Here is a pretty brutal truth about the AI agency
world right now. Most of them are slowly starving
to death. They're just surviving on this diet
of $1 ,500 projects. Exactly. I mean, they build
a cool little one -off chat bot, the client plays
with it for a bit, but then they just get bored,
they drop it two months later, and that whole
exhausting cycle just repeats itself. Welcome
to our deep dive. I am really glad you're joining
us today. If you are listening to this and feeling
that exact agency burnout Just take a breath.
Yeah, we've been there. We are looking at a massive
fundamental shift today We're moving completely
away from selling those small disconnected automation
right because you have to we are talking about
building an actual intelligence foundation An
AI operating system or an AIOS. Which transforms
those exhausting one -off projects into serious
foundational retainers. We're talking $2 ,500
to $6 ,000 a month. That's a huge jump. It is.
So today, we're going to explore exactly why
that old model fails. We'll unpack the three
specific layers of this AIOS. We'll look at how
to actually wire it to real, grieving business
data. And finally, we'll break down the exact
pricing and contracts you need to protect this
model. It really is a completely different ballgame.
But before we can build the solution, we have
to understand why the current agency model is
just breaking down, specifically for small and
medium businesses. Well, the old model just has
this brutal hard ceiling. I mean... Every single
new automation project requires its own custom
setup. It needs its own discovery phase, its
own ongoing maintenance. It's so manual. So as
your client list grows, your profit margins actually
shrink. You're basically just doing more manual
labor to keep the bots running. Exactly. But
here is the really fascinating part. These small
and medium businesses are incredibly ready to
pay. They absolutely are. A 2026 report on AI
spending shows very clear numbers. They're currently
spending $500 to $5 ,000 a month on ready -made
AI tools. Wow. Or they're shelling out $30 ,000
to $100 ,000 on these massive custom builds.
So the money is absolutely there. I mean, the
budget exists. The real question is who they
actually hand that check to. Right. Because the
current scattered solutions out there are causing
two massive pain points for these founders. Yeah.
Pain number one is context loss. Yes. I want
to dig into that. Think about a typical founder.
They sit down to work. and they end up wasting
like 10 minutes every single session, just re
-explaining their business to ChatGPT or Claude.
Every single time. Right. They have to retype
their target audience, they have to explain their
team structure, their specific products. Only
then can the actual real work start. It's kind
of like stacking Lego blocks of data every single
day. That's a great way to put it. But every
morning, someone comes along and just... kicks
them completely over. You have to start from
scratch. That is a perfectly vivid way to picture
it, and that leads right into pain number two,
which is this massive tool sprawl. A typical
small business pays for so many separate, completely
isolated tools. We've got a CRM over here, an
email marketing tool over there. Right, and an
ad platform, an analytics dashboard, plus two
or three different AI subscriptions. None of
these tools. talk to each other automatically.
No, they don't. So the human becomes the API.
The human glue. Exactly. The founder is just
manually copying and pasting data between tabs
all day. In fact, 40 % of these businesses name
integration as their absolute biggest roadblock
to using AI effectively. So does moving to an
operating system model actually eliminate that
daily context loss, or does it just hide it?
It eliminates it by giving the AI, a permanent
readable memory bank. OK, so we know the core
pain is a total lack of permit memory. Let's
look at the exact architecture needed to fix
this. Yeah, so the AIOS architecture has three
distinct layers. Layer one is the context OS.
That is your base. Layer two is the integrations.
Think of that as the plumbing. And layer three
is the workspace. Let's briefly touch on that
workspace layer first. That is basically the
interface. Yeah, mostly. For the builder, like
the agency owner, that usually means using a
coding environment. something like Claude Code
or Cursor. But for the non -technical founder,
they just need a custom chat app, something where
they can ask plain language questions without
learning complex developer tools. Make it as
simple as texting a colleague. Precisely. But
let's really dig into that base layer, the context
OS. I think a lot of people imagine this as some
highly complex abstract cloud architecture. That's
totally. But it's actually incredibly literal.
It is a literal folder on a machine. Just a normal
folder. Yeah. And it contains exactly six specific
Markdown files. And just for anyone who isn't
a developer, Markdown files are simple text files
with no formatting that AI reads easily. Right.
There's no weird PDF styling or hidden code.
It's just. pure text. So what actually goes into
these six core files? The first is offers .dodge
.md. That lists your actual products, your pricing
tiers, and your target audience. The second is
icp .md. That's your ideal customer profile.
I love the idea of using a dedicated file just
for the customer profile, but I read in the sources
that it also includes anti -profiles. Yeah, it
does. Why is that specific part so important?
Oh, it's crucial. An anti -profile tells the
AI exactly who is not a good fit. Right. It prevents
the AI from suggesting marketing campaigns or
sales tactics that attract those nightmare clients
who will just churn in two months anyway. That
makes a lot of sense. You're setting hard boundaries.
What is the third file? Team .md. It lists the
team members, their roles, and their specific
approval authorities. Wait, so the AI actually
knows who has the power to sign off on a discount?
Exactly. It knows the chain of command. Then
you have brand .md. That covers preferred vocabulary,
tone of voice, words the company never uses.
Got it. The fifth file is history .md. And this
one is massive. It logs major business decisions
from the last six to 12 months. Now, why a history
file? That seems like a bit of overkill for a
chat AI. Well, think about it. Have you ever
brainstormed with a brand new employee and they
pitch an idea you already tried eight months
ago that failed miserably? Oh, yeah. All the
time. Right. History .shmd prevents the AI from
doing exactly that. It knows what you've already
tried. That's smart. And finally, you have goals
.md, which outlines the exact revenue targets
for the current quarter. OK, but... writing a
cohesive hyper -accurate business plan across
six text files sounds like a monumental undertaking.
Most founders don't even have this written down
for human employees. Right, which is exactly
why you do not ask them to write it. Oh, OK.
As the agency, you execute this incredibly fast.
You sit down and run a recorded 90 -minute interview
with the founder. It's just a conversation. You're
literally just asking them about their business.
Exactly. You ask them about their offers, their
worst clients, their team dynamics. You take
that raw transcript. plug it into Claude, and
prompt it to generate those six markdown files
using the founder's exact spoken words. Wait,
you don't rewrite it to sound more professional
or polished? Absolutely not. The AI needs the
founder's real, unfiltered language to accurately
match their voice later on. If they say a certain
marketing strategy sucked, you want the words
sucked in that file. That is a brilliantly efficient
use of time. It is, but there is a very strict
rule of thumb you absolutely must follow here.
What's that? keep each of these six files under
500 to 800 words. I mean, I still wrestle with
prompt drift myself. So keeping those files strictly
under 800 words makes total sense. You really
have to keep the AI highly focused or it starts
hallucinating. Exactly. It all comes down to
token limits. Right. And tokens are basically
the AI's short -term memory limit for reading
text. Exactly. Now, Claude code has a massive
context window of 200 ,000 tokens. If your six
files are concise, your whole Context OS only
uses about 5 ,000 tokens. Leaving the vast majority
of the AI's brain power wide open for the actual
daily work. You've got it. So if a founder just
dumps a massive 50 -page business plan into that
folder, what breaks? The AI gets overwhelmed,
ignores details, and reverts back to generic
answers. Okay, so a static folder of text files
is great for background. It knows who the company
is and what it sounds like. But an operating
system needs live breathing data to be truly
useful. If it doesn't know how much money came
in yesterday, it can't really help you today.
Right. And that is exactly where layer two, the
integrations, becomes absolutely critical. This
is the plumbing. OK. You need to connect five
core tools, and you have to do it in a very specific
sequence. Walk us through that sequence. Why
does the order actually matter? Well, step one
is always your revenue. Always. You connect Stripe
or whatever payment processor they happen to
use. You use a restricted API key with strict
read -only access. So the AI can see them. money,
but it absolutely cannot move it. Safety first.
Always. You can't compromise on that. Step two
connects the CRM, HubSpot, PipeDrive, whatever
they already use. That's where the leads live.
Okay. Step three is marketing data. So meta ads,
Google Analytics. Hold on. I want to pause right
there. You mentioned connecting whatever CRM
they already use. So you aren't forcing them
to migrate to some entirely new system. Never.
The whole point of the AIOS is that it layers
perfectly over their existing chaotic stack.
you aren't causing more disruption. Got it. That
lowers the barrier to entry significantly. Okay,
what are steps four and five? Step four connects
daily communication, Slack, Gmail, and step five
connects internal knowledge bases like Notion
or Google Drive. This all sounds incredibly powerful.
But if you have ever run a business, you know
that live dashboards constantly clash with each
other. Oh, it's a nightmare. Stripe might accurately
show 28 new paying customers for the week. Meanwhile,
the MetaAds dashboard proudly claims it got you
40 conversions. Yes, the classic dashboard lie.
Everyone knows it. So if the AI is looking at
both of those sources. Does it just average them
out? Does it take a guess? No, that would be
disastrous. This is exactly why you have to create
one additional absolutely crucial file in your
context OS. OK, what is it called? It's called
datatrust .md. OK, and what exactly does that
do? It acts as a rigid hierarchy. It establishes
absolute ground truth for the AI. You explicitly
write rules telling the AI, for example, Stripe
is the ultimate undeniable truth for revenue.
MetaAds is a secondary source that frequently
overreports, and its numbers always require a
strict cross -check against Stripe. Whoa! I mean,
imagine an AI instantly cross -referencing chaotic
ad dashboards with actual Stripe revenue. and
doing it without opening a single tab. It's beautiful.
So when the AI evaluates a campaign, it sees
Meta claiming 40, but it's hard -coded to verify
with Stripe. Right. It sees 28 and calculates
the real return on investment based solely on
the 28. That completely eliminates hours of manual
spreadsheet reconciliation for the founder. It
saves them so much pain. Why is Stripe specifically
step one in the integration order before leads
or marketing? Because the AI must see real money
first to make accurate business decisions. sponsor.
Welcome back. So we have this foundation successfully
built. The Context OS knows the business deeply.
Live data is actively flowing in through the
integrations. Yep, the pipes are working. But
we don't just hand this massive system over to
the client and say, hey, have fun. No, that is
exactly how you get a confused client who churns
in a month. Right. We have to find the one specific
problem that pays for the entire system immediately.
How do you uncover that specific problem? You
run a clear discovery session with the founder.
You ask four beautifully simple questions to
surface the real pain they're feeling. Give me
an example. First one. What task did you do this
week that you also did last week? Right. Looking
for repetitive manual labor. What else? Which
report do you desperately wish you had every
Monday morning, but you never have the time to
actually build it? That's a good one. Or where
do valuable leads or customers constantly slip
through the cracks? And finally, which administrative
task takes two full hours every single week?
So they give you a list of their daily headaches.
Then what do you do with that? You take their
answers and you score them. You score based on
two metrics, total hour saved and direct revenue
impact. The problem with the highest combined
score becomes your very first build. It's the
quick win. So instead of building random automations,
you build highly targeted solutions. And to do
that, you write very specific prompts into the
system. But these aren't just simple one -liners,
right? Far from it. AIS prompts are essentially
mini briefs. They detail the exact goal, the
strict limitations, the specific data inputs
required, and the exact format the output needs
to take. Let's make this concrete for the listeners.
Give me a real -world example of a prompt that
instantly pays for the system. Okay, let's look
at a weekly ad review prompt. This is incredibly
powerful for e -commerce brands. Okay, walk me
through it. You write a prompt where the AI reads
the offers .dash .md file and the icp .md file.
It pulls the last seven days of meta ads data.
But crucially, it cross -checks that against
the Stripe CSV, the ground truth we established
earlier. Following the exact datatrust .md rules.
Exactly. The prompt asks the AI to find the three
specific ad creatives with the lowest real cost
per lead based only on Stripe revenue. So it
completely ignores the inflated meta metrics.
Right. This specific prompt can expose a hidden
28 % tracking gap between what meta claims and
what actually happened. Wow. It identifies exactly
where precious ad budget is being completely
wasted. I mean, if you're spending 10 grand a
month on ads, exposing a 28 % gap pays for the
agency fee instantly. In week one, it's undeniable.
Let's look at a second prime example, the sales
call prep prompt. Oh, I love this one. It's brilliant.
The prompt tells the AI to prepare the founder
for an upcoming prospect call. It dives into
the CRM, pulls the specific prospect record,
and then it actually searches the connected Gmail
for the last three email threads with that exact
person. So instead of the founder frantically
searching their inbox five minutes before the
Zoom call begins. The AI does it, and it seamlessly
outputs a tight two -line summary of the relationship.
It predicts the prospect's three most likely
objections based on past data. That's incredible.
And it provides the perfect opening question
to start the call. And this just lands in the
founder's inbox exactly one hour before the meeting.
Automatically. They don't even have to ask for
it. And over time, you build five to 10 of these
reusable prompts. OK. They become the actual
product. They become the very thing the founder
touches and relies on every single week. That
small library of tailored solutions is really
what keeps the client happily paying month after
month. Why is exposing that 28 % tracking gap
the ultimate first move for an agency? It delivers
instant financial ROI, making the initial setup
fee an easy yes. We've successfully delivered
that undeniable quick win. Now, how does the
agency actually structure the underlying business
model? Right. How do you totally avoid that $1
,500 burnout cycle we discuss at the very top
of the show? When you need a clear position on
the map, the sources lay out three specific offer
shapes you can choose from. Let's break them
down. The first shape is the training and setup
package. You charge a $5 ,000 initial setup fee
to build the AIOS. Then you offer a short support
window for maybe $1 ,000 to $2 ,000 a month.
But there is a massive risk here, right? Yeah,
a very high risk of the founder just leaving.
After three months, they learn how the system
works, they realize they don't need you to babysit
it, and they just run it themselves. You are
right back to hunting for new clients to survive.
So what is the second shape? It's a highly productized
AIS. You pick one single hyper -specific niche,
say... roofing companies in Texas. You build
a highly targeted version of the context OS just
for them. You charge roughly $10 ,000 for the
setup. That model seems like it works wonderfully
for agencies that want massive, rapid scale.
Absolutely, because your setup time drops to
almost zero after your first five builds. You're
basically just duplicating the exact same architecture.
But there is a third shape, the retainer with
install. This is the absolute most useful option
right now, especially if you're dealing with
non -technical SMB owners. How does it work?
You charge a $5 ,000 setup fee upfront to build
the base layer. Then you charge a monthly retainer
of $2 ,500 to $6 ,000. Now wait, why does this
specific retainer model actually work so perfectly
now in 2026 when it didn't a few years ago? Because
of massive improvements in sheer build speed.
The tooling has evolved so much that one solo
operator can now do the technical work of three
people. You're relying heavily on tools like
Claude Code, Cursor, and ICNOT. Quick pause for
those listening who aren't deeply technical.
What is NN? Is that essentially just a more advanced
version of Zapier? Basically, yes. It's an incredibly
powerful workflow automation tool that lets all
these different apps talk to each other under
the hood. Got it. So the agency handles all that
complex back end wiring. Yeah. But the founder
still needs extreme front end simplicity, right?
Yes. Most business owners have zero desire. to
look at a complex workflow canvas or learn Claude
code. Right. So you use tools like Lovable or
V0 by Vercell. They are platforms that let you
use plain English to rapidly build a custom,
really clean chat interface. Ah, so you essentially
build a custom app for the founder. Right. It
gives them a simple, beautiful interface on their
phone or laptop that is tied directly into their
specific context OS. So they just type a normal
question and your complex backend does all the
heavy lifting. Exactly. But if you're on a monthly
retainer, you have to fiercely protect your profit
margin over time. Otherwise, the client will
just ask for endless tweaks. Scope creep will
utterly destroy your agency if you let it. You
have to use a strictly defined four -line contract.
Only four lines. Keep it simple. Line one, you
guarantee exactly one major automation per month,
defined and signed off in advance. Okay. Line
two, you allow up to two small edits or bug fixes
to existing automations. Line three, you include
one 30 -minute strategy call weekly. And line
four, you provide one monthly ROI report explicitly
proving your financial value. That is beautifully
contained. So anything outside that specific
scope, say they want a massive new marketing
workflow mid -month, that becomes a completely
separate paid project. Exactly. You set that
hard rule perfectly clearly on day one. Won't
clients push back? if you rigidly limit them
to just one major automation a month. No. Writing
the rules clearly on day one actually earns the
founders respect. We have covered a tremendous
amount of ground today. Yeah. Let's slowly zoom
out for the big idea here. The era of selling
disposable $1 ,500 chatbots is officially over.
Business owners do not need more random disconnected
tools. They don't need another tab open. No,
they don't. They desperately need an intelligence
foundation that deeply, permanently understands
their business. You are no longer selling tiny,
isolated automations. You are effectively selling
an entire intelligence foundation. Right. And
here's the secret. When you fully own that context
OS, when you manage the digital memory, you own
the long -term relationship. You successfully
shift from being a completely replaceable freelancer
to an indispensable partner. And as the underlying
AI technology continues to get faster and cheaper,
your profit margins on that retainer will actually
increase. Your manual effort steadily decreases
over time. Stop selling temporary fixes. Start
building their future operating system. I want
to leave you with one final pretty provocative
thought today. Think back to those Lego blocks
of data we mentioned earlier. If you carefully
stack those blocks and you execute this retainer
model perfectly over a year or two, things change.
It naturally leads to the very top of the agency
pyramid. Oh, the equity play. Exactly. Imagine
not just passively collecting a flat monthly
fee from your clients. Imagine actively helping
launch brand new AI first businesses from scratch.
Right. You could strategically trade your deep
context OS expertise for actual company equity.
You could negotiate long term profit share agreements
instead of just retainer fees. That is truly
the ultimate end game for a smart agency owner.
You become a true partner in their growth. So
here is is your clear challenge for this upcoming
week. Don't just passively absorb this deep dive
and move on. Go find one business, and maybe
even your own, struggling heavily with chaotic
tool sprawl. Yeah, find that pain point. Run
a targeted 90 -minute session with that specific
founder, build those six essential markdown files,
and establish their base layer. Take decisive
action. Prove the concept and secure that immediate
financial return. Thank you so much for joining
our deep dive today. Take good care of yourselves
out there. We will catch you on the next one.
Out to your own music.