The Startup Ideas Podcast - Become a $1M/yr FDE (Full Course)

Episode Date: October 1, 2026

Google's most advanced audio models are LIVE, try them for yourself: Gemini 3.8 Live: https://startup-ideas-pod.link/gemini-3.8-live Gemini 3.5 Transcribe https://startup-ideas-pod.link/gemini-3.5-transcribe Gemini 3.5 Live Translate: https://startup-ideas-pod.link/gemini-3.5-live-translate In this episode, I talk with Vas from Varick about what it takes to put AI to work inside a real company. Vas makes the case that AI pays off through process reengineering, and he walks me through the exact method his forward deployed engineers (FDEs) use: interviews, process mining, step sorting, and agents built inside existing systems of record. We go through real engagements, including a $5B public software company and an accounts payable overhaul that cut the cost per invoice from $31 to $6. By the end, you get a clear picture of the FDE role, the business opportunity behind AI roll-ups, and a five-day plan to start on your own. Links Mentioned: FDE Presentation: https://startup-ideas-pod.link/FDE-slides Vas’s Article: https://startup-ideas-pod.link/vas-fde Timestamps 00:00 – Intro 01:31 – Sponsor: Google 03:58 – FDE Overview 05:02 – AI Roll-Ups and Process Reengineering 08:04 – The Personal Systems Analogy 09:55 – Understanding a company’s process (step-by-step) 14:11 – Case Study: $5B Software Company 18:11 – 4 Buckets for Every Step 19:04 – Build Inside Systems of Record 21:36 – Case Study: PE Portfolio 23:43 – Selling to C-Suite Executives 27:07 – Process of Mapping Five NetSuite Companies 28:39 – Example: Accounts Payable Process Map 32:54 – Case Study: 60-Person Accounting Firm 34:51 – When to Use Code, Agents, or Humans 36:00 – Choosing AI Models 38:16 – Sidekick vs Background Agents 40:29 – The 3 Skills of a Top FDE 42:25 – Why FDEs Earn So Much 45:26 – Five-Day Starter Plan 47:39 – On-Premise Hardware Demand 48:36 – OpenAI Private Intelligence 50:20 – The Full Playbook 52:01 – Closing Thoughts Key Points AI pays off when you re-engineer the process first, then build agents into it. Map the real process with interviews, system-of-record mining, and existing docs. Sort every step into four buckets: delete, plain code, agent, or human decision. Build agents inside the tools clients already use, like Salesforce, NetSuite, and Slack. Sell the outcome each buyer cares about, and prove it with before-and-after KPIs. Top FDEs combine domain knowledge, production engineering, AI judgment, and strong communication. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND VAS ON SOCIAL Varick Agents: https://www.varickagents.com/#hero-section X/Twitter: https://x.com/vasuman AI Forward Deployed Engineers: https://learn.varickagents.com/fde-in-30-days

Why are people getting paid a million dollars as forward deployed engineers? It sounds crazy, right? But if you think about it, it isn't. Because if you're deploying agents and you're able to drive $5, $25 million of value for companies, would they be willing to give you a slice of that pie? It turns out yes. So the question becomes, how could you become a forward deploy engineer? How could you deploy agents to make companies run more efficiently, drive revenue,

Lift margins. Well, today, I brought on Voss from Verick agents for an inside look at how this works. He's sharing examples from client engagements with the details change that you just don't get to see publicly. This is for the first time ever on the internet, and that's really cool. I think a lot of people have talked about forward-deployed engineering on the internet, but they haven't gone into concrete examples for how you can actually do this. By the end of this episode, you're going to understand how to find the work, automating. You're going to be able to decide where agents belong. You're going to understand

open source versus close source. You're going to understand where Muse, Grockbot, and dots fit in and all of this and how you can start putting this into practice. This is a masterclass for how to become a forward deployed engineer. I did one other episode with Voss not too long ago, but we kept at high level. And by popular demand, we're going deeper. So send this to a friend, like and comment, and I'll see you at the end of the episode. One of the most common questions I get nowadays is Greg, how are you using voice AI in your everyday life? Well, today I'm going to break it down in 60 seconds. I'm going to give you my voice AI toolkit right now. And this section is sponsored by Google. So Google's actually been crushing it with voice AI. I've been using Gemini 3.8 live recently. So what's really cool about 3.8 live is you can just go and have a phone call basically with it and say something like, Hey, you know, how am I doing with my launch campaign? And it's connected to all my tools and it's basically running my business in the background.

So I think about it as if my, it's almost like my chief of staff. It's smart. It's intelligent. It's connected to my tools. And it allows me to live my life while having voice AI help me run my business. The second is Gemini 3.5 transcribe. Now, what's really cool about this is I can go and leave voice notes and it'll go and parse those voice notes. It removes the ums, the likes.

And how I use it is, you know me, I've got a lot of ideas. I got a lot of startup ideas. And it just allows me to go on walks and just basically give those ideas and get back text that is clear. And that also just allows me to remember because frankly, I forget things. The third is Gemini 3.8 flash text to speech. Now, what's really cool about this is you can go and say, hey, build me. a voice that has a Brooklyn accent, and it'll go and do it. And you might be thinking, well, how can you actually use that? Well, you know, there's so many products that need a voice. You know, for me,

recently I've been building a mobile app and I just included an onboarding voice into the mobile app. And the last thing is speech to speech. You know, not everyone speaks English. So if you're doing business in Mandarin in China, you get this real-time translation via Gemini 3.5 Live Translate. And it's just opens up new market. So these are just four tools that I've been obsessed with lately. Shout out again to Google for sponsoring this part of the episode. And I'll include links for where you can play with these tools and models in the description. Have fun with it. Have a creative day. And I'll see you at the next 60 second master class. Voss, by the end of this episode, what are people going to learn? People are going to hopefully learn the end-to-end workflows that we're seeing as

part of AI transformation and better understand how they can go ahead and do this themselves. Yeah, because there's lots of talk, and I'm sort of guilty for this too, just keeping at high level. Sometimes I'm just like, here's an interesting topic, here's an interesting business model, just apply AI. But the big question is, well, how do you actually apply AI? What does this forward deployed engineer piece look like? So we're going to actually get into the nitty-gritty of that all, right? So that's the commitment you're going to make to the person listening to this. By the end of this episode, they'll understand what it actually means to apply AI. What does it actually mean to forward deploy into something? And where are the business opportunities and monetization opportunities and how big is this thing? Is that the commitment you are going to make today? 100%. You have my word. Let's do it. All right. Cool. So I put out an article a few weeks ago, and it was titled, Don't Apply AI, obviously a play on, you know, applied AI, et cetera. And again, the point of that is to go into, you know, AI isn't something

that can be applied like a code of paint. It's something that really involves process re-engineering. And that's the goal of today's kind of presentation. And thanks for having me on. So as I'm sure everyone already knows, and, you know, Greg, you talked about this a week or two ago. This is already happening, right? A roll-ups is a huge part of the private equity playbook these days where you'll buy a firm that runs very much on people, outdated processes, maybe outdated software as well. For example, accounting firms, IT shops, law practices, et cetera. And these AI holding companies or AI transformation companies are buying up these portfolio companies and putting AI engineers for deployed engineers inside of it. So what they do is they'll

find the processes, they'll map out the systems of record, the exceptions, what happens where, what cycle time is recurring as a result of handoff between two different pods of people, and they'll rebuild that process with agents from the ground up. Thrive, for example, it comes to mind. They have 35 engineers across 70 firms as of, you know, be creating this and doing some research. I'm sure that might be even higher now. And what they're seeing is that it's actually quite successful. The numbers move. You know, tax returns 30% faster, 98% accuracy. Agents are actually able to take work off of people's plates, and it is feasible. It's possible. The only caveat being is that it's a lot more involved than people had maybe

initially surmised. So one example is gross margin at one call center firm was 60% and above, which is actually quite high. And as a result of that, you know, when they buy the firm, let's say they buy it for a billion dollars and they increase the margin, and they double it, for example, in the best case, that actually translates into the valuation of the entire company. So all of a sudden, you can buy it for a billion, implement AI across, you know, three, six, 12, 24 months, whatever that is. And then you can sell it for $2 billion or $4 billion or $8 billion. And that's really the goal of these companies. Cool. Let's keep going.

So here you can just see a few different examples of that Thrive Holdings with Josh Kushner, General Catalyst, AI-enabled roll-up. and then the people who are doing those steps of two and three are those four deployed engineers. Yeah. And I think like we'll get into that by the end. But like I think that's the big question a lot of people have is like I think people hear this and they're like, okay, cool. But like you're basically saying like buy a business, add AI and, you know, question mark, question mark, profit. You know what I mean? So I think like the big question, it's like, okay, but how do you actually do this?

this, we will talk about that. 100%. I'm not going to keep it super high level. We're going to get into it. And the analogy that I like to get everyone, as you're thinking about what it means to be a forward-deployed engineer, is your life is already complicated, right? You have five different inboxes, four file stores, Google Drive, Notion, ICloud drive, desktop. You even have five messaging apps. You have I message, WhatsApp, Slack, Google Calendar, Outlook calendar, all this stuff. And it's actually very hard to understand exactly how you like to use your systems, right?

So which app is the easy part? You can make a simple tool call, for example, to an API of whatever system of record of you're choosing. But if a client emails you, it's a Gmail, it's work, but if a family messages you, then it's I message or it's WhatsApp. And understanding this is quite complicated, even at a personal level.

So imagine at a company, and this is to your point, right? This question mark, question mark, question mark, people are so annoyed at this like, you know, AI is going to fix everything, just use AI. Because they know that it's actually quite complicated. Imagine a company, right? This company has acquired eight other companies in the past.

So now they're existing in five different regions. They're in Sao Paulo. They're in Bangalore. They're in Australia. They're in Sydney. And they have 23 different systems of record. And each region is doing things differently. Chicago is on SAP, Salesforce, and Workday.

Toronto is on NetSuite HubSpot and ADP. and so on and so forth. So really it is quite complicated. And this is exactly why, again, AI cannot be applied. If you're applying AI over this entire company, which literally spans the entire globe, you're going to end up with just making shit faster. I hope I can curse on this podcast.

And leave that out for the answer. Absolutely. And that's the fundamental issue. So now again, I don't want to be too beating a dead horse zone. The problem is very clear. It's very complicated. So how do you actually go about doing this? This is our view, and I'm sure there's many different ways of doing this,

but our view is process mapping, then re-engineering, then building, deploying, and rolling out. And there's a few different ways that we do that. So if you were to go into a company on day zero, here's what I would suggest that you do. The first is interviews on the human side. So let's start off with a single department, right? You'll have, in finance, you'll talk to the head of A.

AR, reconciliations, billing, banking, FP&A, etc. And you'll work your way down from there. And the reason why these interviews are super helpful is because a lot of information lives in their heads. Very rarely do you have a very clean document source that you can just point your agent at it'll learn it and go from there. Very often it's not written down. It's not documented.

And we hear this all the time. When we talk to companies, they tell us saying, oh, yeah, this person's been at the firm for 20 years and they just handle it. It's so common. It doesn't matter the size of the company. It could be a massive Fortune 50. It could be a small SMB. They all have these critical key people where information lives in their head. Yeah, I mean, people. If you think that you're going to walk into a company and they're going to have like an obsidian second brain hooked up to AI agents, you are just mistaken, my friend. You know what I mean? Like, there is no second brain. happening here. Yeah. There's nothing you can just plug into call an API

and all of a sudden you have your knowledge store. It's kind of on you to create that. And it's per department and it's cross departments and it's very, very involved. Okay. So step one is basically in a sense it's creating like a human API.

It's getting all the you know, data systems, SOP, all that stuff into modern digital systems. Absolutely. You always have a good way of articulating.

I like that. It's a human API. That's what it is. And you have to understand why do they do things the way they do them today? Who really decides them? Which step is theater? Which step is legitimate?

What happens when exceptions take place? How often do exceptions take place? All of these things are not written down. From there, we move on to then mining the systems of record. and mining everything else, right? So really, there's a lot of data that exists in their Salesforce, their NetSuite, their dynamics, their workday, et cetera. And that doesn't mean that it's written down, right?

It's not a document which has like one step one, step two, et cetera, but you can sort of create that SOP by living on top of their systems of record. So for example, if I have real-time access to a Salesforce, for example, over the course of, you know, three to four weeks, I have a pretty good understanding. of what sort of data enters Salesforce, how often is it getting corrected, et cetera. And this runs constantly. And you can use AI to analyze what the hidden meaning is behind each one of those actions, right? So if a certain record comes in and it's from a certain company, it's of a certain size, we see these actions taking place. And now we can create sort of a graph

of, okay, once something happens, once something enters of a certain category, A goes to X, B goes to Y, C goes to Z, et cetera. But obviously, if you just do that without the interviews, you're missing half the picture. The final aspect is what lives outside of them. So then you actually go into the existing documentation. Sometimes outdated. Sometimes it's pretty good. This is in SharePoint. This is in Drive. This is in Notion. This is in Slack. This is in Teams. This is in Gmail. This is in spreadsheets. And again, through these three steps, you have literally the entire company's picture. And it varies, the split amongst companies, right? For a very large company, they've had 10 years of Salesforce historical data for you to go off with.

For an S&B, they probably don't. They probably have mostly in people's heads, in interviews, and no processes, no system of record, no software. So it's up to you to determine what angle you need to take per company, and it varies. Crystal clear. So here's a very concrete example. You wanted to go away from high level. This is what we're trying to do.

This is a public software company. This is actually an engagement that we completed. So it was a $5 billion in revenue company. They have over 150 products, tons of solution consultants, and we were brought in by the CRO, and they're public. So this actually makes them quite complicated. You have to deal with regulation, certain laws apply, et cetera. So the process document, which they actually had, and they had completed this, I think it was

engagement with Deloitte that mapped this out for them, was you build a quote, then you submit it, then it goes to deal desk, then you approve it, then you send it, you negotiate it, and you sign it. very cut and dry, and this is always what happens if you just look at one angle. Even just one person, right? They might tell you the wrong stories. You have to interview other people as well. But what the reality was, and this was as a result of process mining agents that we had deployed over their CRM, which I believe was Salesforce. It's actually a 20-step process with seven different loops.

So from step one to step four or five, then you loop back to one if there's an issue. And that's 61% of requests actually follow that. loop. Then later down the chain, legal sends it back 12% of the time, later down the road, 30% of the time, a new quote has to repeat getting approval, et cetera, et cetera. So none of this was really documented. And it was up to us, and actually my team of four deployed engineers, to go in and create this process mapping. It's interesting. The way I'm thinking about it is every business is sort of like a factory. And a factory, if you think about, imagine you're looking down at a factory floor.

You know, there's an assembly line, there's different parts of the assembly line, there's probably maybe some offices where people are doing some accounting. And they all kind of work together to create a product. And what's really cool about what you're doing and just forward-deploy engineering as a service in general is you're basically saying, like, how do I distill every business down to these systems or set of systems per department. And then you're basically, what's really cool is like now, well, we have everything we need to actually get the, you know,

usually the work done. There's the step one is like the digital tools, like the sales forces, the net suites, all those products. Then there's the agents that actually do the work. And then there's the human beings. There's oftentimes like a human being stepped. to it, right? Like, not everything could be fulfilled by agents. So what you're doing here is you're kind of like exposing the full system. You're acknowledging that a lot of people think that they

have a full system, but it's actually usually just the tip of the iceberg. And you're basically saying, how can I open up this system, optimize it? And then I would imagine, like have evals or which you can talk about, but basically make sure that you know, it's doing its job at the, you know, as good as possible. Yeah, 100%. And it's funny that you mentioned that like the mapping of the processes,

let's say across a sales department as we have here, it's actually never been shown before to anybody the department so cleanly to the point where you know the CRO and CFOs are telling us like I feel like you understand our department better than we do and it's true because no one has done that yet and that's the real issue right if you go and say hey we want to use AI well on what what are we doing what what is the broken process what's the problem we're trying to solve and that's why you need this step and then to your point of you know not everything can be agentic some things should be deterministic etc these are the 20 steps

and this is what we bucket them into. Right? There's four buckets. One is delete the step. This shouldn't exist in a post-AI world. Some steps are playing code where you have, you know, rules. There's no judgment required. For example, it's a simple API call. If this happens, then this happens.

Five are agented, right? You have building a quote that requires some level of judgment with who's the customer, what's it worth to us, how much do we need to spend, what resources do we need to allocate, et cetera. And then finally, to your point, humans in the loop, human decision makers who are going to be handling the most risky tasks, things that you really cannot afford to, you know, just get wrong. And it's approval, it's negotiation, it's signing, it's submitting payment, et cetera. Yep. So this is also how we do it.

And this is one thing that I really want to call out, right? A lot of people think of AI agents as a new surface. And we take the opposite approach. what I've seen really resonate with a lot of executive leaders. And it's what I strongly recommend to anybody who wants to be an FTE is pitch yourself

as building these agents inside their systems of record. So one thing that we do is, for example, we'll have agents that mine your Salesforce and take action in your Salesforce. And there's no new surface that you need to go into to interact with this.

We're not asking you to replace your Salesforce, you CRM. That's impossible. Real quotes from our customers. They spent several years and several million dollars, I think $10 million one time, on migrating from one ERP to the next. Same thing for one CRM to the next, et cetera. If your pitch for AI is, hey, we're going to move you from Salesforce to this AI native CRM, you've lost them. And maybe in SMB where they don't have a CRM, you can put them on one. That's great.

But otherwise, try to be inside assistance of record, even here when you have human in the loop, it's a message in Slack. And that's what we've really seen resonate with our clients as well. So I strongly recommend this being the case. And this is also how you don't have to retrain staff, right? They are already used to this. You're working inside their records. And you can just hit the ground running with a much faster rate of utilization,

much higher efficiency, et cetera. Yeah, I mean, in general, like you're trying to sell anything to anyone. Like, you want the path of least resistance, right? So I think that if you're going to ask them to completely move softwares and then kind of like introduce all these new concepts to them because they probably haven't heard of a lot of these. I mean, some of them maybe have, but some of them probably haven't around this whole agentic world that we're living in. So yeah, my point, my take here is like, or I'm just agreeing with you. Like obviously it makes sense.

Like if, especially if someone's listening to this and like wants to be a forward deployed engineer or wants to like start a forward deploy engineering business, like path of least resistance. Yeah, 100%. They haven't heard of Jev or Muse or any of his stuff, right? So just keep it simple. Yeah.

Here's another example, right? And we mentioned there's a lot of PE firms that are looking to agentify their stack. For anyone who's not very familiar, a private equity firm will have ownership in dozens of companies, right? And their mandate is, all right, let's roll out AI across all of them. And it's impossible to do so, right? If you have 26 different companies, that's 26 different engagements. And then each one of those companies, 26, has 10 different departments.

Each department has 10 different workflows. And all of a sudden, you'll need to deploy 50,000 for deployed engineers across, three years if you want to even make a dent. So the way that we recommend going about this is grouping together portfolio companies. Oftentimes it's on systems of record. So for example, if we have a P firm with 26 different companies and we're tackling finance for all of them, we'll group together the five that are on NetSuite as their ERP and four that are in dynamics. So if you're an FDE, your job is to make sure you really understand what capabilities already exist in each software. So what does NetSuite offer? What does it not? What does it not? What does it not? And then where can you fill in the gaps? And how do you make that talk to whatever else they're on, which is Ramp or Brex or Tipalty or Blackline or concur or expensive thigh? On the sales operation side, it means some are on Salesforce, some are on HubSpot. How do you integrate that with everything else across their stack and their spreadsheets, etc. You get the idea. And what happens is if you go about it this way is you,

only have to tackle similarities, right? Instead of getting pulled in different directions, you just streamline with one entry point. And that's a system of record. That's what we strongly recommend, especially if you're an FDE. If you're being asked to do 10 different companies, you have to simplify it for yourself. It also simplifies the politics. You know, you're working with CFOs with the same buyer over and over again. It's interesting that the CFO is the sponsor. Like, it's, you know, you would think that it might be the CTO or someone just from technology. Yeah, we very often get brought in with CIOs.

And then that's just from like the we want to identify everything perspective. But also it's like CEOs, CFOs, CROs who are like, well, this is my department and I want something there. Now, caveat thing, CFOs are self-proclaimingly notoriously skeptical. So good luck selling into CFOs. But that's just how it goes. Yeah, I mean, to me, they don't want to offend any CFOs listening to this, but to me, like, CFOs just care about optimizing costs. So when they hear, you know, agentification or agents, they're just thinking, how do I lower my costs, increase margin? Are you finding that's like the best way to get in like, hey, like we're going to like deploy all this stuff and like you're going to save a bunch of money? Is that how you how you're thinking about it or am I missing it something? No, I would say that's largely correct. We obviously deliver value on three buckets, right? One is cost savings, but the second is revenue uplift and third is risk mitigation. And what we've seen actually resonate with CFO is yes, cost cutting absolutely. First and foremost.

But then also, like, how long does it take you to close your books? And we've heard like, about 22 days, four weeks, six weeks. It's like, okay, that's over a month. It's called month and close. What will be talking about here? So if we can bring that down to four days or eight days with higher accuracy, at the same cost that you're running it today, even, they've really found that to be useful. and then obviously further, if we can cost cut down the line, that's even better. But it's a bit of both. It's not just that we want to keep our existing slop, but cheaper. They actually do want to move towards faster, more accurate, et cetera. I mean, maybe it's just like you speak to who you're selling to.

So if you're speaking to like a CIO or CTO, it's like the efficiency. Maybe it's the efficiency. it's, it's, you know, the output, it's, it's productivity, it's stay up to date, you know, it's all of that. But like, then it gets like handed over to the CFO. It's like, they might not care as much about the efficiency in terms of like, or the output that it's like way cleaner and nicer. They might just care about like the revenue uplift and, and, and, uh, lowering costs. So, I mean, obvious to say, but like that's, you know, for people listening, it's like, sell to who you're speaking to. Yeah, 100% sell the outcome, right? If we're talking to a CHRO or a chief people

officer, it's, you can hire better people faster and train them quicker on day one. It's not about the cost. They don't want to save money here. They want to get way better output. So it varies. Cool. So here's again, deep dive into a company. concrete example. Five portfolio companies all on NetSuite, but even then they have very different ways of running things. You know, 12 steps, 9 steps, 15 steps, 18 steps, 13 steps, and then imagine, you know, they have regional differences, regional variances at each portfolio company level. So again, this is why mapping it out is so important. And this is useful not just for each

portfolio company, right? Each CFO has the same, you know, investment that we talked about earlier. where they see this and you understand their department better than they do. But the PE firm does as well, where they can see things get mapped out and streamlined. And what we've seen is a lot of PE firms ask us for, okay, what's the playbook, right? Tomorrow when they acquire a new company, what process should they follow? How should they go about it? What software should they adopt? And this mapping of saying, hey, look, if you're on NetSuite, this is the concrete way of doing things.

Here you go. You turn this into six steps, agents in certain locations, et cetera. Now, this is obviously a dramatization. You will very rarely get to as clean as like, hey, everyone's on six steps and we did it, guys, perfect efficiency. But you'll actually come quite close to this. And you'll get that by being very deeply involved, mapping it out, and working with the stakeholders, and then re-engineering it with a lot of foresight and a lot of thoughtfulness. So again, this is actually, and this is anonymized, obviously, we can't share details about our clients' actual workflows, but

this is a real process mapping for, I think, it was accounts payable, yeah. And it was 17 different steps with exceptions being handled, right? So these are all exceptions and these are all the steps, etc. And this we uncovered over the course of, I believe, two, three weeks with interviews, process mining, etc. And this is actually a less complicated, you know, workflow for them and in general. We've seen workflows that are 40 steps

or 200 steps. It gets crazy. But your job as an FTE is to, one, map this out. Don't skip this step. Don't skip educating the client on what their mess is. They want to see this. It breaks their heart, but they need it. And then you turn it into, here's the agentic future. One, visually looks much cleaner so they can take a deep sigh of relief. And two, it actually runs much smoother, where you have agents that are handling what they need to handle, and you have humans in the loop where it needed, et cetera. These green boxes are, you know, steps after, I think, human decisions. And this is still like an exception where, like, you know, determinants to code, et cetera. So this is your job as an FDE.

By the way, if you're selling this to a CFO, the way to do this is, like, you have the agents there. and then you put the estimated cost per month of the agents. Because it's going to be like shockingly low, right? Relative to human beings. 100%. And you have the same thing with like the accuracy. And all the KPIs that you can throw at them, they love that. Any C-suite, right?

And part of your job is based on those KPI, say, here's how bad it runs now and here's how it's going to run in the future. And then you hold yourself to that standard. So six months for now, you can say, I did this. Love it. And you keep stealing my thunder, Greg. That's exactly what we're showing here. So, you know, you show them, you're from 17 process steps to seven, cycle time from 24 days to six,

exceptional loops from six to one. This is a really good one, the next two, the straight through rate of an invoice. We drove that from 18% to 87% for this client. And that was a game changer for them because all of a sudden, literally a majority of their invoices were going through exception routes. Like, that's terrible. That means you have really bad process. and we fix that process. That's a process re-engineering flow, by the way. That's not even all about agents. And then finally, we drove the cost of handling a single invoice down from

$31 to $6. It's an 80% reduction. So to your point, right, you show them that in this slide. Right. And to the people who are like, Voss is just replacing human beings with agents, the other piece of this is if you're able to optimize a such that their cost per invoice is going from $31 to $6. Now all of a sudden that company has more margin. Yes, they might take some of that margin, but they also might give back some of that margin to customers. Absolutely.

And also use that margin to hire because the truth be told, right, the clients that we work with are very often like Fortune 1000, Fortune 500, they want to win. They want to grow. We've very often seen that it's reallocation of resources. It's not about doing mass layoffs, right? They would rather have their best people in finance, not spend their time doing manual invoice routing and approvals and parsing of an invoice. That's ridiculous. It would rather have those people on higher leverage tasks, right? Planning out FP&A, certain aspects of that. or migrating them across cross-functional or building their own FDE teams. So I hope that no one is under the impression where these are going to replace everyone's job. Yes, there may be migration of job. But I do think that the companies who want to win are reallocating. They're not doing 50% layoffs.

That's crazy. Cool. Another example, 60% accounting firm. This is more of the SMB side, right? And this is your first FDE project, you should probably start here. this was not actually one of our clients, but someone else in the industry that I had chatted with. They had $12 million in revenue, 400 clients, four systems, and what made it hard is, you know, every client sends its books in a different way.

I actually heard that some invoices were sent as a picture of someone scribbling in a notebook. And that was when I knew like, okay, you can't just apply AI. You've got to really get in there and do it deeply. I won't beat a dead horse here because, you know, one, this link will be in the description, and two, it's more of the same. But reality is they said they had six steps. The reality is they had 14. They have a lot of loops.

It's on you to go in and figure this out. Previously we talked about it in a sales perspective. Now we're talking about it in a finance perspective. So collections, then you reask 70% of the time that happens. Then a partner sending it back later, you see step four here. That happens 35% of the time. To be very clear, if you're an FTE, your job is, yes, to map this out, but also to educate them on what is the cost of this happening?

So if 35% of the time you have to do this a loop, what does that cost? Not just in terms of money, but in terms of time. What is the cycle time of this one person of a team to the next person of the team? That's where a lot of the time goes. In the article that I put out Michael Hammer, who did this study of, you know, digital transformation, said that you might have 20 steps, and if you speed up each step, you might not make the process any faster.

Because it's the cycle time between steps that makes all the difference. That's where the 20 days of time comes out to be. And that's seen time and time again. That was 30 years ago that he said this. So we're seeing the same thing today. Again, same four buckets, sorry. Deletion, plain code, three agents, two human decisions.

It's on you to figure this out. if you want to know how to go about this, it's very simple. Plain code is to be used when it's a simple, if X, then Y, there's no judgment, there's no variance. And if there is variance, it's a switch case, right? If X than Y, if Z, then A, whatever, I ran the letters, you get the idea. On the agent's side, it's where you have enough historical data and judgment is required that you can be pretty concrete about, hey, for example, we have an invoice, a line item shows monitoring. that's very likely going to be office supplies. But there are exceptions.

If it's from a certain vendor, then we know that it's actually not office supplies. It's some other thing. I don't know. You get the idea. And then finally, human decisions, right? If we have to send out payment, there should probably be a human on that.

We don't want an agent to go end to end because then you have phishing scams, right? You have an invoice that comes in. Agent says, this looks legit. We're going to go ahead and pay it. Human in the loop is always super helpful for reviewing and for delivering approval, etc. How should people think about

frontier models versus open source models, Chinese models versus America models? We've just talked about agents as agents, but if you're actually going to deploy these agents, how should people think about

these ecosystems? Super good question. So on one hand, most companies are on co-pilot, Microsoft Go-Pelop. Now, behind them is ClaudeCode, behind that is Codex. And a lot of

what you want should start, especially for an S&B, in skill files, wherever you are. Now, if you're actually building agents, which requires engineering expertise, I'll be honest, and the big labs don't want you to know this, but most of what you're looking to achieve does not need to be

leveraging a frontier mop. There's very few cases where we've seen the need to deploy Fable or Astra. Now that doesn't mean that you're not using their models. We're using, you know, Opus, you know, 4.8 or sonnet, more likely

or, you know, GPT, you know, with lower thinking model, I don't even know what their naming convention is anymore. And also open source. Now, the The other part is if you're talking to Enterprise, and this is some sauce for the viewers, they have an aversion to Chinese models. Even though they're floating point numbers, it's not actually how that works. They don't want to work with models that are out of China.

They can't. There's a strict a version to it. But we have leveraged open source models like, you know, Mews. And sometimes Chinese models where we're allowed to. GROC has been a great model that we've used as well. So don't feel the need to silo yourself into just. chatGBT, Claude, frontier models. You can use their non-frontier models.

You can use GROC, we can use MUSE. You can use GLM, Kimmy, Quinn. All these different things are toolkits. And you should benchmark every single workflow against every single model to determine what model is the right use, is the right model for your use case. We've seen personal agent platforms

start to get big. So we have GrockBot now. We have Muse. Muse actually has Muse for small business. They just announced that instinct, which is more on the consumer side. You've got to think the other big players are going to come into that space too. How are you thinking about using the grok bots of the world to deploy into these enterprises? Are you thinking about it?

Yeah. And then Open AI yesterday with dots, right? I think there's an unlock for that, but it's hard to see governance for those, you know, agents, personal agents, in the enterprise use case, right? I think that is an extension. My philosophy and the philosophy that you follow here at Verrick is that there's two streams of agents for any business or whatever. There's the sidekick agent, which is your co-pilot, you chat with it, it gets stuff done. And instinct and dots and Grockbot and Muse are extensions of that, where you have to chat with it and they get stuff done. The other angle is background agents that truly do work in the background.

They don't bother you. They just do the same thing. And they ping you when they need to. They know what they have to do already. And that's why this deep dive process mapping process engineering is so valuable. Now, I can see them connecting at some point where you can use a muse or a grokbot to set up these background agents that just take work for you all the time.

But that governance isn't there yet. There's still a massive gap in how what involved you have to be and how involved you have to be in from a software engineering perspective. So so far, the use cases to answer your question are limited, and we would rather take work off of their plate rather than make them move faster, because that's the difference in ROI. This gives them 10, 20% faster output.

This gives them 70, 80% faster output with higher accuracy, et cetera. Cool. So, again, selling the idea of an FTE, you need to be three people in one. One, you need to understand how the work actually gets done. Ideally, that means you go off on your own and you really study these systems of record. You study Salesforce, Nets to the dynamics, as we talked about earlier. what do they offer, what do they not? And then, finally, and then part of that is, you know,

how should account a stable function? And you learn that either on your own, in combination with going into a company. The second is shipping production code. You have to be able to do engineering work. Now, it doesn't mean you need to be a undergrad in computer science and, you know, software engineer for 10 years, especially with the advances in AI engineering.

but you do need to be able to ship production code. Agents that call into these different systems of record and do so with auditability, governance, security in place. And then third is the AI layer, right? Knowing what model to use, knowing what you can trust a model with versus what you can't, knowing how to test each model through e-vals and, you know, optimizing your harness. And then finally, you know, how to handle agents taking in correct actions, right? Rollbacks on agents hallucinating, et cetera. If you can do all three of those, you are the best FDE.

You're a very cable edgy. And this is actually very, very rare. Usually you just have one or two of these. Or you're even kind of mediocre at all three. You have to be exceptional at all three, plus the communication of it all, right? Being able to speak to senior leadership and convincing this is a very important. right way to go. And if you are this person, we really need to talk. We want to hire you. And that being said, you'll also have ample opportunity everywhere else. Everyone's looking

for top FTEs. Totally. 100%. Yeah. I mean, I like that you shot you shot there, you know, respect. I think this person is like your NBA player, right? It's, it's, it is, top 0.01%, but if you can figure this out, and the cool thing is you can figure this out in, like you said, like you don't need to have a CS degree, you just have to dedicate yourself to learning the craft. You need experience deploying the craft. And you also need a lot of reps around just all the different ecosystems, open source versus close source, like a lot of the different, I mean, even like the Microsoft ecosystem and the Salesforce ecosystem, you have to understand all these words, bring it together, communicate it in a way that, you know, sells to exact.

So it is hard, but like, that's why these people get paid what they get paid. You know what I mean? and that's why the value is so huge, right? Like the problems that you're solving with deploying FDE's, like, I mean, as we've seen in this episode, like is multi-million dollars of savings and efficiency per year easily. So like someone once gave me advice, well-known, well-known person, well-known founder,

several multi-billion dollar exits. when I was young and he he always said like if you're finding a job the best job to find is the one closest to the money the one that could show that you can optimize that you you know revenue profit because the people that do that are naturally going to get paid the most because they're they're generating as much value for that as we talked about earlier like that factory system So it's like, yeah, you know, you gave $10 million of value to this company. Can I pay you 10% of that, a million dollars? Like, maybe, you know?

Like, that might be a trade. And that's why I think FDEs are so in demand, one, and two, getting paid so well. Yeah. In a heartbeat, you would pay them 10% of what they can deliver for you. And there's even PE firms who are hiring FTEs and giving them a, percent ownership in the carry where you know they bought it for a billion and they hope to sell it for a five billion and you'll get you know point five percent of whatever that delta is based on the work that you're able to do for them um like to your point it's it's the NBA players and like you have to

be good you have to be great you have to be the best of the best yeah so action items if you want to be deeper in the FTE space if you want to kind of get started maybe dip your toes and if you're starting from nothing this is what I would do on a personal level list every single athlete holds your stuff right

I talked about mine earlier today earlier in the presentation which was you know five different inboxes three different texting communication channels etc do this for yourself right which one wins which one disagrees

you know how to route this, kind of do a whole process mapping of your own life. And the next step, take 20 things you did last week, right? You paid a bill, you canceled a subscription. This is a big one.

I'm seeing a lot of use cases on instinct and muse where they go in and cancel their, you know, stuff they forgot about. I got to do that with Adobe, by the way. Adobe for listeners, charging me 40 bucks a month for two years. On Wednesday, you write one process down step by step. You know, how you pay a bill,

end to end or how you submit an invoice if you're a freelancer for work, you know, map this out and try to get as detail as possible. So, you know, there's five different, you know, ways of doing it. There's 10 different exceptions, et cetera. And Thursday sort every single step. Like we talked about the four buckets. What gets deleted. What's deterministic? What's agentic? What do you still need to be there for? And then Friday, figure out who you want to reach out to. And reach out to a bunch of SMBs that you can either get connected with or you can do cold outbound to to do this for them and offer this in a single process start with one workflow make it super simple and do everything for them do the skill files do the uh you know personal assistant instinct

muse grot bot dots whatever it is and build the agents and give them a timeline do it for free if you have to if you're getting started trust me the experience is worth more and your next one you charge that five figures, that six figures. But if you haven't done this ever, get some experience it. And then you go from there. And take what you learn from this, from this presentation. How important is it to know how to deploy hardware with agents at these enterprises? Is that something a lot of people are asking for? Hardware in terms like GPUs? Yeah, like, you know, they have sensitive data. And so they, you know, they want to, you know, they want open source models on, you know, on premise, basically.

Gotcha. Versus, versus cloud agents. Yeah. Truthfully, we've seen zero of that. I know there are companies who do that where they kind of like either rent or sell GPU clusters to very large, maybe heavily regulated companies. We've worked with some of the most heavily regulated companies on the planet, like banks, financial services, health care, pharma and I don't think that they're there yet maybe eventually they might be but not right did you see open AI launched some security features yesterday at dev day or the no I missed it what was

that they launched pull it up so I don't butcher it so they launched private intelligence open AI private intelligence helps businesses use frontier AI with greater confidence that their data is protected. So there's a zero data retention with private safety processing, which enables automated safety reviews. Basically, you don't have to give open AI personnel access to the underlying content. I think a lot of people were kind of like, I want to use some of these models, you know, but I mean, when I say people, I mean businesses.

Businesses are like, I want to use some of these models, but do I really want to give the keys? to open AI, like maybe not. So they end up launching a feature like that. Oh, very cool. Yeah, I mean, we route through like Azure Foundry, you know, AWS Fedrock, Vertex, and they have, you know, again, they've agreed to not train, ZDR, et cetera.

But I can see how this has been doing more and more of an issue for a lot of companies and make sense if they launched it. Cool. And yeah, I like this step-by-step process. This is valuable for, for really not just for a lot of people. Number one, if you want to be an FDE, this is valuable.

Number two, if you want your company to be more AI-native, it's valuable, except Friday stuff. Like, you're not reaching out to people, but Monday, Thursday, you're just like understanding the system and optimizing the system. So it's valuable for a lot of different people. Yeah. And one of the questions we get asked is, like, how do we make our own FDs internally? and I'd have them follow the same playbook, right? So again, this is, if I leave the viewers with nothing else,

this is what it should be, don't apply AI, right? Too many times, and this is the reason why most AI pilots fail, is they try to slap AI on the business, and it's very hand-wavy, and maybe it's rolling out a license with Claude Code to everyone. Maybe it's, you know, building an agent that doesn't really understand the workflow, it doesn't work. When we go into these large companies,

this is the exact process that we follow. And this is why we successfully have transformed departments with AI. We do these steps. We find the real process. We measure the time, baseline all of the KPIs. We pick processes with owners. We sort every single step. We baseline before we build,

and then we build it once and we deploy it everywhere. and we measure it constantly. So we'll go to these CFOs over the course of four weeks do an audit where we understand their systems, we build the POCs in all in four weeks. In the next four weeks,

we build the agents. And then three months after that, six months after that we say, hey, this is what it used to be. This is what it is now. Let me prove it to them. So it's not just, oh, we built AI, we deployed it, we're done.

That's the job of an FD, doing everything end to end. So hopefully that was helpful for all due watching, and I'm wishing everyone best to luck. We'll include the link to this in the show notes in the description so people can access it. Sometimes people ask me, actually they don't even ask me. They go in the comment section and they're like, you're involved in this company. I'm not involved in this company, you know. Like Voss hasn't bought me a beer.

He hasn't sent me money. He hasn't given me a coffee. Nothing. I think that he's just really smart when it comes to this stuff. and I think that if you understand this stuff, you have an unfair advantage, and that's why I bring him on here. I'm on here because he's world-class

when it comes to this stuff. He's not afraid of sharing the sauce, and that's why he's here. I appreciate that. Well, now I feel bad, and I do owe you a beard. But, yeah, I mean, look, if I get one thing out of this,

I need to hire people. So if people can apply, that's one thing that, you know, maybe I can send Greg kickback for, but yeah, you know, I just really appreciate you. have me on. No kickback needed at all. I like what you're doing. And it's funny, you're saying, like, don't apply AI, but apply to my company. So that's hilarious. And yeah, I wish more people,

I hope, if you've made it this far, that you go, no matter if you want to be an FDE or you want to apply this methodology, like go and do it, get your hands dirty. if people want me to go deeper on these topics, please let me know in the comment section. I read every single comment. I respond to most. Voss, you're a legend for coming on, sharing the sauce. I appreciate you. Please come back again. I'll include links where you can follow Voss on the internet and his Apply AI article that I saw and I reached out to him and I was like, hey, you've got to come back on the pod. And I'll see you next time, my friend. Cheers. Thanks so much for having me.