind of pave the path for everyone else who is maybe like super valuable, but is not naturally just going to try some new technology, but will use it if they're shown, hey, this is actually something that is going to help you in your job.

Natalia (6:32)

Yeah. I mean, Dan, I feel this with you all the time. A new model will come out and you'll be like, why haven't you run this through X model? And I'll be like, you're right. Why haven't I run it through this model? But also I see it internally and the ways that it comes up naturally. When in our Monday standups, someone will say they were tinkering with a whole new use case or application, and then the rest of us will sort of see there's this new dimension of what is possible. And it's exciting when it works. It's exciting, but you need to be in this creative space where you're trying, you're failing. It's not really working. It is really working. It's really powerful. And then when you see what's possible, then you really understand, like, where you can go and just how far it can take you.

Dan (7:20)

Do you have any specific stories? You know, obviously we can't share from clients, like by name, but do you have any specific stories about unlocks that you've seen that were particularly powerful? Maybe it's something that we've done with our clients or just something that you've seen that maybe feels a little bit counterintuitive? Because I can imagine people listening to this and being like, yeah, that sounds good. Like, generally it's great. It's CEOs into AI. Generally it's great to, to promote your power users internally, but like, want to get down into the NITTY gritty of here's some like actual concrete stuff that is maybe a little bit counterintuitive or is a really big unlock versus the effort required. Like what comes to mind.

Natalia (8:00)

There's, there's two things that come to mind. One is with a private equity firm that we've, we started working with last year, that we're still working with. And that one comes to mind because our partner, kind of like our day to day liaison at that firm is both brilliant technically, but his superpower is actually that he understands the people dynamics around AI really, really well. And because his role is he's taken it upon himself to roll out AI at his firm. And he understands that it's a technical challenge but, but what he really understands is that it's a people challenge. And because he's at a private equity firm and like a lot of other investment firms, there's a lack of bandwidth, there's a lack of capacity to try a new technology, see it fail. There's people that are more advanced teams that are already using it in pretty advanced kind of interesting ways. There's other teams that just haven't had capacity to implement it. And so one of the things that we did together is, is we started out our work together by, he sat down with the investors at his firm and I think, you know, he basically mapped out every single task that they do in like to, to the most detailed kind of like end every single task that they do from research to diligence to market mapping to, to portfolio management, to just kind of like the day to day of like running their lives as investors. And so what we ended up with was the starting point of a very, very detailed view of what it looks like at this firm for an investor to do their job and what that looks like also by team because it can vary quite a lot depending on what the strategy is. And then what we did is we looked at that long task, that long list of tasks for that firm and we went through and highlighted where there are opportunities to use AI that are really high leverage. And so what we ended up with is this, is this map that we end up creating for all of the clients that we work with. But it was so detailed that we could really be very, very precise about looking for solutions that would give the team not just bandwidth, but really high leverage in any of the training and work that we did to together. And that that's the kind of work that's only possible when you have someone on the inside who is not just describing the work and workflows that teams rely on generally, but like very, very specifically to the work that they do and the way in which they approach or think about their work. And it's made it so that in the training and the enablement and the tools that we've been able to develop together, there's this moment, this like, aha moment that is like wild, you know, where investors will kind of come and realize that there is a new way that they can write an investment memo that previously took two to three weeks and they can now get like a really high quality draft in literally 30 minutes. And that's only possible when you have someone on the inside who understands all of the elements.

Dan (11:15)

That's interesting. Tell me more about that moment. And like, what that kind of either automation or workflow. What, what is that? Is that like they're using ChatGPT? Well, they're using QuadCode? Well, like, yeah, go get more deep into that.

Natalia (11:28)

So in that case, it's a few things. One is this particular firm has a lot of resources in SharePoint around the theses that they have their investment theses. And this is, this is kind of like the IP of the firm. Right. They've spent a decade, if not more really thinking about how they approach a particular area of investment. And when they're diligencing a new company, they want to understand, given this repository of knowledge that they've accumulated over a decade, how should they be thinking about this opportunity, you know, beyond just the number crunching? And that's something that is like really quite onerous, a huge task to take on, to like really read that and then digest how it compares. And of course that's something that just chatgpt is able to do effectively. Very, very effectively. And so what it looks like for them is connecting the right context, really the right sort of sources of data, and then funneling it through this prompt that is trained to understand how they think about that particular investment strategy. And then, yeah, basically just creating kind of like a set of like GPTs and prompts that make it so that it's really easy to synthesize all of that information into an investment memo. That really gives them sort of that general rubric of how that company compares to this broader opportunity and to the decade of information that they've collected. That's something that again, an analyst and associate of principals spend, you know, two to three weeks to pull together before it goes to the ic, the investment committee. And now you get like a really solid draft in like 30 minutes.

Dan (13:05)

That's really interesting. And I Think that's like actually a broad general pattern that we see in a lot of companies, even in our own company is like the first one is the obvious one is the connect the AI to all your data sources, which is, it's hard but like that's, that's sort of table stakes is it needs to be connected to the place where all the context lives. But then the other thing that's been happening, especially as you know, our org and lots of other orgs that we're around are transitioning more into a like agent, let's say agent native world where they're using cloud code or they're using coworker, they're using, you know, these other kinds of tools where you kind of expect the agent to be going off and doing some work for 20 minutes. And it's not necessarily like a back and forth chat. In the same way, once you have the connections to all the data, it's really important to have the prompt or the skill that you've built, be able to tell the AI like, hey, here's how you find the specific thing that you want. Like for example, for us, if you wanted to figure out what our revenue is, there's three different places you could go. You can go into Stripe, you could go into chartmogul, maybe you could go into Post Hog. But our head of growth Austin has a particular way that he's defined what our MRR is that instead of forcing the agent to figure that out from scratch every single time, putting into place here's how we think about what MRR is and that transfers into for consulting for one of these clients, like here's how we think about this sector and here's where you get the data for this particular sector. That's like where a lot of the value is and a lot of what makes your use of AI different from someone else's use of AI.

Natalia (14:51)

Yeah, that's totally right. I think it's the hardest part of AI actually, and this is the part that has been so magical at this particular firm that we've been working with, is that our partner, his name's Jonathan, Jonathan basically interviewed every single investor and every single team to really understand the nuance in which a team collectively thinks about every part of the investment memo. And this work that we've been able to do together really would not have been possible. And if it didn't have such a high degree of tailoring. You know, this is like, this is like Savile Row sort of prompt tailoring. Like it's so, so, so Specific, the way that numbers show up, the way that figures show up, the way that they express or think internally around this stuff, it's really important. And the prompts reflect that. And so the prompts really end up being this analyst that does really high quality work that is dependable. And that's so cool.

Dan (16:00)

That's really cool. I know we've also done a lot of work with hedge funds and also with tech companies. Any other examples you want to share in those domains?

Natalia (16:10)

Yeah, I mean, let me think there. So there's so many cool applications. Maybe I'll speak to. There's a really interesting pattern that we're seeing at one of the tech companies that we're working with right now. We know that when it comes to working with engineers and with engineering orgs, there's sort of like a four step process that works well when it comes to implementing AI. And that is you plan, you delegate, you assess and then you compound what works or kind of like the learnings of that, that particular session. And when we spoke to the engineers in this particular org, we found that they were actually really effective at the delegating, at the assessment and even the compounding. But there was no planning phase. And so they weren't going very far and they were running into the same sort of challenges over and over again because there wasn't a good plan for them to really scaffold significant workarounds so they could solve a lot of small issues, but they weren't able to address these big sort of like meteor problems that we kind of hope for AI to help with. And so this is the kind of thing that only by understanding how that particular group of engineers was using AI could we really realize like you're just missing the planning phase, right? Like we just need to do enablement around what good planning looks like. And we're already seeing that, as I think we all know it makes a huge difference and you can only really compound as much as you plan. Right? So now that they're starting to compound these big plans that are developing significant work, I think we're starting to get that sort of high leverage machine that we hope to see work in engineering orgs.

Dan (17:57)

What do we think is possible here? Like what are the kinds of speed

Natalia (17:59)

ups that, that we expect in engineering in particular? You know, that's a difficult question to answer, but you know, I would say we are consistently seeing when this plan, delegate, assess, compound framework is in place and used well, we're frequently seeing engineers generate two weeks of work effectively in an afternoon and I wouldn't be surprised if that continues to speed up.

Dan (18:31)

Yeah, I mean, we see that too. And it definitely changes how we think about who we can hire on the technical side and what we are optimizing for and even how we do like, you know, programming interviews and stuff, like technical interviews. It's really, it's a really interesting change, but I think one of the more interesting ones is for you. I've just watched you and several other people who are not technical inside the org just get totally like your, your, your, your mind totally blown over the last like three or four weeks. Like, it feels like there has been this like massive phase shift where I would just like message you and you'd be like, yeah, I've been up since like 6am by coding. Can you like, can you tell us about that? Because I think it's really interesting and I think that you're, if I had to guess, you're sort of the leading edge of the spear. And there's gonna be a lot of people coming after you that are feeling the same way and that we're gonna spread a lot of things that you're learning right now to our clients and just really anyone who's watching videos like these, because it's, it's a, it's a new way of working that really valuable.

Natalia (19:36)

So I'll be honest. I, uh. So yes, I think I will admit that I am a bona fide vibe code addict at this point. So I. The way this happened is a funny. It's a funny thing. I actually realized I was starting to fall into a trap that I often see our clients fall into. So at the end of last year, you know, we had so many projects going on. We were supporting hundreds of people, people across organizations. And every day my day would start and I just had a bunch of meetings and a bunch of work to do. And so I didn't really have time to play with a lot of these tools. And going into this year, we realized with Natash Aggarwal, who is our applied AI engineer that is in the consulting team and is fantastic. He was previously an engineer at Quora and helped build this beautiful product and then moved into the consulting org to help amplify the work that we're doing with our clients. We realized with Nitesh that we weren't going to move as fast and do the creative work that we wanted if we were scheduling the work to happen in the nine to five, if you will. And so we decided to start our day three hours early. So we would meet at 6, 6am and we would basically just vibe code from like 6 to 9am and it all started with us asking, you know, could we create this really ambitious project which is, you know, project management is really time consuming for any consulting business. Right. Any great consulting business has an entire function around project management. And it's a real skill, but it also requires, you know, understanding a lot of moving pieces, how clients prefer information, how they are scheduling sessions, like all of the sort of like nuanced things that are happening for any given project. And Nitesh and I sort of asked, you know, do we think we could really spin up a. We could spin up basically like an agent, hire an agent to be our project manager. And the answer really quickly was yes. But also the framework for how to do that effectively, this is in Claude code specifically, actually took a lot of iteration, I would say. We got 85% of the way there three times and then had to scrap it, given what we learned, and then start again to get to somewhere, to get to a new framework that actually got us to 100%. So it's just been so fun. It's so cool to really build something. And it's just, I think it's really creative work. Right? And it's really also clarifying work to really understand, to think about the questions, what does it mean to be a good project manager? What does it mean to be a good project manager for me and the business that we're running? And how do I codify this into a series of instructions? You know, we talk about AI using AI effectively being a lot about being a good manager. It's like, how do I, how do I, how can I be a clear communicator and provide clear instructions so that we can really create this agent we call Claudie to really run on their own and do this work for us? And it's just so cool to be a few weeks out from that and to really have this system working.

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Dan (24:34)

That's really cool. I want you to, I want you to show us the system. Before we get there. I want to point out the that interesting pattern which is instead of just expecting it to happen inside your 9 to 5, like you actually just carved out three hours outside of your job to like play. And I think that's like an interesting lesson that we definitely just know ourselves internally inside of every. Like we just got back from Think week, which we do every, every six months where we were all in Panama together and we just got rid of all of our day to day work and the whole point was just like play around with new technology, get to know each other, build interesting things, just, just play, like do whatever you want. And I think that's so important in a world where technology is changing so fast because what you don't want to do is like work really hard to be the fastest horse and buggy driver, you know, and, and you can't learn to drive a car until you like take some time out of your horse and buggy race to be like what is this car thing? You know? And I think you, you discovered that. I think that's something that we've done inside of every and it's also something that a lot of our clients and just generally companies that I think do this well know how to do is give people the space that they need in order to feel like they can try out new technology in a risk free way where they're not going to get behind in their job. They can kind of like learn it inside outs and fail. And then after a couple times of the iterations of this, they're like holy shit, I had this, I'm driving a car now. Like I'm not driving a working buggy anymore. And I think that's so valuable.

Natalia (26:16)

And it's really hard actually. Having that creative space is very, very counterintuitive to the way that we usually work. Right. How, how much of our time is really spent, you know, in traditional jobs, just like figuring out, like, seeing if there's like a new way to do things right. Historically, usually when you're hired to do a job, you're hired to do a specific set of functions that have been laid out to you that you're supposed to do until you get to the next level, whatever that is. And for a company to be so bold as to say, hey, we think this is all changing and we don't know exactly how it's changing, but we trust that you can figure it out and you can figure out what this means to you. And maybe we'll bring in outside partners to like, accelerate the way in which you do that. It's really revolutionary, it's really amazing. But also it can be a really creative space where you have to, to be at a company that's willing to see things fail to experiment. Right. Like, we had to throw our project manager agent away three times before we found the scaffolding that really works and that saves us so much time per week. But I'm not an engineer, I'm not a product manager. This isn't kind of like my day to day job. And this is only exciting and possible when you put on this creative hat and just keep on tinkering until you find something that really works. And for me, having an attache, you know, for me having these like incredible resources, you and everyone on the, every team around me, where I'm seeing constantly what's possible, it just makes all of these things so much more achievable.

Dan (27:54)

Yeah, I think that's another really good pattern, is you have Nitesh, who's an applied AI engineer who can literally sit with you and help you figure out, okay, given my workflow, how can I build this project manager? And you have the expertise and like what's needed, and he has the expertise in like what's at the edge of technology. And I also, I think that that's another really good pattern. I see a lot of CEOs doing is, okay, the company's going to only go as far as I go in terms of knowing how to use AI. I'm going to have someone who knows about what they're talking about and I like just literally sit with me and talk me through, okay. I have this project on my mind that I feel like would Be really fun and really valuable. If I got it done, it's going to be half learning, half us. Just like trying to knock out this, like really ambitious, interesting thing. And I think that's actually a really good way to get in, get yourself addicted to is have someone who's sitting next to you as you put your toe in the water.

Natalia (28:51)

Yeah, absolutely. I'll also say, I think there's something to be said about you need as much engineering power and like, sort of like AI know how to as you need an understanding of like what good looks like, which is very specific depending on what it is that you do. And the different iterations of our agent that didn't work were. One was just too engineering focused. It was too focused on the framework and the strategy of how the data would be connected to each other. The other one was too focused on just what the work is. Right. So it was just kind of like a job description. And. And it wasn't until we realized, like, all right, it's a mix of the know how of like what good project management looks like and what it looks like to us, which is kind of the information and context that I have. And then also, you know, how tasks and agents and sub agents and all of this CLAUDE code infrastructure can best be organized so that it serves kind of the need that we're specifically looking to solve, that it really came together and worked. And so you can have really great engineering power, but you also need to have the know how to get to something useful that makes sense.

Dan (30:03)

Do you want to show us a little bit of Claudie?

Natalia (30:06)

Yeah, let's do it. Okay, cool. Can you see my screen?

Dan (30:09)

I can see your screen.

Natalia (30:11)

All right, so welcome to the every consulting GitHub page. And this is where. This is where Claudie lives. So. So Claudie is our project manager for the consulting work that we do with our clients. And the first thing that I'll show you is the architecture, which I think is pretty cool. This took, you know, two weeks to like really refine and come up with to have it work. So I won't, you know, I actually won't, I won't go too into the details of this. We actually have a great post that we'll share and go into the details of how this is set up. But at the highest level, we have this CLAUDE MD file that has, you know, the instructions, the context. I'll share that in a second. Basically kind of like the job description that Claudie has. Then we have a list of commands that basically Run Claudie. So if we want to do a quality check on the data that that is collected for a weekly update on what's going on across clients, we're trying to set up a new client. If we're onboarding someone new, then we have a list of tasks. Tasks came out fairly recently, and they have been instrumental to Claudie being effective because they are. Tasks happen in phases, and so they manage dependencies and enable sub agents to basically double check and triple check the quality of the work that's being done before it comes back to us. And then we have some general purpose sort of agents, so some skill files, general principles. We want things to be well formatted. We want the things to be written in a way that reflects every in our brand. And those are skills that we've enabled on the back end. And then this is kind of like maybe the most important part, it's the data sources, right? So we enabled mcps that connect to Gmail, to Calendar, to Google Drive, and then to the meeting transcripts for the work that we do. So at the highest level, this is sort of like the architecture of Claudie, our project manager. And these are some of, some of the commands to kind of. These are some of the commands that help Claudie work effectively. So if you're watching this and you need a product manager, this is a pretty good. This is going to be a pretty good sort of template or model for how you can think about setting a project manager that could work for you. I'm actually going to now dive into the. What I'll call. This is the Claude MD file, and this is really what I'll call the job description that we've given Claudie. So this is a file that Claudie reads every single time we ask her to do something. And we found this to be really important because if you are a project manager, you always know where you work, what your job is, what it means to do a good job, who you report to and who your colleagues are. Right. If you're a person, you always know this information. And so we wanted to create a file that at its baseline, always gave Claudia this information to remember who it's working with and where it could be drawing information from in order to do its work really well. So it knows who we work with, it knows where to draw data from, and then knows every time it encounters a dashboard. This is the general way in which we've structured information so that it can continue to populate and maintain that information with high fidelity. Here, there's some ID conventions that I'll actually mention really quickly, because if you are creating your own project manager, we realize there are some principles of database management that actually help a lot in project management, where you are relating different pieces of information to each other. Did this person attend this training session? Did this training session deliver prompts or agents or whatever it was? And so creating ID conventions that are effectively like database management, that allow Claudie to connect who did what, where were huge unlock for us to have this entire system work well. And then we gave Claudie some principles to always keep in mind. So, you know, data accuracy, totally key. You have to be proactive, not reactive. Don't wait for, you know, if you want to be a good employee, you want to be proactive, right? You don't want to be asked to do things. So we kind of gave it that mentality. You know, every interaction builds or erodes trust formulas over manual entry. These are just good, good best practices that if you are a project manager or you deal with data, you really think about, and then when in doubt, escalate. So just ask questions. And these are some general principles that we've seen really help Claudia work very well. And what you'll just see here, you know, I won't go into the rest of the details, but what you'll see here is this is actually a fairly concise file. You know, we're not giving a job description that is incredibly detailed. You. Claude is really, really smart. Opus 4.5 is what Claudia runs on. And it actually doesn't need to have us define what a project manager is or what it does. But these boundaries, conventions, and sort of sharp edges, refining of sharp edges, have really allowed Claudie to do really good work for us.

Dan (35:32)

This is so cool. There's so much in here. But what I want to do is just show, like, I don't want to see it myself. I want to see, okay, how does it work in action? Maybe like, how it works to set up a new client. Because I know that that's one of those things where it's like, okay, you send a new client, it's usually a big deal, It's a lot of money, but it takes a long time to get them all in all the systems so that we can actually execute the project. Can you take us through how that works?

Natalia (36:02)

Yes. So let me take you. I'll create a new warp page page here. All right, so we're going to open Claude.

Dan (36:16)

Living dangerously. Dangerously. Skipped permissions. Love it.

Natalia (36:19)

Always living dangerously. I wouldn't rec. I wouldn't recommend this to our enterprise clients, but no better way to do it. All right, so we are in warp, and I've just opened Claude and I'm going to. So what we did here is if you go to our plugins, you'll see that we have all of our plugins connected here in the every consulting. So we have a few things that if you're following closely, you might have also heard about. We have a PowerPoint skill. We have client work skill. This actually lives, Claudie lives in the workflow plugins, and that's all updated. So what we do is we go to Claude and. And let's say we're onboarding a new client. So we would say new client setup. And I'm going to pretend like I'm onboarding one of my favorite clients that we've been working with for a little bit now. Headway. So going to go to headway and what you'll see is it loads the skill. So now it knows what it's doing and it's going to read information. So. So as required by the handbook, Right. So now it has really clear instructions on what to do in this case, when it's creating, when it's setting up a new client. Now, often with AI, we think that things are going to be instantaneous, but I think this is just kind of like a myth, right? I probably like with AI for anything to be actually useful, it just takes time, right? And you want to do quality checks. And so we're probably going to see Claudia work for a while. Last time we set up a client, I think Claudia worked for about 30 minutes. But what you'll see here is that we've instructed Claudie to do a lot of work in gathering information first. So here, the first phases of her, of the work are looking through my Gmail, looking through my calendar, looking through the drive, looking through call transcripts just to establish kind of like a foundational set of like, truths before it goes and then starts populating information into a dashboard.

Dan (38:35)

This is so cool. Like, I. It's like, okay, you just launched five sub agents or four sub agents to, like, look through your Gmail, look through your calendar, look through your drive, look through your meetings to, like, get context on the project. Like, I just want to pause and be like, that is. That's crazy. That's kind of crazy that that's possible. And then it's going to go and gather that information and then put it in the right place into the spreadsheets that you Use to run the business.

Natalia (39:01)

That's right. I mean, the only thing that's crazier is that the alternative to Claudie doing this is me doing this.

Dan (39:09)

Suddenly, that feels crazy. But four weeks ago, it didn't seem so crazy. It was like, yeah, well, that's the job. But now it's not. So what do you do with all your time, Natalia?

Natalia (39:20)

Work with more clients, Dan, of course.

Dan (39:25)

I think that's actually really interesting, though, because one of the most important things about doing change management inside big companies is this feeling that, okay, if I do something like this, if I set up an agent that does all this stuff and legitimately, it can do a good portion of a job at this point, not a whole job, but a good portion of it, or at least the tasks of a job. What am I going to do? And. And that's where a lot of the resistance comes from, is like, I don't want to give it up until I have a vision of what comes next. And what we've been doing inside of every is on Think Week, we had a day called Promote Yourself Day, where the idea is like, literally figure out how to promote yourself so that you're not doing your IC job anymore. You're. You're one level above. And framing it that way, it's like, yeah, of course, once you're. Once you have hired a project manager, you. You wouldn't expect, like, if it was a human, if you had hired a project manager, you would not expect then to not have a job anymore. You would expect to be like, well, now I manage the project manager and I can do a lot more stuff. And same thing is true for this, which I think is really interesting to see.

Natalia (40:30)

Yeah, definitely. There's two kind of truths to that that are maybe non obvious. One is you are still managing something, right? So anytime Claudie inevitably makes a mistake or lacks, you know, sufficient information to have updated me in a way that I wish I had been updated, I have to go back and then give it context that will live somewhere in some command or maybe in the Claude file in order for it then to do that in the future. And this is the same way that you would build a relationship with any new staff member that you would bring on board. You're really building and cementing that relationship, and you're also investing in that relationship as being something that you can rely on in the future to get good work done. So that's one thing, right? When you set something like this up, it's an ongoing effort where we are Constantly improving it and constantly evolving it to meet your needs. The second thing is this is exactly where AI shines and this is where I get most excited about AI. I. My favorite thing about any of the work that I've ever done has been working with people. I love our clients, I love the companies that we get to work with. I love spending time with them. And any hour that I am not spending tabulating information, I am spending with the people that I get to work with. And that is so much more fun and so much more valuable to me as a person who gets to spend a little bit less time in an Excel sheet. All right, so let's see. This is a little bit of a dummy dashboard that we've set up for this demo, but this is effectively the structure of what we. The output that we would get. So here we have, you know, the total number of sessions that we might work to deliver with this client. The deliverables for that we will ultimately give to them any open items. And these are tracked again across the email, the granola notes, or the notion sort of meeting notes that we take. So if I say in a call, okay, I will follow up with this, then that's going to come up as an open item and how sort of important it is that I track that open item. So again, those would be cataloged here. Then we have the people. I've hidden columns that sort of explain who the people are specifically. But I talked about this earlier. We have some database sort of management principles here where every person has an ID and a title and also a team id. So we actually know how they map to each other and we understand how they're moving across different initiatives that we are, that we're working on. We have a team summary. So we have a good sense of, you know, how many people are part of a given team, how many sessions they've participated in, if there are any coming up. Again, this is all information that's populated, that's populated automatically. Once we've delivered, say, a training session, we have a session id and then we know what team participated, what people participated, what we covered in that particular session, where it was delivered, in this case, zoom, who delivered it and how many people attended. This is information that's really important to track over time so that we know really what we've done. And it's really quite tedious. And it has been historically to catalog this information and save and save it. And now it's just populated automatically as a session is scheduled. And once it's complete, it's just automatically marked as completed. Same for deliverables. Anytime that we deliver a new workflow or training material or a curriculum, this is all tagged here. And then we have source materials that finds and tags it to and gives us a status for what's going on. We have a feedback tab where that is also accumulated. And so all of a sudden we're going from a working relationship where I am looking for this information my drive and populating this dashboard manually to I just open this drive, I ask Claudie to update the the dashboard based on what's happened in the past week and it proactively tells me how we're doing with any given client.

Dan (44:31)

Absolutely incredible. And how long would this normally take you?

Natalia (44:36)

On any given week I spend at least 10 to 15 hours on just project management. Now with Claudia, I. I'm collecting information for an hour a week.

Dan (44:50)

That's incredible. And then you're spending an extra like 15 hours vibe coding. Um, so it's just. And you can. I love that. Natalia, this is so impressive. It's so cool. The work that you've done in the last year has been incredible. I feel very lucky to get to work with you. Um, I'm super excited for what we're going to do this year. If people are interested in following you or getting in touch with every to do consulting, where. Where can they find us?

Natalia (45:18)

They could find us on the every site. So we are at every dot to forward slash consulting. And I will give lots of kudos to one Natash who's just been an incredible partner to work with. We just. We're so lucky to have such an awesome team. And then we have a really outstanding lead who runs our financial practice. Brooker Belcourt came to every from Perplexity where he built and ran the finance arm there. And he now is in charge of all of the work that we do for hedge funds, private equity firms and all of our finance clients. So if you're in finance and interested in working, developing an AI strategy and helping need. Sorry. If you're a financial institution and need help thinking through your AI strategy, but more importantly implementing it, reach out to us. And also if you're a tech company that's doing this for your org, we have a fantastic lead on the tech side that we've been that has been leading that effort and we're excited to be doing more of that this year.

Dan (46:20)

Awesome. Thanks, Natalia.

Natalia (46:22)

Thanks, Dan.

Podcast Narrator (46:31)

Oh my gosh, folks, you absolutely positively have to smash that like button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure, unadulterated knowledge. Bombs About Chat, GPT. Every episode is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat, craving for more. It's not just a show. It's a journey into the future with Dan Shipper as the captain of the spaceship. So do yourself a favor, hit like Smash, subscribe and strap in for the ride of your life. And now, without any further ado, let me just say, Dan, I'm absolutely, hopelessly in love with you.