Okay, think about the usual hassle of hiring
creators. Yeah. You know, all the contracts,
the back and forth, weeks of waiting. Right,
revisions. And you're spending maybe hundreds,
even thousands, for just one 30 -second ad. An
ad that might totally bomb, by the way. Exactly,
a huge upfront gamble before you even see if
the idea connects. Now, compare that whole headache
to... Getting an almost unlimited supply of professional
looking kind of viral style UGC ads. Generated
automatically by AI. Yeah. Ready to go for maybe
15 cents each. That difference is just, it's
huge. It really flits the script on how marketing
creative gets made. So today we're going to dig
into the blueprint for building exactly that
system. Right. We're unpacking this automated
AI thing. Let's call it a UGC ad generator. And
it uses no code tools like NAN. Google Sheets.
All hooked up to some pretty advanced AI models.
So the plan is, first we'll look at what you
put in and what you get out. Super simple inputs,
surprisingly powerful outputs. Then we'll get
into the interesting part, comparing the three
different AI workflows, like three teams competing
to be the best engine for this. And finally,
we'll talk about how you go from just building
one ad to scaling this whole thing up into like
a 247 content factory. Sounds good. Let's jump
in. So the old way, getting good UGC. It's tough.
Right. And slow. Oh, yeah. Costs can be anywhere
from like 50 bucks to maybe $500 for single video.
And just coordinating everything. Emails, shipping
products, waiting for approvals that can easily
eat up days, sometimes weeks. Plus, there's always
that risk hanging over you. You pour in the time,
the money, and crickets, the ad just doesn't
perform. It's a real logistical nightmare for
something that often has a pretty short shelf
life anyway. Okay, but the beauty of this AI
approach is how little input it actually needs.
It's kind of amazing. Yeah, you basically kick
off the whole process just by filling out one
single row in a spreadsheet, like Google Sheets.
Symbol data in, finish content out. We found
there are basically five key pieces of info you
need. Right. Number one, the URL for the product
photo. Then who's your target audience? Yeah.
Your ICP. Yep. What product features do you want
to talk about? Uh -huh. And where should the
video look like it's taking place? The setting,
a kitchen, a car. And the last one, which is
important for testing, is choosing which AI model
setup you want to use for that specific ad. And
what comes out the other end? It's pretty slick.
You get a fully automated, ready to use, maybe...
eight to ten second ugc style video ad what's
professional yeah surprisingly so it includes
a realistic looking human presenter ai generated
dialogue that sounds pretty natural and critically
it's ready to post immediately already formatted
for tick tock reels you know vertical video you
just get a video link drop it straight into your
ad campaign The real power here, though, it seems,
is the scale and the speed of testing. Exactly.
That's the game changer. You can test like 50
different creative angles at the same time, focus
on different features, different settings. And
see what works almost instantly instead of waiting
weeks per test. Right. It slashes that time to
content. That's the edge. So boiling it down,
what's the main advantage of this scale compared
to just hiring one person? It's automatically
scale testing tons of different creative ideas
all at once. Okay, got it. Let's get into those
three workflows then. This is where you see the
different ways to build this engine. Yeah, and
we should probably define a couple of terms first.
We keep saying NN. Right, so NNN, think of it
like the supervisor on a factory floor. It's
a no -code tool that connects all the different
steps and APIs together in the right sequence.
It tells everything what to do and when. And
the other piece is FAL AI. Ah, yes, FAL AI. That's
basically a service that bundles up a bunch of
different cutting -edge AI models. So instead
of juggling multiple accounts and APIs, Fal gives
us one place to access models like Nanobanana
and Vio, which we used here, makes things simpler.
Okay, so Workflow 1, this is the one you recommend,
the pro version. Yeah, this is the one we landed
on as the most reliable. It's a two -step process,
Nanobanana plus Vio 3 .1. Step 1 uses Nanobanana.
What's that? It's an AI image model. Its job
is to take your product photo and your prompt
and create a new really realistic image of a
person actually holding or using your product
correctly. Okay, so it generates the person with
the product. Then step two. Step two uses VO
3 .1, which is an AI video model. It takes that
image Nano Banana just made and animates it,
adds the talking, the subtle movements. And the
big advantage here is? Accuracy, mainly. The
product usually looks right, held correctly.
And really importantly, this two -step thing
avoids that horrible static thumbnail problem.
Because the first frame isn't just the product
photo. It's the AI -generated person already
moving. Exactly. The video starts with action,
which is way better for grabbing attention on
social feeds. What's the downside? Well, it's
two steps, so it takes a little longer. And it
costs a bit more, came out to around 32 cents
per video in our test. Okay, workflow two then,
the speed demon. Yeah, the one everyone wants
to work, Sora 2 only, just straight. Image to
video. And it's fast and cheap. Super fast and
the cheapest. We clocked it at about 15 cents
for a 10 -second video. But there's always a
catch. Big catch here. Sora 2, at least right
now, has pretty tight content rules. It often
flags and blocks realistic AI -generated human
faces. Oof. That's a non -starter for believable
UGC ads. Pretty much. Plus, you still get that
static product photo as the first frame, that
bad thumbnail issue again. All right. And workflow
three, the middle ground. Kinda. This one uses
VO 3 .1 only, so direct image to video like Sora
2. It's reasonably fast, about 30 cents for an
8 second clip. And does it have the face restriction
problem? Nope, no face restrictions, which is
good. So what's wrong with this one? Oh, this
one had a major flaw. A deal breaker, honestly.
Product alteration. Meaning? It kept changing
the product. We were using this example of a
glass jar of gummy supplements. VO 3 .1 kept
turning the jar into a flexible bag in the video.
Seriously, it just swapped the packaging. Yep.
Consistently. We wasted a bunch of runs trying
to fix it. We even nicknamed it the gummy thief
internally because it kept stealing the jar.
Wow. Why would it do that? Just misinterpret
the image. Our best guess is it over indexes
on context. Like if the prompt talks about grabbing
something quickly on the way out, the AI thinks
quick grab must be a flexible bag and ignores
the fact that the input image was clearly a rigid
jar. That's. Not good for brand consistency.
Not good at all. It completely undermines the
point if the product isn't shown accurately.
Okay, so given that risk with VO 3 .1 alone,
why is that more complex two -step process and
workflow one necessary? To make sure the product
looks right. And crucially, to get that dynamic
first frame with action. Right. Reliability wins
out. Okay. Okay, so workflow one it is. Let's
peek behind the curtain now at how this actually
works in ANN. Sure. So prerequisites, you need
an NA done setup, a Google Sheet ready, your
file AI account, and an OpenAI API key for the
brains. And the workflow starts how? It kicks
off with an ANN trigger. It's basically just
watching that Google Sheet, looking for any new
row you mark as ready. Finds a ready row, then
what? hits a switch node that's just like a traffic
controller it looks at which ai model you chose
in the spreadsheet for that row and sends the
job down the right path for our winning workflow
it sends it to the nano banana path first okay
so node one in that path is the image prompt
agent what's that doing This uses an AI model
like GPT -4 .0 as an agent. We give it a really
detailed system prompt. Think of the system prompts
like the AI's job description and rulebook. You're
telling it exactly how to behave. Exactly. We
tell it, your job is to write a prompt for an
image generation AI. Make the image hyper -realistic.
Think lifelike skin, tiny imperfections, maybe
a selfie angle. And critically, make sure the
product in the image looks exactly like the one
in the photo URL we gave you. So it crafts the
instructions for now. nanobanana, then nanobanana
starts making the image. But that takes time.
Right. AI generation is an instance. So that
brings us to the polling loop. This is super
important. Because you can't just wait indefinitely.
Nope. The workflow uses a wait node to pause
for a bit. Then an alpha node to check the status
from fal .ai. Is the image done yet? If not,
it loops back, waits again, checks again. Keeps
knocking on the door until fal .ai says completed.
Precisely. That loop stops the whole system from
timing out or breaking while it waits. Okay,
image is done. Now, this next part is really
interesting. Node 7, analyze generated image.
You use OpenAI Vision here. Yeah, this is maybe
the cleverest bit. You take the image that NanoBanana
just created, and you feed it back into another
AI, GPT -4O, with vision capabilities. Hold on,
you use AI number two to look at what AI number
one just made? Why? Seems redundant. It's like
quality control. It solves the problem of AI
hallucination. Sometimes the first AI might slightly
mess up or maybe the image isn't quite what you
prompted. Ah, so the vision AI describes what's
actually in the image. Exactly. It looks at the
picture and says, OK, I see a woman with brown
hair sitting in a blue car holding a white jar.
It confirms the visual reality. And that description
is then used for the next step. Yes. That description
becomes a key input for Node -8, the video prompt
agent. Now, the AI writing the video script knows
for sure it needs to write dialogue for a woman
in a blue car holding a white jar, not a red
truck or a green bag. That's smart. It anchors
the video script to the actual image that was
generated, ensuring consistency. Totally. Prevents
weird disconnects between the visuals and the
dialogue. I can imagine getting these prompts
right, especially chained together like this,
must be tricky. You mentioned prompt drift. I
still wrestle with prompt drift myself when managing
API calls, getting the JSON clean and consistent.
Oh yeah, it's a constant thing. Prompt drift.
It's like playing telephone with the AI. You
give it instructions, but by the third or fourth
step in a chain, the AI might kind of start interpreting
things a bit loosely. Forgets the original strict
rules. Yeah, you asked for ultra -realistic,
but maybe it starts leaning a bit more stylized
down the line if you're not careful with how
you pass context. It requires careful prompt
engineering and sometimes explicit reminders
in later prompts. Makes sense. Okay, so Node
8, the video prompt agent, uses the audience
info, product features, and that verified image
description. To generate the final video prompt
for VO 3 .1. This includes writing the eight
seconds of dialogue the person should say, making
it sound spontaneous and natural, matching the
scene. Got it. And the last few steps. Nodes
9 through 12 are basically send the final prompt
to VO 3 .1 to generate the video, run another
polling loop to wait for that to finish. The
waiting. Yep, more waiting. And then the final
step, update the Google Sheet, mark the status
as finished, and paste in the URL of the final
video. Boom, ad generated. So to recap that complex
part. What's the absolutely essential function
of analyzing the generated image mid -workflow?
It guarantees the video script matches the visual
reality of the generated image. Consistency.
Mid -role sponsor, read placeholder. Okay, let's
talk results. The brass tacks. Cost. You said
the winning workflow, Nano Banana plus VO3 .1,
landed around $0 .18 an ad. Yeah, about $1 .18.
And remember, Sort 2 was cheaper at $0 .10. VO3
.1 only was $0 .15. But the comparison isn't
really between $0 .10, $0 .15, and $0 .18, is
it? It's between 18 cents and, what was it, $50
to $500. Exactly. That's the money ball moment,
right? We're talking orders of magnitude cheaper
than traditional methods. Whoa. Okay, just thinking
about that, testing 50 different creative ideas
for less than $10. compared to maybe thousands
for just one human creator test. That's the democratization
aspect. Small teams, even solo founders, can
suddenly test creative at a scale that was previously
only possible for huge agencies. That's a massive
advantage for anyone who jumps on this early.
But cost isn't everything. What about the quality?
Do the 18 -cent ads actually look good? That's
the crucial question. Because, you know, saving
8 cents per ad sounds great. But if the cheaper
ads don't convert because they look bad or have
issues... Then it's false economy. Especially
if you're running thousands of these. Right.
And this is where reliability becomes the deciding
factor. Workflow One, the Nano Banana Plus VO
3 .1 combo, was the clear winner on quality and
reliability. Why specifically? Best natural look,
consistent product accuracy, no gummy thief incidents,
and that vital action -first frame. You pay a
few cents more, but you get an ad that's much
more likely to actually work on social platforms.
So even though Sora 2... Workflow 2 is cheapest.
Yeah, the face blocking and the static first
frame really hurt its potential for genuine -looking
UGC. It's maybe useful for some things, but not
ideal. And VO 3 .1 only, Workflow 3. Dead on
arrival because of the product alteration risk.
Turning a jar into a bag? You just can't have
that. It makes the cost savings totally irrelevant.
So the real benefit of spending that extra, what,
3 to 8 cents on the winning workflow boils down
to? Quality and reliability simply outweigh tiny
cost savings, especially avoiding critical errors
like product changes. Okay, so you've built your
generator. It's making great ads one by one using
Workflow One. How do you scale this up? Go from
a little workshop to a full -blown factory. It
actually starts pretty simply, right? With batch
processing. Yeah, you just tweak that initial
Google Sheet trigger node in NEN. By default,
it's set to only grab the first row it finds
marked ready. You just untick that box. Basically,
yeah. Remove that limit. Now, NEN will grab all
the rows marked ready. So you could line up,
say, 20 different ad ideas in your sheet, different
angles, features, audiences. Hit ready on all
of them, and the workflow will just chew through
them one after another, maybe overnight while
you sleep. That's the factory mode unlocked.
But just making more isn't enough. You want to
make better ads too. Right. Optimization. This
goes back to those system prompts we talked about.
You can create different versions tailored to
specific needs. Like if you have a luxury product,
you tweak the prompt to ask for a premium aesthetic,
maybe soft, elegant lighting. Or for a fitness
gadget, you'd write prompts demanding energetic
movement, dynamic angles, maybe even visible
sweat for realism. You can bake the brand tone
right into the generation instructions. And you
should also... test different messages, not just
visuals, right? Absolutely. Use four rows for
the same product image and setting, but in row
one, focus the script on convenience. Row two,
results. Row three, value. Row four, maybe social
proof. Then you run them all and see which message
actually connects with people. Exactly. Let the
real world data tell you what resonates. Which
brings us to the really advanced move. Closing
the loop. This is where it gets really powerful
integrating performance data back in. Yeah. You
add a webhook node to your workflow. This node
listens for data coming back from your ad platforms
like Facebook ads or TikTok ads manager. Pulling
in actual results, views, clicks, conversions.
You configure the ad platform to send that data
to the webhook. Then you have NAIMN write that
performance data back into new columns in your
original Google Sheet right next to the ad it
belongs to. Okay, so now your spreadsheet shows
not just the ad, but how well it did. And here's
the final piece. You add another AI agent. It's
job. Read the sheet, analyze the performance
data, and figure out what's working best. And
then it automatically creates new ready rows
based on the winners. If the convenience angle
ads got way better click -through rates, this
analysis agent automatically queues up 10 more
variations on the convenience theme. Wow. So
the system starts teaching itself and improving
automatically based on real results. It becomes
a self -optimizing content engine. a true factory
that not only produces but also iterates and
improves based on live market feedback. Okay,
so what's the ultimate goal, the big win from
integrating all that factory mode stuff? Creating
a self -improving loop that automatically tests,
learns, and iterates using real performance data.
Let's just zoom out one last time and grasp the
scale here. Ten traditional UGC ads. You're looking
at, what, $500 minimum, maybe up to $5 ,000?
and weeks of work, coordination, back and forth.
Right, versus 10 AI -generated ads using this
winning workflow, costing maybe $1 .80 total.
Yeah, maybe $1 .80, $2 max, and generated in
minutes, ready to deploy almost instantly. It's
not just cheaper. It's a completely different
economic model for creating marketing assets.
The advantage clearly goes to whoever adopts
this kind of automation and learns to iterate
quickly, like we said, testing 50 ideas for the
cost of maybe one old -school ad. And what about
future -proofing? Are we going to have to redo
this whole thing when SOAR 4 or VO5 comes out?
That's another beautiful part of using tools
like NEN and FAL AI. The core logic, the workflow
structure stays the same. So when a better, faster,
cheaper AI model drops? You literally just go
into your NEN workflow, find the node that calls
the AI model, and update the model name in the
settings. Maybe tweak the prompt slightly if
needed. And your entire factory instantly upgrades
to the next generation of AI content. Exactly.
The system itself is designed to be adaptable.
So for you listening, we really encourage you
to start exploring these ideas. Autonomous workflows,
clever prompt engineering. The barriers to creating
high quality, scalable content are rapidly disappearing.
Really, the main constraint now is just the quality
of the ideas you feed into that initial spreadsheet.
So go build your engine.