Mike Futia builds AI tools and workflows for ecommerce brands and agencies, including systems for producing ad creative and creating landing pages through ScaleBot.
Mike Futia is most useful for showing AI production steps for ecommerce ad creative and landing pages.
Best for
DTC brands and agencies needing more ad variations or a practical AI creative process session.
Not for
Brands seeking celebrity-style awareness endorsement, personal testimonials, or guaranteed reach and sales impact.
Key takeaways
They turn one product photo into many image variations for testing.
They analyze an existing ad shot by shot and preserve cuts and audio.
ScaleBot exports editable desktop and mobile landing page frames to Figma for designers.
Check first: Confirm they offer this workshop format and whether posts include measured performance results.
Main ideas
Eliminating Creative Deadlines in Static Ad Production · Automating and Testing High-Performance Landing Pages · Applying AI to Granular Video and Campaign Optimization
Mike Futia builds AI tools and processes for ecommerce advertising, especially through ScaleBot.
Their posts show them applying generative AI to static ads, video production, and landing pages.
The examples include turning one product photo into many image variations, automating parts of an AI-song ad process, and exporting editable landing-page designs to Figma.
Futia’s work connects AI generation to specific production steps rather than stopping at model demos.
In their ad-creative posts, they describe variations for testing different models, formats, and settings; in the Seedance example, they describe analyzing an existing ad shot by shot and preserving its cuts and audio while changing the person.
Their ScaleBot posts extend that process from creative production to landing pages, including adapting a layout to a brand and handing editable desktop and mobile frames to a designer.
This makes their work useful to follow for a view of how ecommerce teams might put emerging AI tools into repeatable creative processes.
Mike Futia applies advanced artificial intelligence and proprietary tools, including Nano Banana Pro and ScaleBot, to address bottlenecks in e-commerce marketing.
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They argue that traditional production methods for ad creative, landing pages, and video are too slow and expensive for modern digital marketing. Their approach uses automated, scalable processes to test variations, clone competitor success, and rapidly generate professional-quality assets while retaining human-verified steps.
Eliminating Creative Deadlines in Static Ad Production
Futia argues that traditional product photography is financially prohibitive and creatively exhausting for digital brands.
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They present automated tools that turn a single product image into dozens of campaign-ready variations, without studio bookings or long waits, for consistent branding and rapid deployment across formats and placements.
Using Nano Banana Pro, users can upload one product photo and generate over 50 different images, including infinite lifestyle shots, instant model swaps for persona testing, pixel-perfect text in any language, and digitally relit assets to prevent creative fatigue across ad campaigns.
Automating and Testing High-Performance Landing Pages
Futia promotes AI tools to bypass the manual work of building or replicating successful web presences.
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They suggest marketers can quickly analyze market leaders by cloning their page structure and adapting it to their own brand.
With ScaleBot, users can enter a competitor's URL to recreate its layout, adapted to their brand's colors, fonts, and copy. They can export the generated landing pages as editable Figma frames for professional designers.
Applying AI to Granular Video and Campaign Optimization
Futia applies AI to complex media creation and optimization, iterating on high-performing assets or building complex creative formats from the ground up through structured processes.
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To refine a winning ad, Futia developed a Claude Code skill that analyzes a conversion video—including shot tables, camera movement, and lighting—and generates prompts to replace the featured person while preserving the original audio and graphical elements. They have also built automated processes for producing AI song ads, from script generation and AI music creation to character design, with each stage reviewable before final rendering.
Start here if you’re facing ad creative burnout or the high cost of traditional product photography.
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See how AI can rapidly scale marketing assets.
Nano Banana Pro turns a single product photograph into over 50 campaign-ready images, bypassing bottlenecks such as studio bookings, photographer availability and long waits. It keeps logos and packaging sharp while generating lifestyle shots from one SKU.
For brands planning a campaign
This helps DTC brands overcome creative fatigue and produce omnichannel assets, reducing the time and cost of creating varied visuals for different ad placements.
Use this to test whether your ad copy resonates with different customer types or to enter international markets quickly and affordably.
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Nano Banana 2 enables virtual photoshoots, letting users swap models to test different consumer personas without hiring talent or scheduling reshoots. It also renders pixel-perfect text directly into images in any language, enabling immediate global scaling.
For brands planning a campaign
Brands can test messaging and target specific demographics with real-world lifestyle variations, speeding up market testing and enabling immediate international deployment of winning ad concepts.
Use this to test market-proven landing page designs without rebuilding them from scratch.
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ScaleBot’s landing page builder recreates a competitor’s layout from its URL, adapting it with the brand’s color palette, fonts, copy and product imagery.
For brands planning a campaign
Brands can analyze and adopt conversion architectures used by market leaders, then quickly iterate on and validate high-performing page structures within a proven framework.
See how to streamline the handoff from AI-generated designs to professional implementation.
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ScaleBot generates landing page designs that export directly to Figma as editable desktop and mobile frames. It preserves every layer, text element and other component, so designers don’t have to recreate the page from a screenshot.
For brands planning a campaign
This tool connects generative AI with professional design workflows, cutting the time and cost of handoffs so marketing teams can move designs from concept to development faster.
See how AI can support granular performance testing of established ad assets.
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Using a Claude Code skill integrated into Seedance 2.5, Mike Futia built a method for replacing faces in existing, high-converting ads. The tool analyzes the original ad—including shot tables, camera movements and lighting—then generates prompts to replace the person while preserving the original audio and scene elements.
For brands planning a campaign
A performance-testing method lets brands quickly compare how different consumer profiles respond to the same ad creative, without the cost and time of a full production reshoot.
Review this if you need an AI platform to manage digital content beyond individual ad creatives.
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ScaleBot uses models like GPT-6 Astra to automate a range of tasks: building on-brand landing pages from a basic prompt, ripping competitor pages, and writing copy based on customer reviews and ad comments. It also offers one-click transfers to Figma and Shopify.
For brands planning a campaign
The system covers multiple stages of digital marketing, from landing-page development to content generation, with tools for rapid, data-informed work.
See how Mike Futia applies automated content generation to e-commerce workflows.
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ScaleBot automates AI song ad production, generating the script, AI-composed original music, character definitions, storyboards and final video clips. E-commerce brands and Meta ad agencies can use it to test creative formats.
For brands planning a campaign
The tool helps agencies produce complex, narrative-driven AI media at scale, reducing manual work and creative risk when testing experimental ad formats.
Build landing pages and hand them off for editing3
Generate many static-ad variations from one product image2
Adapt a winning video ad by changing the featured person1
Automate a multi-step AI song-ad workflow1
Generate many static-ad variations from one product image
Futia presents AI image generation as a way to produce lifestyle scenes, model variations, text-led ads, refreshed versions of existing creative, localized assets, and different aspect ratios from product references.
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They describe named workflows including “Anchor Stack,” “Mannequin Swap,” and “Hook Factory.”
The approach uses repeatable workflows rather than a single image-generation prompt: anchor the product with references, vary the person or scene, add ad text, then adapt or refresh the asset.
For brands planning a campaign
A brand with a usable product-image library could evaluate a creative-variation project for persona, message, and placement testing. The posts describe the workflow and its benefits; ask for measured results and evidence of product accuracy before committing.
Adapt a winning video ad by changing the featured person
Futia describes a Claude Code skill that analyzes a converting ad’s cuts, camera moves, and lighting, then scopes changes to selected shots while retaining elements such as captions, packaging, and original audio.
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They frame it as a way to make versions of an existing winner rather than invent a new concept from scratch.
The workflow identifies what should stay fixed and what should change before rendering, and quotes a cost before generation.
For brands planning a campaign
This is most relevant to a brand or agency with a proven video ad and a specific need for more versions. A pilot could test whether the new-person versions preserve the original ad’s timing and important product details; ask for performance results before scaling.
Futia describes ScaleBot’s AI Video Lab generating a script and shot list, song, characters, storyboard images, and video clips, with a review and approval step between stages.
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The example is positioned for ecommerce brands and Meta ad agencies exploring a song-led format.
The process breaks production into stages and keeps human approval between them, instead of treating a finished video as a single unreviewed output.
For brands planning a campaign
Consider this when testing a deliberately different creative format and when the team can review each production stage. Ask for performance evidence before deciding whether song ads outperform other formats.
Futia describes ScaleBot generating a landing page from a campaign concept or competitor URL, adapting it to saved brand and product context, and allowing AI-assisted edits.
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They also describe exporting editable desktop and mobile frames to Figma, publishing through ScaleBot, or sending a page to Shopify as an unpublished draft.
The examples emphasize connecting ad and page production to brand inputs, followed by editing or a handoff into tools used by the team.
For brands planning a campaign
This may suit a team testing a faster path from campaign concept to landing-page draft, especially where a designer needs an editable handoff. Before committing, ask for a live demonstration and evidence of page quality and conversion lift, and confirm current availability.
Mike Futia writes about using AI tools to produce advertising creative at volume.
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Their recurring subject is the bottleneck between a brand's product assets and the ads, landing pages, and videos it needs: they describe turning one product photo into dozens of static variations, swapping the featured person in a converting video, automating song-ad production, and rebuilding a competitor's landing page around a brand's own colors, fonts, and copy.
Most posts are worked walkthroughs rather than commentary, naming the specific tool, the numbered steps, and what the output looks like; several end with a named playbook or a comment-to-receive offer.
Start with the static-ad posts for the clearest statement of the approach, then read the Claude Code and ScaleBot posts to see how they extend it into video, landing pages, and multi-stage workflows with a human approval step.
The work targets DTC ecommerce brands and the agencies and paid-media teams that serve them, and assumes readers have usable product imagery and an active ad account.
Guilhem tackles the same challenge of producing ad video faster with AI.
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They describe a UGC ad factory assembled from Sora 2, Gemini, and Pletor agents that runs from script to finished video, plus a Claude skill that turns a static ad into motion design while keeping the text intact. Both treat a winning ad as raw material to vary, and both write in short numbered steps that name the model stack.
Guilhem focuses on motion and video production, assembling third-party AI agents into a stack. Futia focuses on static image output and tools built into a product they run, with landing pages and handoffs to design tools also in scope.
Maciel covers the same shift toward AI-assisted ad production, explaining how Google's generative video test inside Demand Gen turns static assets into video and how PMax image tools handle expansion and background generation.
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Both write instructional posts that end with a concrete preparation checklist and argue that asset volume and variety determine what a platform can do for an advertiser.
Maciel works within the ad platform's own controls and account structure, tracking asset grading and experiment setup in Google Ads. Futia builds workflows outside the platform and sells access to them through a membership and product.
Marrano describes chaining AI agents so performance data triggers creative briefs and new ad production, with a human strategist approving the output.
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Both argue that faster feedback loops and higher creative volume matter more than account tinkering, making the case through a described working system rather than a general claim.
Marrano emphasizes data handoffs between agents and the automation loop itself, while Futia focuses on specific generation tools and the step-by-step production of individual assets.
Mike Futia focuses on applying AI and automated workflows to digital commerce brands and marketing agencies.
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Their content targets DTC professionals and entrepreneurs, addressing operational bottlenecks and ways to scale creative output with tools such as ScaleBot, Nano Banana Pro, and GPT-6 Astra. They show brands how to move beyond costly photoshoots and lengthy production cycles by using AI to recycle assets, generate content at scale, and deploy landing pages quickly.
Mike Futia offers AI-driven tools and workflows through the ScaleBot platform to help e-commerce brands and agencies scale creative operations.
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They automate high-volume asset generation, landing page development, and end-to-end AI video ad creation.
AI Creative Asset Generation (Nano Banana Pro)
Buyer requirement
Turn a single product photograph into more than 50 campaign-ready images, with lifestyle variations, instant model swaps for persona testing, and pixel-perfect text rendering. The workflow helps address creative fatigue without traditional photoshoots.
Landing Page Automation (ScaleBot)
Buyer requirement
Build, edit, and deploy responsive landing pages. Start from scratch with brand assets or replicate competitor layouts using your own brand context, then export to Figma or Shopify with one click.
AI Video Ad Generation (ScaleBot AI Video Lab)
Buyer requirement
Create AI-generated video advertisements, from scripts and original AI songs to storyboards and individual clips, then assemble them using advanced coding skills.
Tool Access and Playbook Guides
Buyer requirement
Access the full set of tools, playbooks, and research protocols through the SCALE AI community membership or social media prompts, such as requesting the Nano Banana Pro Guide.
Strong topic fit for a proposed paid workshop, subject to confirming that Mike offers this format. Their posts describe turning product references into lifestyle, model, text, and format variations, and organizing that work into repeatable protocols. They also describe adapting a winning video while specifying what stays fixed and what changes. Those examples match a team looking to expand variations rather than start with an untested concept. The posts make capability claims but provide no measured performance results, so the campaign should sell a creative process demonstration—not promised cost savings or lift.
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Brief
A DTC brand has a product photo library and a proven static ad, but needs more versions for persona and placement tests. It wants a practical session on building and reviewing an AI-assisted creative process.
Sources
When to choose a specialist
Poor fit based on the posts. They focus on AI production processes and ScaleBot, not broad consumer endorsements, personal product testimonials, or campaign performance. A brand seeking endorsement reach should shortlist a creator with verified audience and endorsement information instead. Mike may be more relevant if the campaign shifts to a practical AI creative process demonstration.
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Brief
A consumer brand wants a broad celebrity-style awareness endorsement centered on Mike's personal use of its product, with guaranteed reach or sales impact.
Sources
A worked campaign example
Angle
From one product image to a reviewed set of ad tests
Deliverable and test
Agree in advance on a limited set of draft variations and a human review checklist. The brand can compare them with its existing creative for legibility, product fidelity, and fit with its approved claims, then decide whether to run a controlled test. Don’t promise a particular number of final usable assets or a performance improvement.
Hypothetical brief
A DTC skincare brand has one approved product image and wants to test three customer angles across social placements without commissioning a full new shoot.
Next step
Ask Mike to confirm whether they accept paid workshops or consulting, what the session includes, and whether the brand’s product and claims can be used in the proposed process. Their posts describe tools, guides, and community access, but don’t verify a consulting or workshop offer.
Proposed activation
Invite Mike to lead a paid, hands-on workshop, if they confirm availability and scope. Use the brand’s approved product references to demonstrate a small set of AI-generated scene and model variations, then adapt selected concepts into placement formats. Have the brand team review product details, claims, and text before using any asset. Their post describing Nano Banana processes supports this as a relevant demonstration; it doesn’t establish that every generated asset will preserve product accuracy or perform well.
Consider Raphael if the campaign needs to turn static ads into motion-design variations or assemble AI-assisted UGC video.
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Their posts describe a workflow for animating static ads while preserving text and product details, plus a separate stack for building UGC-style video ads from a product and campaign brief. That makes Raphael's documented work more relevant than Mike's when video production—not a broad set of static-ad variations—is the priority.
The examples focus on video workflows, offering less evidence of how to expand static creative across personas and placements. The posts describe capabilities but provide no measured campaign results.
Consider Nick if the brand needs to prepare assets and run structured tests in Google Ads, especially Demand Gen or Performance Max.
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Their posts lay out asset preparation, suggested creative inputs, and a control-versus-variant testing approach with performance metrics. That is a closer match than Mike's examples when the buyer wants platform-specific advertising guidance alongside creative production.
The examples center on Google Ads features and workflows, rather than a cross-tool process for generating brand-specific creative variations at scale. They describe tactics and expected benefits, not verified results.
Consider Chris if the brand already has advertising data and wants to connect performance signals to creative briefs and production.
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Their post describes a chain in which campaign data triggers a strategist agent, an image-generation step, and human review. That is a stronger match than Mike's examples when reducing handoff delays in an ongoing creative operation is the main goal.
The approach depends on connected data, agents, and human review, making it more involved than demonstrating variations from product references or a winning ad. Ask for measured results from the workflow.
Mike Futia's LinkedIn and Instagram posts cover AI-generated static ad variations, video variation, an AI song-ad workflow, and landing-page build and export features.
The posts do not carry publication dates, so this page describes recurring subjects and current practice rather than a timeline of how the work has changed.
Account profile figures are from October 2026.
The posts contain no campaign performance results, no independent testing of the tools described, and no verified audience demographics, so this page makes no claims on those points.
The posts also do not mention books, shows, or earlier career history, and they do not mention external brand partnerships or sponsorship offers; this page is explicit about those gaps rather than filling them.
For comparison, two LinkedIn posts each by Raphael Guilhem, Nick Maciel, and Chris Marrano, dated November 2025 through August 2026, cover shared subjects and differences in approach.
The comparison does not use those posts to make claims about Futia.
