How We Built 'Claudie,' Our AI Project Manager (Full Walkthrough)

For AI to be useful to a company, it needs to be a coordinated effort.

Claudie is our project manager for the consulting work that we do with our clients.

You just launched four sub-agents to look through your Gmail, look through your calendar, look through your drive, look through your meetings to get context on the project. 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. I just want to pause and be like, that's crazy. That's kind of crazy. I mean, the only thing that's crazier is that the alternative to Claudia doing this is me doing this. I

am a bonafide vibe code addict at this point.

Natalia, welcome to the show. Hey, Dan. Good to see you. Happy to be here.

Good to see you too. So for people who don't know, you are the head of consulting at Every. We've known each other for a really long time. You've never

been on the podcast, even though you've been head of consulting for a while now.

I think you've been with us for like nine months or so, and you've done a fantastic job. So I'm just really excited to get you on the podcast and share who you are and what you know with people. Thanks. Yeah. I love our community and the podcast and just excited to chat and also hear how other people are thinking about consulting and AI in their companies. And so, yeah, happy to be

here. Awesome. So one of the things I think could be super helpful for you

here. Awesome. So one of the things I think could be super helpful for you to share is over the last nine months, you've had a front row seat talk to some of the top companies in the world about how to do how they do AI deployments. And those are people that have reached out just to

chat. Those are clients that we work with, do a lot of training and integration

chat. Those are clients that we work with, do a lot of training and integration and implementation work with like hedge funds, PE for 400 to 500 companies, lots of name brands that you know about. And so I just feel like you've had this front row seat for what works and what doesn't. And like, what are the patterns that the companies that are really doing well at AI adoption and AI transformation are

following? And I'd love for you to share some of those things. Yeah, that's

following? And I'd love for you to share some of those things. Yeah, that's

true. I mean, I think we have been in a really unique position in the consulting work that we do at Every because, I mean, I personally have spoken to over 100 companies in the past year, hearing their concerns around how they could be using AI, trying to benchmark how other competitors might be using AI, and then trying

to get a sense of what actually works. And It really comes down to two things. We talked about this in a post that we did a few months ago

things. We talked about this in a post that we did a few months ago about kind of learnings from those 100 companies that we talked to or so. And

one is you really need an organized effort when it comes to using AI well in a company. For AI to be useful to a company, it needs to be a coordinated effort. For AI to be a high leverage tool at any given company, it needs to come from the top down. So unlike historic, you know, kind

of like software where, you know, someone heard that Asana was helpful for a company to use, then they just let the CTO sort of buy it and then hope that people would use it. If there isn't a coordinated effort to understand what the possibilities are in using AI at a company, creating tailored opportunities to

actually getting leverage and value out of those sort of use cases and then tracking how people are actually using it and then really implementing the ways in which it works really well, AI really kind of doesn't go nowhere. It ends up being that there are like a few high-powered users that get a ton of leverage out of it. And then everyone else is sort of floundering. And so there's

it. And then everyone else is sort of floundering. And so there's really two things that we see working well at companies. One is it comes from the top down. So leadership understands that this is a really high leverage tool and it's fundamentally changing the way that we think and relate to work. And two, they're

really giving people an opportunity to become champions and owners of what it means to work with AI and creative power to explore how to rethink their roles and how to train other peers and other people to use AI really effectively given kind of this new paradigm that we're in. So

it coming from the top down, there's a coordinated effort and people, AI champions really being empowered to think creatively, try, experiment, fail, around AI initiatives and then really doubling down on the things that really work. Yeah, that makes a lot of sense. I think some color I can give from my seat because I don't see

sense. I think some color I can give from my seat because I don't see nearly as much as you, but I do see a lot. On the top down front, the CEOs that are actually doing, not just saying you need to do this, but actually doing it, like those are the companies that go the furthest. 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. Like, you know, Atobi from Shopify is a good public example where he's just like 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 bottom's up 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 like try some new technology. But will use it if they're shown, hey, this is like actually

new technology. But will use it if they're shown, hey, this is like actually something that is going to help you in your job. Yeah, I mean, Dan, I feel this with you all the time. You know, 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, you know,

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

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 where you can go and just how far it can take you.

Do you have any specific stories? Obviously, we can't share from clients by name. But

do you have any specific stories about unlocks that you've seen that were particularly powerful?

Well, 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 at CEOs into AI. Generally, it's great to promote your power users internally. But like

AI. Generally, it's great to promote your power users internally. But like

I 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? There's two things that come to mind.

One is with a private equity firm that 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

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 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 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 we started out our work together by,

things that... we did together 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 the most detailed kind of like, and every single task that they do, from

research to diligence, to market mapping, 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

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 map that we end up

leverage. And so what we ended up with 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 together. And 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 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 aha moment that is wild, where investors will 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. That's interesting. Tell me more about that moment and like what that kind of either automation or workflow, what is that? Is that like they're using ChatGPG well, they're using Cloud Code well, like, yeah, go more deep into that. So in that case, it's a few things. One is this particular firm has

that. 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 kind of like the IP of the firm, right? 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 beyond just the number crunching? And that's something that is 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 CHAP-TPT is able to do effectively, very, very effectively. And so what it looks like for them is connecting the right context, really,

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

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 to now you get like a really solid draft in like 30 minutes. 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 hard. It's hard, but like that's sort of table stakes is it needs to

is hard. It's hard, but like 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 were 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, all these other

kinds of tools where you kind of expect the, agents will 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, like there's like three different places you could go. You can go into Stripe, you can go into ChartMogul, maybe you can go into PostHog. But like our head of growth, Austin, has like a particular like way that he's defined what our MRR is that instead of forcing the agent to like figure that out from scratch

every single time, sort of putting into place, here's how we think about what MRR is. And that transfers into like, you know, 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. Yeah, that's totally right. I think it's the hardest part of AI

of AI. 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

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 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 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

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. 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? Yeah, I

mean, let me think. 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 the plan, you delegate, you assess, and then you compound what works or kind of like the learnings of 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

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 work around. So they could solve a lot of small issues, but they weren't able to address these big sort of like meatier

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, yeah. 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. What do we think is possible here? Like what are the kinds of

engineering orgs. What do we think is possible here? Like what are the kinds of speed ups that we expect? In engineering in particular? Yeah.

You know, that's a difficult question to answer, but, you know, I would say we're 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. 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 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 mind totally blown over the last 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 6 a.m. vibe coding. Right.

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 going to be a lot of people coming after you that are feeling the same way and that we're going to 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 a new way of working that's really valuable. So

I'll be honest. So yes, I think I will... Admit that I am a bonafide 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 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 Natasha Agarwal, who is our applied AI engineer that is on 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 Natasha 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 a.m. And we would basically just vibe code from like 6 to 9 a.m. And

a.m. And we would basically just vibe code from like 6 to 9 a.m. And

it all started with us asking, you know, could we create this really ambitious project, which is, you know, project management is really, really, time consuming for any consulting business, right? Any great consulting business has an entire function around project management.

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 Nishesh and I sort of asked, you know, do we think

given project. And Nishesh and I sort of asked, you know, do we think we could really spin up 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. Like I would say we got 85% of the way there three times and then had to scrap everything it given kind of 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

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 in 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 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 to 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. Growth is good.

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That's really cool. I want you to show us the system. Before we get there, I want to point out that interesting pattern, which is instead of just expecting it to happen inside your nine to five, like you actually just carved out three hours outside of your job to like play. And I think that's like a... 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're 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 your 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 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

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 risky way where they're not going to get behind in their job. They can kind of like learn

it ins and outs and fail. And then after a couple times of the cool iterations that they're like, holy shit, I had this, I'm driving a car now. Like

I'm not driving it working buggy anymore. And I think that's so valuable. And it's

really hard. Actually having that creative space is very, very counterintuitive to the way that we usually work, right? How much of our time is really spent in traditional jobs, just like figuring out, like seeing if there's like a new way to do things, right? 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 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 this 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 attach, 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. Yeah, I think that's another really good pattern is you have a test shoes and 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 and 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 is going to only go as far as I go in

is okay. The company is 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 in AI, like just literally sit with me and talk me through. Okay, I have this project on my mind that I feel like would be

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 just like trying to knock out this like really ambitious, interesting thing. And

I think that's actually a really good way to get yourself addicted to bi-reporting is have someone who's sitting next to you as you put your coal in the water.

Yeah, absolutely. I'll also say, I think there's something to be said about you need as much engineering power and sort of like AI know-how as you need an understanding of 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 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 cloud 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 know, you can have really great engineering power, but you

and worked. And so, you know, you can have really great engineering power, but you also need to have like the know-how to get to something useful. That makes sense.

Do you want to show us a little bit of Claudia? Yeah, let's do it.

Okay, cool. Can you see my screen? I can see your screen. All right. So

welcome to the Every Consulting GitHub page. And this is where, this is where Claudie lives. 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, 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

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 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 with Claudie. So if we want to do a quality check on the data that is collected, if we want a weekly update on what's going on across clients, if 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

someone new. Then we have a list of tasks. Tasks came out fairly recently, and they have been instrumental to Claudia 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 as you have some skill files, general principles, you know, we want

of agents. So as you have some skill files, general principles, you know, we want things to be well formatted. We want things to be written in a way that reflects, you know, 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 Claudia, our project manager. And these are some of the commands to kind

project manager. And these are 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 project 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 ClaudMD 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 it 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, right? 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 If you want to be a good employee, you want

not reactive. Don't wait for 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. Every interaction builds or erodes trust. Formulas over

manual entry. These are just 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, I won't go into the rest of the details, but What you'll see here is this is actually a fairly concise file. We're not giving a job description that is incredibly detailed. Claude

concise file. We're not giving a job description that is incredibly detailed. Claude

is really, really smart. Opus 4.5 is what Claudie 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. This is so cool. There's so much in here. But what I want to do is just show like, I don't want

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, send a new client. It's usually

a big deal. Like it's a lot of money, but like 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 like how that works? Yes. So let me

the project. Can you take us through like how that works? Yes. So let me take you, I'll create a new warp page here. All right. So we're going to open Claude.

Living dangerously. Dangerously skipped permissions. Love it. Always living dangerously. 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

Every Consulting. So we have a few things that if you're following closely, you might have also heard about, we have a PowerPoint. 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 Claud and let's say we're onboarding a new client. So we

would say new client setup and I'm gonna pretend like I'm onboarding one of my favorite clients that we've been working with for a little bit now, Headway. So I'm gonna go to Headway and What

Headway. So I'm gonna 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 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? Probably like with AI for anything to be actually useful, it just takes time, right? And you wanna do quality checks. And so we're probably gonna 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 Claudia to do a lot of work in gathering information first. So here, the first phases 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. It's so cool. Like, 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 contacts on the project. Like, I just want to pause and be like, that's crazy. That's kind of crazy. But that's not the goal. And then it's going to

crazy. That's kind of crazy. But that's not the goal. 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. That's right. I mean, the only thing that's crazier is that the alternative to Claudi doing this is me doing this.

Like 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? Work with more clients, Dan, of course.

I think that's actually really interesting, though, because, you know, one of the most important things about doing change management inside big companies is this feeling that, OK, 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 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 I

see job anymore. 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 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

the same thing is true for this, which I think is really interesting to see.

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 cloud 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,

AI shines. And this is where I get most excited about AI. I,

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

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 the output that we would get. So here we have the total and Merv sessions We might work to deliver with this client the deliverables 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 catalogued here. Then we have the people.

item. So again, those would be catalogued here. Then we have the people.

I've hidden columns that, you know, 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'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 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

we've done. And it's really quite tedious and it has been historically to catalog this information 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.

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 in my drive and depopulating this dashboard manually to I just open this drive. I ask Claudie to update the dashboard based on what's happened in the past week. And it proactively tells me how we're doing with any given client. absolutely incredible. And how long would this normally take

you? On any given week, I spend at

you? On any given week, I spend at least 10 to 15 hours on just project management. Now with Claudie, I am collecting information for an hour a week. That's incredible. And then you're spending an extra like 15 hours live coding. So it's just you can say,

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. 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 can they find us? They can find us on the Every site. So we

are at every.to forward slash consulting. And I will give lots of kudos to One, Natasha, who's just been an incredible partner to work with. 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

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 has been leading that effort. And

we're excited to be doing more of that this year. Awesome. Thanks, Talia. Thanks, Dan.

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