In this episode, a fellow land investor and water/environmental engineer with 13 years of experience, Mitch Klein, walks through one of the most aggressive AI buildouts seen from an operator in the land investing space. Running two businesses, one VA, and six kids, Mitch cut his AI receptionist cost from $250 to $3 per month while collapsing multi-day engineering deliverables into hours. The episode documents what role evolution looks like in practice when a non-technical operator commits fully to building with AI.
Key Takeaways:
- $3 vs. $250: The Receptionist Replacement Mitch replaced PatLive with a custom Retell AI voice agent that runs 24/7, collects caller info, and pipes everything into Pebble via Zapier for just $3 per month.
- 85% of a 16-Hour Engineering Deliverable in 30 Minutes By feeding Claude a design manual PDF and specifying colors, fields, units, and ranges, Mitch produced the bulk of a complex height buoyancy calculation spreadsheet in a single working session.
- The “AI Compounding Effect” Framework Each new AI capability added revealed the next set of use cases that weren’t visible before, enabling Mitch to build 10 core business documents (risk registers, AR processes, etc.) in a single day instead of weeks.
- Dual-AI Quality Control Mitch pits Claude and ChatGPT against each other, having one tear apart the other’s draft, which drives quality up exponentially with no human grinding required.
- From Doing to Managing Mitch’s role shifted from execution to supervising and QCing AI output, with his 13 years of judgment still fully in the loop but his typing, research time, and document drafting fully offloaded.
Tune into the full episode to hear how Mitch built a project management system inside Claude that outperforms what he saw at top-tier engineering firms, and what that means for where your own AI runway starts today.
(Podcast transcript below)
Welcome to Get Serious. We’re at Serious Land Capital. We have funded over $6.25 million worth of vacant land deals with industry-leading 41 % operating margins. So today I wanted to read over the most recent newsletter as I had hosted one of my closer land colleagues, Mitch Klein, for a back and forth with our AI usage across our businesses.
And so wanted to share a lot of the wins from Mitch and also how it can relate to yourself. And of course, us both learning so much from Callan Faulkner and her team at the Uncommon Business and her flagship A2A Accelerate to Automate program that is currently available for a couple more days here in the Spring 2026 cohort. So.
With all of that in mind here, I titled this newsletter with Mitch as his $250 per month receptionist, now costs just $3 and that’s just the small win. So what I’m thinking about, the actual job description for an experienced operator in 2026 and the fellow land investor who showed me exactly what role evolution looks like.
And we had never actually had a real conversation before this one, though he’s been reading this newsletter for a while. Much appreciated there, Mitch. So his name, as I mentioned, is Mitch Klein. He is a water and environmental engineer with 13 years in the field. He’s actually been on Seth Williams’ Ari Tipster pod.
A couple of times in 2025, you can find those episodes number 246 and 248. One was specific to septic and sewer. The other was more about water access for land properties. And Mitch runs his own engineering firm, Solo. And he runs a land investment business with one full-time VA while raising six kids. You can hold that in your mind for a moment.
FYI, just to give Mitch a plug here, if you need a civil engineer’s perspective for larger development projects, site analysis, AI consulting within that space, he has multi-state reach across the country. You can connect with Mitch at xpengineer.com that is spelled X P as in Pam.
E N G R dot com will include that in the show notes as well. and he had joined Kellan Faulkner’s accelerate to automate program, last fall, spring 2025. and again, that program is Kellan’s core 12 week course. it’s really designed to upscale business owners in practical AI applications better than any other offering at least that I’m aware of. you know, we’re,
My team and I are alumni from the Spring 2025 program. So we’re well-versed in what is on offer there. And so roughly six months after starting A2A, Mitch had, or he has already spoken at a civil engineering conference on applying AI inside firms within that industry. Notably, Mitch does not have a tech background, making this jump in capability even more impressive.
And throughout our conversation, I barely talked. I just took notes, occasionally asked clarifying questions and listened to Mitch walk me through one of the most aggressive AI build outs I’ve seen from an operator in our space. The kicker is he hasn’t even started using Claude Cowork, at least by the time we were chatting. More on why this matters in just a minute here.
So in Mitch’s land flipping business, he had built out a custom retail AI receptionist. Retail is a voice agent platform behind a lot of new AI phone systems. He built that out to replace PatLive, which is a human call answering service that many land investors have used over the past several years. So the old cost was $250 a month.
And as I teased earlier, the new cost was just $3 a month. So when Mitch had built up the retail AI service, the system rated itself as a 7.8 out of 10 on its own first build with a 54 point improvement plan to get to a 9.5. And his custom program runs 24 seven, collects caller info, pipes everything into Pebble.
which is the LAN CRM he utilizes all through Zapier. The background noise is selectable for realism, like a coffee shop, a call center, outdoor, et cetera. But the broader move here was simplification. So Mitch went from 10 plus tools, know, RocketPrint, Price, Datatree and others, down to a much cleaner stack. He would utilize LAN portal for market research and data.
Pebble, was mentioned, launch control for text and then his own custom retail build. So Mitch’s own phrase for this surgical implementation and consolidation pattern is the quote, AI compounding effect, where each new capability you add reveals the next set of use cases that weren’t visible before. So for the next topic he went over is Mitch told me about this
height buoyancy calculation spreadsheet that included an instructions tab, a calculator tab, should be obvious if it’s a calculation spreadsheet, a glossary, more ancillary tabs associated with it. Very typical for civil engineer deliverable. But the traditional build for a working engineer takes roughly 16 hours or two to three work days of just
pecking away between client work and having to deal with calls and field work and so forth. So Mitch had fed Claude the design manual PDF as a knowledge base specified the colors, fields, units, ranges and figures and got 85 % of the calculation spreadsheet produced in just 30 minutes.
He then ran a quote, man versus machine experiment on a separate engineering project and was planning to time himself against Claude’s output side by side. But he had to abandon the experiment early because Claude outpaced him so badly during the research phase of the project that there wasn’t a fair comparison left. So his framing of what happened to his role, and this is the part that really stuck with me.
is his job had shifted from a quote, doing to a quote, managing and QCing type role, effectively like supervising a junior engineer. As a reminder, QC equals quality control. And just recall that Mitch is a senior civil engineer with 13 years of reps.
So AI didn’t replace his judgment. It replaced his typing, his research time, and his document drafting. So the judgment, the calibration, the quote, is this actually right, check is still 100 % him. So tech amplifies his expertise, but never replaces his judgment. So that’s the durable framing to keep in mind here. As another example,
When Mitch needed to build core business documents for insurance, for example, a risk approach, the risk registers, the accounts receivable processes, et cetera, he expected weeks of grinding. He built 10 of them in one day with the help of AI. So same with client proposals. The old workflow took days, sometimes weeks.
But the new workflow was that he would record client prospect calls on speakerphone with granola, which is a popular meeting transcript tool. He would drop the transcript into a quad project loaded with previous proposals, a brand guide and his website content. And then he would get back a complete proposal draft, jurisdiction, call action plan and competitive cost analysis in just three to four hours.
and then he would also pit, Claude and Chet GPT against each other for quality control. So one, a, what, what, one AI, tears apart the other draft iterates and quality goes up exponentially with no human grinding required. so you can kind of think of it as if, Claude and Chet GPT are two junior engineers reviewing each other’s work, with Mitch as the senior engineer making the final call. So same management structure.
but radically different time and resource cost compared to running a full human team to review this. One more example to go over here is that Mitch is currently running a complex development project that incorporates entitlements.
Yeah. Purchase agreements, funding, a pro forma document to prep, multiple engineering dis, disciplines that were needed, builder evaluation, et cetera. so he built a weekly, PMO packed in a pack and PMO stands for project management office. so that’s typically a status tracking deliverable inside engineering or consulting firms. and he built all of that within Claude that generates an action register tracking every action ID.
context, the context involved, the next steps, and dependencies. So all of this was designed, in his words, to be, quote, turn your brain off easy so you don’t make mistakes. It also includes HTML dashboards for both investor-facing and internal-facing views, a Gantt chart for visual critical path analysis, a change log with a, quote, AI field map tab.
So the AI can self-reference the spreadsheet structure when updating it and auto-generated weekly summaries flagging which assumptions changed and what next week’s priorities are. So his paraphrased direct take on this was, you know, this change management process is better than what I saw, you know, speaking as Mitch, at top tier engineering firms where Mitch used to work.
So again, let that sink in. A career engineer who’s worked inside the biggest names in the industry just outbuilt one of their internal systems by himself using AI that he learned to wield in a 12-week course. And again, what I teased earlier is that Mitch hadn’t touched cowork yet when we had hopped on a call. So…
everything that I’d walked through Mitch had built using standard cloud projects. It’s the chat-based version, powerful, but not as powerful as Cloud Cowork with all the file management and file editing structures involved with associated knowledge bases and custom instructions. But again, to recall Cloud Cowork, which is something I’ve talked about extensively and will continue to discuss in it every single day, including my team.
you know, we run pretty much our entire business through cowork at this point, like it’s still just a few months old. and as a reminder again, like it connects directly to local files, notion drive, Gmail, calendar, GitHub work, WordPress, stack of other connectors. you know, most critically is the local file connection. but it can also run scheduled tasks and builds full workflows. can call from anywhere, meaning, know, you can utilize your phone to work directly off your,
home workstation remotely. so when I, or while I demoed some of our cowork builds to Mitch during our call, he commented that he could see immediate improvements he could make to his current AI projects and workflows. And since we’ve been in touch, like Mitch said, yeah, all the stuff he shared, you know, he’s already light years past that when, now that he’s implemented cowork.
And critically is that that current Accelerate to Automate, the A2A program is now centered on Claude Cowork, which means that the operators going through this A2A cohort in spring 2026 are positioned to ramp up the AI learning curve faster than ever before. Again, to note, Mitch was not unusually technical. He’s not under occupied. Certainly. Remember, he had six kids, two businesses, one VA.
He decided AI was the leverage point, found the right teacher and put his head down. So as my coach, Trevor McGregor, coach T routinely says, quote, education without application is merely entertainment. So Mitch obviously applied himself and the results speak for themselves. In Mitch’s case, saving three grand a year on a receptionist is real money.
No doubt about that. But the role transformation, the major time efficiency gains, the conference talks, those are the compounding wins. And again, Mitch is still less than a year into his AI journey. So the runway in front of you is the same. Like Mitch, the operators who learn to build with AI now are documenting their judgment, training systems around their expertise, moving faster than teams five times their size.
and creating operating leverage that compounds every month they keep building. So I love how Section AI put it a couple of weekends ago. Section AI is one of my favorite newsletters, really focused on practical business applications of AI. And so their head of AI put it this way, where six months ago, the difference between an AI forward company and a laggard was subtle.
You had a squint to see it. Now you don’t. The gap is showing up in throughput, in the scope of what small teams can take on, in how fast decisions get made. And the companies on the wrong side of that gap aren’t falling behind gradually. They’re watching the lead widen every week. Exactly that.
My team and I are collapsing projects that would have taken a month in the past into a single day. And you’ll hear me talk about even more of these going forward here. Also like Mitch, we wouldn’t be where we are today in regard to AI skills without Callan and her team. And we leveled up the most when we were enrolled in A2A a year ago. So the critical distinction again is that A2A is not quote, how to use AI, but it’s
is showing you how to become the kind of operator who can build AI employees, AI workflows, and AI powered business infrastructure around the knowledge already sitting in your head. So if you’re listening to this now, the cart closes end of May 8th. So you have a couple more days to consider this. Payment plans are available. can find the link to register for Accelerate to Automate.
below in the show notes. Hope to see you on the inside here. And again, if you want any potential land funding, you can find us at serious land dot capital, 50 K minimum purchase price. So with all that in mind, subscribe and share everybody hope you enjoyed this more unique podcast actually going over a case study, bringing somebody else’s perspective in from the AI side, not just myself. So you can see how broadly it applies here. Looking forward to next time.
Take care now, bye.
