This AI Creates Perfect 60-Second Ads

The End of Manual UGC Production?

Scroll any social feed and you’ll see the same pattern: shaky phone clips, bedroom lighting, a human talking straight to camera. That rough, UGC-style format routinely outperforms glossy brand spots, often driving 2–4x higher click-through rates and dramatically lower CPAs on TikTok and Instagram. Audiences trust what looks like a friend’s recommendation far more than a 30-second TV-perfect anthem.

Brands know this, which is why agencies now crank out endless “fake UGC” for every product launch, variation, and A/B test. Each winning ad spawns dozens of riffs: new hooks, alternate CTAs, fresh angles for different demographics and platforms. Demand scales exponentially, but the production model behind it still looks painfully analog.

Traditional UGC creation remains a bottleneck. Marketers juggle: - Creator outreach and contracts - Product shipping and approvals - Multiple reshoots and edits

A single 60-second UGC ad can cost hundreds of dollars, take 1–3 weeks, and still miss the brief. Multiply that by 50 variants for a serious paid campaign and the math breaks fast.

AI video promised relief, but early tools mostly spat out 3–8 second clips that felt like uncanny-valley GIFs. Models struggled with Character Consistency: haircuts morphing between shots, outfits changing mid-sentence, faces subtly mutating every generation. Stitching those fragments into a believable 60-second narrative for TikTok or YouTube Shorts was basically impossible.

Most workflows also treated each shot as an isolated prompt, so story continuity died at the cut. You could generate a cool single scene, but not a coherent sequence where a creator picks up a product, reacts, and pays off a hook 45 seconds later. For performance marketers who live on watch time and retention curves, that made AI video a non-starter.

Zubair Trabzada’s new n8n-based automation flips that script by chaining specialized tools—NanoBanana Pro, Veo 3.1, FFmpeg, fal.ai—into a single Create pipeline. Upload a product image, write one or two sentences, hit execute, and the system generates intro and continuation clips with the same on-screen persona, then merges them into a clean 60-second file.

Instead of begging creators for last-minute revisions, marketers can Grab a tested Premium Template, Learn How to scale UGC on autopilot, and even Make Money selling done-for-you 60-second Ads. Manual UGC shoots stop being the default, and start looking like the exception.

Meet Your New Autopilot Ad Factory

Forget casting calls, lighting setups, and frantic editing sprints. Here, the entire ad pipeline starts with a dead-simple form: upload a product image, type a one-sentence description, click submit. From that moment, an n8n workflow quietly spins up an assembly line of AI agents that handle everything else on autopilot.

The experience feels closer to ordering DoorDash than producing a commercial. You pick a photo of your makeup brush or gym water bottle, add a prompt like “22-year-old female model talks about this amazing makeup brush,” and walk away. Minutes later, you get a single URL to a finished, social-ready 60-second UGC ad you can download or post directly to TikTok, Instagram, or YouTube Shorts.

The demo makeup brush spot looks eerily like something scraped from the “Get Ready With Me” side of TikTok. A young woman gushes over “how soft these bristles are,” shows off a smooth gradient on her skin, and drops the kind of offhand verdict — “Okay, obsessed now” — that brands pay creators thousands of dollars to say. Her delivery feels casual, unscripted, and continuous, even though the system stitched it from multiple short clips.

The water bottle ad hits the same authentic tone but in a gym context. The model talks through post-workout hydration, shows the bottle “still icy cold even after that workout,” and lands on a clean CTA: “If you’re serious about staying hydrated, you need this in your gym bag.” No jump cuts, no uncanny character resets, just a coherent mini-story that would not look out of place in a paid social campaign.

Behind that simplicity, the workflow chains together NanoBanana Pro, Veo 3.1, FFmpeg, and the fal.ai API. Each Veo 3.1 call generates ~8-second clips — intro, continuation one, continuation two, and so on — all using prompts engineered to maintain character consistency from scene to scene. FFmpeg then merges every segment into one continuous file, smoothing transitions so it plays like a single take instead of a playlist.

Users never see that complexity. They only see a form, a spinner, and a finished ad that feels like it came from a real creator, not a stack of models and code.

The Tech Stack That Makes It Possible

n8n sits at the center of this whole contraption, acting as the orchestrator that keeps every AI service in sync. Trabzada’s workflow uses n8n’s no-code nodes to handle form intake, prompt generation, API calls, and error handling, so the user only ever sees a simple “upload image + description” form. Under the hood, n8n sequences dozens of steps, from image generation to video stitching, without a single manual click. For a deeper look at its capabilities, check out n8n - The Workflow Automation Tool.

NanoBanana Pro handles the first critical step: generating a hyper-realistic base image that locks in Character Consistency. That image becomes the canonical version of the “creator” for the entire ad. n8n feeds NanoBanana Pro your product shot and description, then stores the resulting character image and prompt details so every subsequent clip references the same face, pose, and style.

Veo 3.1 then turns that static image into motion. Trabzada chains multiple Veo 3.1 image-to-video calls: one intro clip plus a series of continuation clips, each roughly 8 seconds long. Want a 60-second ad? The workflow simply duplicates that Veo 3.1 block 7–8 times, with n8n passing along updated dialogue and context while preserving the original character frame.

Supporting all of this, fal.ai provides on-demand, serverless GPU access so the workflow never touches a local graphics card. n8n hits the fal.ai API to run NanoBanana Pro and Veo 3.1 in the cloud, scaling from a single test ad to a full client batch without any infrastructure work. Once all clips render, FFmpeg takes over, automatically merging intro and continuation segments into a single MP4, normalizing audio, and outputting a clean download URL ready for TikTok, Instagram, or YouTube Shorts.

Step 1: Birthing Your Digital Actor

Step one starts with a single image. You upload a product shot into n8n’s form node, and an OpenAI node immediately goes to work, dissecting it for context: object type, materials, brand cues, dominant colors, background style, even camera angle. That analysis becomes structured metadata, not just a blob of alt text.

Instead of asking you to write a perfect prompt, the system spins up its first AI agent: a dedicated prompt engineer. Using the OpenAI output plus your short description (“22-year-old female model talks about this amazing makeup brush”), it expands the idea into a multi-line, hyper-specific brief. It specifies age, gender, lighting, framing, mood, environment, and styling so downstream models know exactly what kind of creator they’re supposed to synthesize.

That engineered prompt then feeds NanoBanana Pro. This model doesn’t just hallucinate a random influencer; it uses both the text prompt and the original product image as a visual anchor. The product’s exact shape, logo position, and color palette get locked into the frame so the bottle, brush, or gadget looks identical in every future shot.

NanoBanana Pro generates a new, photorealistic image of a person actually interacting with the product: holding the water bottle mid-sip, pressing the makeup brush to their cheek, or gesturing toward a skincare tube. Skin texture, hair style, clothing, and even micro-expressions get baked into this first frame. That single still becomes your digital actor’s headshot.

Everything downstream depends on this moment. Veo 3.1 will later generate multiple 8-second clips, but each one uses this NanoBanana Pro image as the visual reference for Character Consistency. If the first image is off—wrong age, wrong vibe, sloppy product placement—every clip inherits those flaws, and your 60-second ad falls apart.

Nail this initial image, and the rest of the workflow behaves like a rigid style bible. The same face, same product, same aesthetic carry through intro, mid-roll, and outro, so your final 60-second ad feels like one continuous shoot, not a collage of unrelated AI shots.

Step 2: Animating Your Character Scene by Scene

Second comes the moment the still image starts to move. A new AI agent spins up and breaks the ad into an intro and a chain of continuation beats, each with its own prompt. Instead of one monolithic “make me a video” request, n8n drives a scene-by-scene script that feels like a human creator planned it.

This prompt agent reads the earlier product analysis and the user’s description, then drafts separate video instructions: how the character should enter, what they say or do, and how the shot should evolve. One prompt might focus on a close-up of a makeup brush against skin; the next might shift to a wider lifestyle shot while preserving the same face, outfit, and lighting cues for Character Consistency.

Once those prompts exist, the workflow starts a tight loop with Veo 3.1. n8n passes the consistent character image plus the intro prompt to Veo 3.1 through the fal.ai API, asking for an 8‑second clip. That length is fixed: you get roughly 8 seconds per request, so a 60‑second ad usually means 1 intro clip plus 6–7 continuation clips.

Each continuation block looks almost identical under the hood. n8n sends the same base character image, a fresh continuation prompt, and any needed style parameters to Veo 3.1, again via fal.ai, and waits for another 8‑second video segment. The result is a stack of short clips that visually match but progress like a narrative instead of a glitchy loop.

The clever part is how modular this loop is. In the n8n canvas, Trabzada groups the nodes for a single continuation into a tidy cluster: generate prompt, send to fal.ai, wait, then capture the returned URL. Want a 40‑second ad instead of 24 seconds? Copy‑paste that continuation group a couple of times and reconnect the wires.

Scaling to 60 seconds or beyond becomes almost mechanical. Each additional continuation group adds another 8‑second slice, so creators can tune length by literally counting node clusters. Agencies can build presets—30, 45, 60, 90 seconds—by shipping templates with the right number of continuation chains already in place.

All of this depends on handling asynchronous AI calls correctly. Veo 3.1 does not return a finished MP4 instantly; fal.ai hands back a job ID and a processing state. If you fire off three clips at once and assume they are ready, the workflow breaks.

n8n’s wait nodes and status checks keep everything orderly. Each Veo 3.1 request triggers a loop that pings fal.ai until the job flips from “processing” to “completed,” with short delays to avoid hammering the API. Only after a valid video URL comes back does the workflow hand that clip off to the next node, guaranteeing every segment exists before the final merge.

The Secret to Flawless Character Consistency

Character Consistency in this workflow hinges on a deceptively simple rule: never change the face. Once NanoBanana Pro generates a single high-res UGC-style image of the model, that exact file becomes the canonical reference. Every Veo 3.1 request — intro, continuation one, continuation two, and beyond — receives the same source image as a visual anchor.

Veo 3.1 still behaves like a generative video wild card, so Zubair leans on prompt engineering as a second constraint layer. The intro clip prompt sets age, vibe, setting, and tone: “22-year-old female model, casual bedroom lighting, talking directly to camera about this makeup brush.” Continuation prompts then reference that baseline explicitly to avoid drift.

Each continuation node in n8n tells Veo 3.1 to “continue from the previous scene” rather than reinvent it. Prompts describe micro-transitions: zooming in on bristles, cutting to a different angle, or shifting from demo to recommendation, while repeating key descriptors like outfit, hairstyle, and camera framing. That repetition turns free-form generation into something closer to a storyboard.

Because Veo 3.1 outputs ~8-second clips, Zubair chains multiple nodes to hit 40–60 seconds without losing the character. Every node pulls: - The same NanoBanana image URL - A running text summary of what just happened - A tightly scoped continuation instruction

That running summary matters. The workflow feeds Veo 3.1 a short recap — “she just applied foundation and commented on softness” — so the next clip logically advances the narrative instead of resetting it. The result feels like a single continuous take, not stitched-together strangers.

This multi-step, prompt-driven system effectively patches current generative video limits, where native multi-shot consistency barely exists. It mirrors how tools like FFmpeg and fal.ai - Serverless GPU Cloud for AI glue together specialized models: constrain inputs, enforce shared references, and let orchestration do the heavy lifting.

Step 3: Assembling the Final Cut

FFmpeg quietly solves the last big problem in this workflow: stitching a pile of 8-second AI clips into a single, watchable ad. Each Veo 3.1 render tops out around 8 seconds, so a 60-second spot usually means at least 7–8 separate files that need to play back-to-back without awkward fades, black frames, or audio pops.

Instead of dragging clips into Premiere or CapCut, the system hands everything off to FFmpeg, the open-source command-line workhorse behind half the internet’s video pipelines. FFmpeg can concatenate MP4s, normalize audio, and re-encode to social-friendly formats using a single command, which makes it perfect for an automation-first setup like this.

Inside n8n, a dedicated workflow segment wakes up only after every intro and continuation clip finishes rendering. Nodes collect the clip URLs coming back from the fal.ai API, verify that each file exists, and write a temporary “concat list” that FFmpeg understands. That list becomes the blueprint for a seamless final cut in exactly the right order.

A single Execute Command–style node then fires the FFmpeg merge. Under the hood, it runs a concat operation that: - Preserves resolution and frame rate from the Veo 3.1 clips - Keeps audio continuous across cuts - Outputs one compressed MP4 ready for TikTok, Instagram, or YouTube Shorts

No editor ever has to touch a timeline. Once FFmpeg finishes, n8n uploads the merged file to storage and surfaces a clean, shareable link. The user gets a single URL that points straight to the finished, downloadable UGC ad—no ZIP files, no manual exports, just one link they can post, send to a client, or drop into an ad manager.

This Isn't Just About Ads Anymore

Ads are just the obvious first stop for this kind of pipeline. Once you can spin up a 60-second, character-consistent UGC clip on command, you can just as easily generate an entire product education library: setup guides, “how to use” series, and side‑by‑side comparisons for every SKU in a catalog of hundreds.

Imagine a Shopify store where every product page auto-populates with: - A 30-second explainer - A 60-second deep-dive - A 15-second “hook” for Shorts and Reels all starring the same AI persona who never misses a shoot, never ages, and never renegotiates the contract.

Sales teams can weaponize the same stack. Feed an outreach tool a prospect’s name, company, and industry, and the workflow can output personalized sales videos at scale: the same trusted face, but with scripts tuned to SaaS, ecommerce, or healthcare. Instead of blasting a PDF deck, an SDR could send 50 custom clips per day without recording a single frame.

Social media turns into a scheduled stream from a synthetic host. A brand can lock in one AI spokesperson and have n8n schedule daily posts where that persona reacts to trends, answers FAQs, or unboxes new products. Character Consistency stops being a technical flex and becomes the foundation for a persistent, cross-platform AI persona that audiences start to recognize.

Agencies get an entirely new product line out of this. Call it Automated Content as a Service: clients pay a flat monthly fee for “unlimited” video variations, capped only by API budgets and agreed guardrails. Instead of quoting per shoot or per edit, agencies sell access to a content engine that can output dozens of ad angles, hooks, and formats per week.

That shift quietly rewires what it means to be a creative shop. The core skill stops being hands-on editing and moves to designing, monitoring, and improving automated systems: picking models, tuning prompts, debugging workflows, and deciding when a human editor steps in. The winners in this next wave won’t just know how to cut a great 60-second spot; they’ll know how to architect a machine that can Create a thousand of them on command.

What Does an AI Ad Factory Cost?

Spinning up an AI ad factory sounds expensive, but the line items stay surprisingly grounded. n8n sits at the center: a solo creator can run this on the free self-hosted tier, while n8n Cloud starts around $20–$50 per month depending on usage and executions. For agencies pushing dozens of workflows per client, higher paid tiers buy more executions, priority resources, and support.

AI brains come next. OpenAI API calls for script generation, scene planning, and prompt refinement usually cost pennies per run. Even with multiple AI agents per workflow, a full 60-second ad typically burns well under $0.10 in language-model fees unless you insist on the largest models for every step.

GPU time is where things get real. fal.ai and similar providers usually bill per generated second or per GPU-minute. With 8-second clips stitched into a 60-second timeline, you might generate 8–10 clips per ad; at a rough $0.02–$0.05 per second of video, that lands in the $1–$5 range for the full spot, depending on resolution and model choice.

Storage and bandwidth add a small tail. Hosting final videos on object storage (S3, Cloudflare R2, etc.) usually runs in the low cents per gigabyte, even at scale. FFmpeg - The Complete Solution to Record, Convert and Stream Audio and Video handles local merging and transcoding, so you avoid paying extra for proprietary editing services.

Stack those pieces and a realistic per-ad cost for a solo operator looks like: - $0.05–$0.10 for OpenAI prompts - $1–$5 for fal.ai GPU time - Fractions of a dollar for storage and bandwidth

Even rounding up, you often stay under $7 per finished 60-second ad, with n8n’s subscription amortized across dozens or hundreds of runs.

Compare that to human UGC creators. On platforms like Billo, Trend, or direct outreach, a single 60-second UGC ad with usage rights regularly lands in the $150–$500 range, and premium creators charge more. Agencies paying for casting, revisions, and management can see effective per-ad costs climb north of $1,000.

ROI goes beyond raw savings. This workflow trades day-long production cycles for near-real-time generation: you can spin 20 variants of a hook, test them in paid social, and keep only the winners. Scalability turns into strategy; you buy the freedom to treat creatives as disposable experiments instead of precious assets.

So this AI ad factory is not free, but it compresses production costs to a fraction of traditional UGC. That gap funds aggressive A/B testing, hyper-specific audience targeting, and always-on marketing experimentation that human-only pipelines simply cannot match.

Build Your Own AI Ad Generator Now

Ready to spin up your own AI ad factory? You can recreate this entire system in an afternoon if you have three things: an n8n account, OpenAI API access, and GPU time on fal.ai.

Start with the basics. Sign up for n8n Cloud using the free tier, create an OpenAI account, and open a fal.ai account. Grab your API keys from each dashboard and store them somewhere you can paste into n8n’s Credentials manager.

From there, you don’t need to rebuild the workflow node by node. Zubair’s video ships with a downloadable n8n Premium Template — a complete blueprint of the automation. Import it directly into n8n using “Import from file,” and you instantly get the entire workflow: form trigger, OpenAI prompt logic, NanoBanana Pro image generation, Veo 3.1 video clips, FFmpeg merge, and final download URL.

To follow the exact build, jump to 1:09 in the video for the Code Tutorial and workflow overview. The description links out to: - The n8n template (“Grab this Premium Template & Learn How to Make Money with AI”) - Free n8n Cloud signup - The AI automation Skool community

Once imported, your main job is wiring credentials. Update nodes that call OpenAI, fal.ai, and any webhooks or forms with your own keys. Run a test: upload a product image, add a one-sentence description, and you’ll get a 60-second UGC ad with full Character Consistency on autopilot.

This stack doesn’t belong only to engineers. No-code means marketers, solo creators, and small agencies can Create their own pipelines, tweak the prompts, adjust clip counts, and design formats that match their brand. You don’t just watch someone else automate UGC — you Sign in, clone the system, and start producing ads, client deliverables, or new products that actually Make Money.

Frequently Asked Questions

What is n8n?

n8n is a powerful workflow automation tool that enables users to connect different applications and services to create complex automated processes without writing code.

Why is character consistency crucial for AI-generated ads?

Character consistency ensures the same person appears across multiple video clips, creating a cohesive and believable narrative. This is essential for building trust and telling a coherent story in advertising.

What are the main AI models used in this workflow?

The workflow primarily uses NanoBanana Pro for creating a high-quality, consistent source image of a character, and Veo 3.1 for generating video clips from that image.

Can this process create videos longer than 60 seconds?

Yes, the workflow is modular. By duplicating the 'continuation video' steps within the n8n canvas, you can extend the final video length by stitching together more clips.