How should a creative team use AI for creative strategy without losing the original ideas that make ads work?

Use AI to compress the non-creative work that surrounds ideation, not the ideation itself. A creative strategist typically spends 40 to 60 percent of the day on coordination, recaps, and admin; cutting that to 20 or 30 percent buys back the hours where original ideas actually come from. Feed the model a living brand context document so it can draft, expand, and fact-check briefs and scripts, but keep the originating idea human, because an LLM predicts from rules and past patterns and will always regress toward what has already worked.

In this episode, Will Sartorius and Matthew Gattozzi of Goodo Studios break down how to use AI for creative strategy without losing the original ideas that make ads work. Matthew explains why a strategist should hand AI the coordination and admin instead of the ideation, how to build a brand context document that actually improves your outputs, and why he cancelled his ad analytics software and built his own reporting tool instead.

We discuss:

• Where AI delivers real ROI in a business built on physical production
• The "Matthew tokens" model for deciding what you personally should spend time on
• How to cut non-creative work from 60% of a strategist's day down to 20%
• What belongs in a brand context document, and how to get AI to build it with you
• How to sync context documents across a team so they stay living, not stale
• Why the best ideas come from being out in the world, not behind a laptop
• When a creative strategist should break the rules their own context doc sets
• Why he replaced his analytics SaaS with a custom tool, and what it actually tracks
• How to find net-new angles that will never show up in your account data

Join the AI DTC WTF newsletter: https://www.ai-dtc-wtf.com/aidtcwtf-newsletter This episode is brought to you by Runneth from Motion: https://runneth.com Learn more about Matthew Gattozzi Goodo Studios: https://www.goodostudios.com/ LinkedIn: https://www.linkedin.com/in/matthewgattozzi/ Site: https://matthewgattozzi.com/ To connect with Andrew Foxwell reach him here [email protected] To connect with Will Sartorious DM him here https://x.com/will_sartorius To Connect with Thomas Moen DM him here https://x.com/thomasmoen To learn more about Foxwell Founders and conversations like this one, go here: www.foxwellfounders.com Learn more about Foxwell Digital: https://www.foxwelldigital.com All episodes: https://www.ai-dtc-wtf.com AI DTC WTF is the podcast for people who work in DTC, use AI, and want to know what is actually working right now. Every week: tactical tips, strategies, and ideas you can implement in your AI workflows to make your brand or agency more money.

Chapters 00:00 Why AI can't break creative rules 01:12 How Goodo Studios combines creative strategy and production 03:20 Where AI fits into creative strategy, and where it doesn't 05:02 The "Matthew tokens" model for protecting your best thinking 07:04 Cutting non-creative work from 60% of a strategist's day to 20% 09:04 Why the best ad ideas come from living, not prompting 11:07 How to build a brand context document that improves AI output 13:09 Using AI to draft, expand, and fact-check ad scripts 17:50 How to share and sync AI context docs across a team 20:16 Let AI interview you to build your creative filters 22:30 What taste actually is and how you develop it 27:23 Separate context docs for statics, UGC, and studio video 28:25 When a creative strategist should break the rules 31:40 Why he built his own creative analytics tool instead of paying for SaaS 35:29 Tour of the custom Meta creative reporting dashboard 38:34 How to find net-new angles the data will never show you 43:41 Why AI is retroactive and can't generate new ideas 45:43 Building a hooks library from saved ads 47:43 Getting your team to actually use the AI tools you build 49:46 Parting advice: finding harmony between AI and creativity

In this episode, Andrew Foxwell and Will Sartorius talk with Harry Delmege about using AI for ad creative — specifically where it helps and where it quietly destroys your hit rate. Harry co-founded Roly Poly Digital and is best known for long-form "yapper" ads that have scaled past $1M in spend. His team uses AI heavily for execution and almost never for ideation. You'll learn why manual research still beats AI research, why AI UGC fatigues faster than real UGC, and the one quality Harry now hires creative strategists for.

We discuss:

• Where AI belongs in a creative process and where it doesn't
• The Claude skills Roly Poly built for yapper ads, VSLs, and native long-form
• Why AI can't find the customer story your ad actually needs
• Where AI creative genuinely wins: claymation, Pixar-style spots, and b-roll you could never shoot
• Why AI UGC dies faster than real UGC, even when it works
• What a $200K/month ad account actually looks like in 2026
• Why brand owners are firing agencies over "AI slop"
• How to run a manual research bounty that keeps your team's skills sharp
• Why human QA is what takes an ad from a six out of ten to a nine

Subscribe to AI DTC WTF: https://www.ai-dtc-wtf.com/ Listen on Spotify: https://open.spotify.com/show/033haXJDf9RTIAJzUQY4yC This episode is sponsored by Proppel, the hiring agency that has placed more than 75 employees for Foxwell Founders members. Get 10% off your first hire when you mention the AI DTC WTF Podcast: https://www.weareproppel.com/foxwell-... This episode is brought to you by Runneth from Motion: https://runneth.com To connect with Harry Delmege DM him here https://x.com/harrydelmege_ To connect with Andrew Foxwell reach him here [email protected] To connect with Will Sartorious DM him here https://x.com/will_sartorius To Connect with Thomas Moen DM him here https://x.com/thomasmoen To learn more about Foxwell Founders and conversations like this one, go here: www.foxwellfounders.com Learn more about Foxwell Digital: https://www.foxwelldigital.com All episodes: https://www.ai-dtc-wtf.com

AI DTC WTF is a short, tactical podcast from Foxwell Digital about using AI to get an edge in DTC. Hosted by Andrew Foxwell, Will Sartorius, and Thomas Moen.

Chapters:

00:00 Why AI isn't making better ads yet 01:27 Where Harry Delmege uses AI and where he refuses to 03:02 The Claude skills behind yapper, VSL, and native ads 05:03 Why manually written ads still beat AI first drafts 06:51 Why AI can't mine Reddit for real customer stories 09:07 Where AI creative actually wins, and why AI UGC dies faster 12:28 What a $200K/month ad account looks like now 16:56 Why ad volume is not the game 22:16 The pendulum swinging back to direct response fundamentals 24:40 AI psychosis and the 80/20 split inside an AI-enabled agency 26:20 The one quality to hire a creative strategist for 30:43 What the best AI users will be doing a year from now 32:58 It's not the format that's crushing, it's the story 37:14 Why human QA takes an ad from a 6 to a 9 40:05 How to stop your team from getting AI-dependent 45:27 Why big brands can't get AI animation approved

How do you build an AI agent team for a marketing agency? Tristram Dyer, CEO of Webtopia & OAKS, walks through the exact system his agency runs: fast AI that speeds humans up with MCP data connections and AI creative tools, and slow AI, a team of scheduled background agents that detect performance anomalies, pace budgets, write morning briefings, and operate Meta ads with a human approving every change.

We discuss:

• Fast AI vs. slow AI: the Thinking, Fast and Slow framework for agency work
• The creative workflow connecting Claude, Arcads, Claude Design, and HeyGen
• Why every Webtopia employee gets a paid Claude account
• The full agent roster: anomaly detector, agent 00 morning briefings, the Meta ads operator, and Mia, the Google Ads specialist
• Will AI replace ad agencies, and how to incentivize a team to adopt AI
• Why agentic ad buying may be only three months away

Watch the previous episode with Cody Plofker: https://www.ai-dtc-wtf.com/podcast/why-ai-ugc-is-a-trap-and-what-actually-works-cody-plofker

This episode is brought to you by Runneth from Motion: https://runneth.com Learn more about Tris and Webtopia: https://www.webtopia.co

To connect with Andrew Foxwell reach him here [email protected]

To connect with Will Sartorious DM him here https://x.com/will_sartorius

To Connect with Thomas Moen DM him here https://x.com/thomasmoen

To learn more about Foxwell Founders and conversations like this one, go here: www.foxwellfounders.com

Learn more about Foxwell Digital: https://www.foxwelldigital.com All episodes: https://www.ai-dtc-wtf.com

AI DTC WTF is the super practical podcast about leveraging AI to get an edge in DTC, hosted by Andrew Foxwell, Will Sartorius, and Thomas Moen.

Chapters

00:00 How to build an AI agent team for your marketing agency

01:22 Fast AI vs. slow AI: the Kahneman framework

02:23 The fast AI stack: Meta, Kaizen, and Motion MCPs

06:26 Kaizen: adding qualitative client data to the numbers

08:27 Why every employee gets a paid Claude account

09:28 Creative workflow: Claude → Arcads → Claude Design → HeyGen

11:29 Tangible results: faster decisions, happier clients

13:31 The roadmap: putting agents in founders' hands

16:33 The agent team: BigQuery warehouse and anomaly detection

18:34 Morning briefings and a self-improving agent

19:35 The Meta ads operator with human-in-the-loop

22:40 Will AI replace ad agencies?

25:46 The Cody Plofker landing page example

27:48 How to incentivize employees to use AI

30:51 Meet Mia: the Google Ads specialist agent

Andrew Foxwell and Thomas Mowen run through the biggest AI updates hitting Meta, Shopify, and YouTube. On the Meta side, the platform is going all in on catalogs: product images can now be auto-converted into video ads, in-campaign AI editing will crop, animate, swap backgrounds, and generate text variations on your creative automatically, and Gen AI tool adoption has jumped from 1 million to 4 million advertisers in six months. Meta also rebuilt its opportunity score, rolled out a "brand memory" feature that learns your tone, look, and fonts from your ad library, added client creative sign-off inside Ads Manager, and is expanding automatic AI disclosures. The Meta Ads AI connector (MCP) now has 29 tools for reporting, campaign builds, catalogs, pixel diagnostics, audits, and benchmarking. Plus a whole bunch more that the guys get into.

Shopify announced that AI searches powered by its catalog convert at 2x scraped data, and introduced agentic storefronts, a setting that syndicates structured product data to AI models for better discoverability across SEO, AEO, and GEO. Sidekick got major upgrades including multi-step tasks with store-wide context. Norwegian clients are already seeing meaningful agentic traffic in their analytics.

On YouTube, Shorts remain underpriced for the incrementality they drive. Demand Gen now connects product feeds with live pricing from Google Merchant Center, shoppable feeds in Shorts let you tag up to 60 products per video with in-app checkout rolling out, and Google claims Shorts shopping converts 3 to 5x better than traditional pre-rolls.

The TL,DR: AI is handling production plumbing while platforms pull discovery and checkout inside their walls. The winning recipe is clean data (especially your catalog), the right creative volume, and knowing which levers to pull.

Key Takeaways

• Is your product catalog quietly costing you conversions in the AI shopping era?
• What happens when Meta starts cropping, animating, and rewriting your ads automatically?
• Could Meta's rebuilt opportunity score already be affecting your auction performance?
• Can Meta's new brand memory finally fix "off-brand" AI creative?
• Are Meta Business Agents about to become a legitimate sales channel inside your DMs?
• Why do Shopify catalog-powered AI searches convert at double the rate of scraped data?
• Would you recognize agentic traffic in your analytics if it showed up today?
• Are YouTube Shorts the most underpriced placement left in your media mix?
• What would tagging 60 shoppable products per Short do to your revenue?
• Should your winning Meta ads be running as YouTube Shorts right now?
• Is your AI disclosure strategy ready before Meta applies one for you?

Chapters:

• 00:00:00 — Intro
• 00:01:09 — Fable is back: workflows, token-saving tips, and an 11-bug code review story
• 00:04:33 — Sponsor: Runneth by Motion
• 00:06:58 — Meta updates: catalogs, AI editing, opportunity score, brand memory
• 00:11:44 — AI disclosures and the Meta Ads AI connector (MCP)
• 00:13:45 — Meta Business Agents: AI selling in your DMs
• 00:15:10 — Shopify: agentic storefronts, Sidekick upgrades, analytics
• 00:20:20 — YouTube Shorts: Demand Gen, shoppable feeds, in-app checkout
• 00:23:25 — The go-forward recipe + wrap-up

This Episode is sponsored by Motion's very own Runneth. Runneth is the AI Tool that is quickly becoming the go to decision maker brands turn to when it comes to creative decisions within their ad accounts. From Creative stratigest to hook insporations and creative briefs, runneth can do it all. See the case studies and try runneth yourself https://runneth.motionapp.com/

To connect with Andrew Foxwell reach him here [email protected]

To connect with Will Sartorious DM him here https://x.com/will_sartorius

To Connect mith Thomas Moen DM him here https://x.com/thomasmoen

To learn more about Foxwell Founders and conversations like this one, go here: www.foxwellfounders.com

This bonus episode covers the abrupt shutdown of Claude Fable 5 (alongside Mythos 5), Anthropic's most powerful model, which lasted only three days before the U.S. government forced it offline. Host Andrew is joined by Thomas Moen from Norway, and the two unpack what happened, why it matters, and what D2C operators and agencies should do about it. The framing is blunt: if you wired anything into Fable 5 during its brief window, this episode is your fire drill.

The hosts walk through the timeline. On June 12 at 5:21 PM Eastern, Anthropic received an export control directive from the U.S. government ordering it to suspend access to Fable 5 and Mythos 5 for any foreign national inside or outside the country, including Anthropic's own foreign-national employees. Because the company could not cleanly segment access by nationality, the practical result was a full shutoff for every customer. The stated trigger was a national security concern around a jailbreak that let the model read a codebase and spot software flaws. The hosts stress that no one was hacked and no data was breached. Anthropic complied but publicly disagreed, calling it a misunderstanding, and the situation was likened to a car part being recalled mid-deployment.

From there the conversation turns critical (without getting too political). Andrew, who previously worked as a press secretary in Congress, argues that government simply moves too slowly to regulate AI at its current pace, and that pulling a single model is more PR theater than a real solution. Thomas notes the chilling effect: people he knows are now experimenting with DeepSeek and looking harder at European model options, newly aware of how unstable reliance on U.S. models can feel. They also flag the irony that Anthropic's own transparency, admitting no model is perfectly jailbreak-proof, may have handed regulators their rationale. Both agree this sets a dangerous precedent: a government can now take down a frontier model after launch.

The back half is tactical. The core lesson is that single-model dependency is now a real operational risk, on par with relying on one 3PL or one ad platform. Agencies feel it harder than brands because of scale, with one analogy of 70 clients meaning 70 fires to put out. The hosts recommend auditing all AI dependencies (prompts, workflows, MCP configs, Zaps), assigning an internal owner for AI governance, building and actually testing a fallback ladder so models are not hard-coded, keeping a manual or "caveman mode" playbook, maintaining a locked manual database in parallel with AI-driven ones, and understanding the underlying logic of your skills and SOPs rather than trusting bloated AI-written ones. Thomas advocates for open-source systems and local models as insurance. The closing takeaway is that AI is shifting from a playground into pseudo-regulated infrastructure, so contingency planning is no longer optional. To connect with Andrew Foxwell reach him here [email protected]

To connect with Will Sartorious DM him here https://x.com/will_sartorius

To Connect mith Thomas Moen DM him here https://x.com/thomasmoen

To learn more about Foxwell Founders and conversations like this one, go here: www.foxwellfounders.com

This is a solo episode of the AI D2C WTF podcast a departure from the usual deep-dive guest interviews. Instead host, Thomas Moen goes practical and breaks down two dead-simple AI "skills" that he personally uses 10 to 15 times a day. A skill, as he explains, is just a markdown text file that Claude (or another AI agent) reads to follow a standard operating procedure, and the AI automatically knows when to invoke it.

The first skill is Grill Me, originally created by Matt Pocock and published on GitHub. It is only three sentences long and instructs the AI to interview you relentlessly about every aspect of a plan, one question at a time, until you reach a shared understanding, offering its recommended answer for each question. Though it was built for coding and development, the host has evolved it into a business and life tool. He shares how he and his business partner each ran Grill Me when deciding whether to hire a new head of agency. He surfaced 36 questions, his partner 17, and the exercise produced two markdown documents they compared to find the right path. He notes it works for anything from fixing weekly reports to deciding between a tent trip and a glamping trip.

The second skill is Handoff, also from Matt Pocock's library. It solves the context window problem. The host references the idea of a "smart zone" and a "dumb zone," where after roughly 200,000 to 300,000 tokens the AI starts to get confused and less reliable. When he feels a conversation slipping into the dumb zone, he invokes Handoff, which captures everything relevant from the current chat into a markdown file plus a short prompt that lets a fresh AI agent pick up exactly where the last one left off. The skill is about five sentences and includes instructions to save to a temp directory, reference artifacts by path rather than duplicating them, and redact sensitive information like API keys and passwords.

Websites and SaaS Products Mentioned

• Claude (Anthropic) — the AI assistant the skills run on
• Motion (motionapp.com) — creative analytics platform and episode sponsor
• Slack — referenced as a place to receive an AI morning performance brief
• Matt Pocock's skill library — the source for both the Grill Me and Handoff skills (shared via GitHub)

To connect with Andrew Foxwell reach him here [email protected]

To connect with Will Sartorious DM him here https://x.com/will_sartorius

To Connect mith Thomas Moen DM him here https://x.com/thomasmoen

To learn more about Foxwell Founders and conversations like this one, go here: www.foxwellfounders.com

Frederick Rode, a German AI consultant transitioning from creative strategy to full-scale agency AI implementation, joins Andrew and Thomas for a detailed breakdown of the systems he's actually running. The episode opens with his Claude research agent, a no-code Claude skill wired to Foreplay (for competitor ad library scraping) and Firecrawl (for web scraping) that outputs a 10-page brand document for agency teams to review first thing when they arrive to the office. From there the conversation covers automated ad performance reporting, and the "easier version" of that same pipeline that listners can implement right away. Thomas then shares that he currently has six Mac Minis running AI media buying for 10 of his clients (still monitored by a team but working). Freddie rounds out the episode by walking through a multi-agent CRO landing page system he built in one afternoon with Claude Code, and why splitting one big agent into three specialized ones (copy, layout, CRO) produces dramatically better output. The episode closes with a candid conversation about AI adoption: agencies are excited, not scared, and the ones moving fastest are freeing employees from execution work so they can focus on analysis and strategy. Key Takeaways:

• How connecting to Foreplay's MCP server via Claude is changing the way brands can analyze their competitors' ads
• Why Freddie skips local file storage and auto-upload all research directly to Supabase.
• The "easy version" of automated ad performance reporting, and how listeners can build this without needing direct Meta API access.
• How Railway is turning a weekly Slack performance report into a $5/month automated system for agencies
• The ClickUp status trigger that automatically checks ad creative for grammar errors before they ever go live
• How to build a multi-agent CRO landing page system in an afternoon with Claude Code, and why three specialized agents outperform a single all-in-one agent every time

Products & Software Mentioned

• Claude (Anthropic) — https://claude.ai (core model; used for skills, research agent, all downstream workflows)
• Claude Code — https://claude.ai/code (used to build CRO landing page agent and grammar review automation)
• Foreplay — https://foreplay.co (save and scrape Facebook ad library; one-click MCP server connection to Claude; competitor ad transcripts, video downloads, run duration)
• Firecrawl — https://www.firecrawl.dev (web scraping via MCP; extracts brand pages, Trustpilot reviews, competitor sites)
• Gemini (Google) — https://gemini.google.com (visual analysis of competitor ad videos via MCP; analyzes hooks, angles, text overlay, first 3 seconds)
• Supabase — https://supabase.com (database for storing personas, ad performance data, copies, headlines; official Claude MCP integration)
• Meta API — https://developers.facebook.com/docs/marketing-apis/ (official ad account data: spend, hook rate, ad-level performance)
• Apify — https://apify.com (ad scraping tool with official Meta ad scraper; receives post links from 2-Minute Reports and downloads asset URLs, videos, images)
• 2-Minute Reports — https://www.2minutereports.com (Google Sheets-based ad reporting, similar to Supermetrics; scheduled auto-reports; no Meta API setup required)
• Frame.io (Frame) — https://frame.io (creative review platform; API used to automate grammar/copy QC on ads the moment they hit "ready for review" in ClickUp)
• ClickUp — https://clickup.com (project management; task status change to "ready for review" triggers Frame API automation)
• Railway — https://railway.app (~$5/month; connects GitHub + runs scheduled script to deliver Slack performance reports automatically each week)
• North Beam — https://www.northbeam.io (MTA attribution tool; discussed as a potential data cross-reference source alongside Meta reporting; has an API)
• Motion / Runneth — https://motionapp.com (sponsor; AI agent inside Motion's creative analytics platform, powered by Claude, trained on $14B in ad spend)
• Codex (OpenAI) — https://platform.openai.com/codex (coding AI; mentioned as an alternative Freddie is exploring alongside Claude)

To connect with Freddy DM him here https://x.com/freddyrode3

To connect with Andrew Foxwell reach him here [email protected]

To connect with Will Sartorious DM him here https://x.com/will_sartorius

To Connect mith Thomas Moen DM him here https://x.com/thomasmoen

To learn more about Foxwell Founders and conversations like this one, go here: www.foxwellfounders.com

Florian Litterst, German paid social agency owner since 2012, joins the show to break down exactly how his team has wired AI into every corner of their operation. The conversation gets specific fast, from a Claude-powered agent that scrapes competitor ads from Meta's EU ad library and surfaces untested angles every 30 days, to a another that mines Trustpilot, TrustShops, and Amazon competitor reviews for creative ammunition. Florian walks through his image generation workflow using krea.ai, why he now runs AI-ready photo shoots so every product can be "prompted into any situation," and how AI-generated B-roll is solving the impossible shot problem (think: socks on a runner's feet filmed from behind). The episode closes with a forward-looking debate on where AI is taking agencies from execution focused shops toward data-connected consultancies and a unanimous take that experienced human taste is the one thing AI still can't replace. Key Takeaways

• What a real competitive intelligence AI agent looks like and how you can build one using Meta's ad library, Airtable, APFI, and Claude.
• Why pulling competitor negative reviews from these places are one of the most underrated sources for ad angle discovery.
• How the EU's ad transparency rule actually gives European agencies a data advantage over US-based media buyers.
• Why investing in "AI-ready photo shoot" is cheaper than repeatedly shooting new product content everytime
• When AI-generated video becomes a liability instead of an asset
• How this one site is being used to create scalable image and video workflows.
• Why creative iterations are losing their value in Meta's algorithm, and what agencies should be doing with their production time instead.

Products & Software Mentioned

• ChatGPT (Deep Research) — https://chat.openai.com
• Claude (Anthropic) — https://claude.ai
• Airtable — https://airtable.com
• APFI — API wrapper/scraping tool (referenced as tool for pulling ad library and review data via API; search "APFI API tool")
• OpenAI Whisper — https://openai.com/research/whisper (used for video transcription)
• krea.ai — https://www.krea.ai (image and video generation platform; wrapper for Nano Banana, Sea Dance/C-Dance, and other models)
• Nano Banana (Google) — Google's image generation model, accessible via krea.ai and Google Flow
• Google Drive — https://drive.google.com
• Slack — https://slack.com
• Trustpilot — https://www.trustpilot.com
• TrustShops — https://www.trustedshops.com
• Amazon (reviews) — https://www.amazon.com
• Supabase — https://supabase.com (mentioned as alternative to Airtable, not used by Florian)
• Netlify — https://netlify.com (briefly mentioned in context of landing page creation)

To learn more about Florian and his team at Ads Venture go here: adsventure.de

To connect with Andrew Foxwell reach him here [email protected] To connect with Will Sartorious DM him here https://x.com/will_sartorius To Connect mith Thomas Moen DM him here https://x.com/thomasmoen To learn more about Foxwell Founders and conversations like this one, go here: www.foxwellfounders.com

Co-host Will Sartorius chats with Andrew on a wide-ranging episode that opens with a hot-take roundup on the latest model drops (Claude Opus 4.8, Google Omni (Veo 3.4)), and why Google Flow's $200/month subscription might be the most slept-on deal in AI right now. Will then shifts gears and dives into his full operational playbook. He breaks down how his agency uses AI to close more enterprise deals (a proposal workflow that litterally turns a sales call recording into 14 spec ads, a gap analysis, and a competitor breakdown that's delivered same day). He then outlines the three-piece framework his team uses to generate better creative: gap analysis, time series analysis, and social listening. Andrew and will also get real about the messy details of AI and how tool costs can add-up fast, the danger of automating for automation's sake, and how to actually structure AI governance inside an agency. This episode closes with a four-bucket framework for how agencies and D2C brands should be thinking about AI as they move through 2026 and beyond. Key Takeaways

• The same-day proposal workflow Will is using to close enterprise deals faster and how you can too.
• Why the gap analysis the first piece of the creative puzzle and what Will's AI tool actually shows you about your ad account.
• What a time series analysis is for ad creative and how you can use the Meta API to tag every ad you've ever run by persona, angle, format, and emotion.
• How social listening on these platforms will change the ads you prioritize.
• How Google Flow's $200/month subscription actually stacks up vs. paying per-generation on SeaDance for high-volume video work.
• Why Will decided to go back to having a human editor QC every AI-generated static before it goes live.
• The TRUE cost of "playing around" with AI, and how to audit if your AI projects are actually generating revenue versus just burning tokens.
• How the smartest agencies thinking about AI governance and what every agency needs to build out now

Products & Software Mentioned

• Claude Opus 4.8 / Claude Sonnet 4.6 (Anthropic) — https://claude.ai (used for copywriting agents, tagging historical ads, and proposal workflows)
• Claude Code — https://claude.ai/code (used for proposals, landing pages, and creative workflows)
• Google Omni / Veo 3 (Google) — https://deepmind.google/technologies/veo/ (video generation model)
• Google Flow — https://flow.google/ ($200/month subscription unlocking Nano Banana, Veo 3.1, Veo 3.2, Omni)
• Nano Banana (Google) — Google's image generation model, accessible via Google Flow
• C-Dance / Sea Dance (ByteDance) — Video generation model; comparable to Google Omni but priced per generation
• GPT-4o Image Generation (OpenAI) — https://openai.com (referred to as "GPT-2" for image/static generation in context)
• Read.ai — https://www.read.ai (AI meeting recording and transcription)
• Granola — https://www.granola.so (AI meeting notes; mentioned as comparable to Read.ai)
• 11 Labs (ElevenLabs) — https://elevenlabs.io (voice AI; briefly mentioned)
• Meta API — https://developers.facebook.com/docs/marketing-apis/ (used to pull historical ad data)
• adlib.getskipper.ai — https://adlib.getskipper.ai (Will's free gap analysis tool — enter your brand, get persona/angle/emotion gaps)
• Skipper — Will's product (social listening and creative intelligence platform; referenced throughout)
• NFL.ai API — Used for generating static ad images directly into Claude Code workflow
• Higgsfield — https://higgsfield.ai (AI video platform; mentioned as an alternative to Google Flow)
• file.ai — Referenced as a platform team uses for video and static generation
• Vercel — https://vercel.com (used for spinning up landing pages quickly)
• GitHub — https://github.com (used in landing page/CRO workflow)
• HQ — Project management tool for AI workflows (referenced as a way to organize Claude Code projects)
• Codex (OpenAI) — https://platform.openai.com/docs/guides/code (mentioned as an alternative coding AI)

To connect with Andrew Foxwell reach him here [email protected]

To connect with Will Sartorious DM him here https://x.com/will_sartorius

To Connect mith Thomas Moen DM him here https://x.com/thomasmoen

To learn more about Foxwell Founders and conversations like this one, go here: www.foxwellfounders.com

This episode features Cody Plofker, of Jones Road Beauty, and one of the most talked-about voices in the DTC/e-commerce space. The conversation covers how Cody has turned Claude into a full-stack CRO and conversion engine, going 5-for-5 (as he puts it) on winning A/B tests with almost 10% CVR lifts each.

Cody also gets into his end-to-end workflow and how it all starts with his "customer intelligence" markdown file fed by live MCPs ( a combination of Junip reviews, RichPanel CX tickets, OuterSignal, ListenLabs, GA4, Heatmap API). He also covers how he built Jones Road's entire design system inside a GitHub markdown file so he can now one-shot landing pages at an 80% quality rate in a single session.

Andrew and Will dig in with the harder questions from why human taste still matters, why QC agents alone won't save you and how to decide whether to build a landing page first or an ad first, The episode closes on KPIs and how Cody is raising output expectations across the board and tying AI utilization directly into performance evaluations. Key Takeaways:

• How a CRO intelligence process that's pulled from customer reviews, support tickets, surveys, heatmaps, and GA4 can change the quality of your test hypotheses.
• What you're actually leaving on the table when relying on QC agents to catch everything in your AI output.
• Why you should be building your ads & landing pages from a single brief.
• How your team can go from idea to live within 24 hours to get more testing cycles and improve your overall funnel launch standard.
• How to use this analysis to surface winning persona/angle/format combinations you quietly turned off a year ago.
• Understanding how much of your competitive moat actually lives in the quality of data and documentation you've fed into your AI system.
• When you tell your team AI adoption is mandatory and the approach that creates real behavior change.
• What "best in class" output actually looks like now that AI has raised the ceiling on what a single operator can produce.

Tangible Links

• JunipProduct: reviews platform with MCP filejunip.co
• RichPanel: AI-powered ecommerce CX helpdesk - richpanel.com
• OuterSignal: Customer intelligence and data enrichment - outersignal.com
• ListenLabs: AI-moderated customer interviews and surveys - listenlabs.ai
• Typeform: Survey tool - typeform.com
• Granola: AI meeting notes tool - granola.ai
• GitHub: Code repository - github.com
• Google Trends: Trend monitoring - trends.google.com
• Reddit: Community platform - Reddit.com
• ClickUp: Project management software - clickup.com
• Tempo: Recently launched tool mentioned as an example of commoditized software in the design/build space - WithTempo.Ai

To learn more about Cody Plofker and his team at Jones Road Beauty go here: https://x.com/codyplof https://www.jonesroadbeauty.com/

To connect With Andrew Foxwell reach him here [email protected] To connect with Will Sartorious DM Him Here https://x.com/will_sartorius To Connect With Thomas Moen DM him Here https://x.com/thomasmoen To learn More about The Foxwell Founders Community and the conversations, like this one being had go here: www.foxwellfounders.com