Joe Nilsen

Machine Learning Engineer 🤖 Revolutionizing E-Commerce with AI

The AI conversation in e-commerce has been hijacked.By people who've never shipped a product. By "experts" who discovered LLMs last year and now talk like they invented them. By creators recycling the same surface-level garbage with a fresh screenshot.They're loud. They're everywhere. And they're getting real operators burned.I've spent 15 years in the Amazon trenches. Built and exited two brands. Architected the systems behind TraceFuse, Seller Strategies, and Selletek.Now I deploy AI inside real operations while the influencers are still workshopping their next thread about it.I'm not here to add noise. I'm here to end it.What I share is the operational reality of AI that nobody else will show you. What to automate and what to never touch. Workflows that hold up under real volume. The mistakes that sink teams. What's working now versus what's already dead.No fluff. No theory. No "exciting updates coming soon."If it isn't deployed, it doesn't exist.

Founder

Our vision is to make the world a more connected place through the art of cooking and passion for food. The platform was launched in 2015 and was built upon challenging the status quo. Over the years, Culinary Connect evolved into a movement celebrating cooking, food, and love. These core principles are the cornerstone of what the modern-day brand is built upon.

The Rufus optimization advice you're about to hear at conferences is mostly garbage.

We know because we tested it.

300 real Rufus queries. Category browsing, product comparisons, buying prompts.

Here is the gap between the slides and the data.

"A+ Content is critical for visibility"

Reality: A+ was referenced in 43% of queries. Bullets appeared in 85%. Reviews in 92%.

Rufus is not an A+ reader. It is a bullet and review synthesizer.

"Optimize images for AI shopping"

Reality: Rufus contradicted packaging claims in 26% of tests. When image text conflicted with backend data, Rufus ignored the image.

Backend truth beats visual marketing.

"The Overall Pick badge helps recommendations"

Reality: In most "best option" queries, Rufus recommended products without the badge. It runs its own value math.

Badges are decorative. Rufus runs calculations.

"More reviews equals more visibility"

Reality: Volume plateaued fast. Content did not.

We watched Rufus recommend a lower-review challenger because users complained the market leader's container was hard to open.

Rufus read the complaints. Then used them as evidence.

The hierarchy after 300 queries:

→ Reviews are the primary evidence layer
→ Bullets define differentiation
→ Backend attributes must match claims exactly
→ Value per unit is calculated automatically
→ Images are secondary and overridden when conflicting

Sellers optimize for the PDP.

Rufus optimizes for accuracy.

Frameworks are opinions. Queries are evidence.

I don’t follow AI trends in e-commerce. I create them.

Infographic titled "Inside the AI Shopper’s Brain: How Rufus Really Decides." This visualization maps the actual data hierarchy Amazon’s Rufus AI uses to generate answers, based on research of 100 queries, debunking common Amazon SEO strategies. The central element is a glowing blue neural network brain labeled "RUFUS AI SYNTHESIS ENGINE," illustrating how the generative AI processes information.

On the far left, a section titled "THE 'TRUTH' FUEL (Source & Frequency)" ranks the inputs Rufus prioritizes. At the very top, the most critical source is "REVIEWS (95%+)," accompanied by chat bubble icons, indicating Rufus relies heavily on them for "Sentiment & Specific Quotes". Below that is "TITLE & BULLETS (85-90%+)," with a document icon, showing these are the primary source for "Key Claims & Specs" and backend attribute confirmation. The third tier is "PRICE (80%+)," with stacked coins, showing Rufus uses this data for "Per-Serving/Unit Value" calculations, ignoring decorative badges. 

Amazon gurus are teaching AI image shortcuts that don't work in real catalogs.

The prompt template pitch goes like this: Here's a prompt. Copy it. Paste it. Get professional images in minutes. Save $2000 on photography.

That's the fantasy. Here's the reality.

I've been generating AI images for e-commerce since the day each image model was released. The learning curve is steep. Don't let anybody tell you different.

What actually makes it work:

→ Context files. You need to tell the model what your brand looks like, what you've done before, and what your competitors are doing wrong.

→ Example libraries. The more reference images you feed it, the faster it understands what you want. This takes time to build.

→ Specification depth. "Make it look professional" gets you garbage. "White background, 45-degree angle, soft shadow, lifestyle context showing kitchen counter with morning light" gets you usable output.

The upfront investment is real. Hours of setup before you get anything worth posting.

But here's where it flips.

Once you've built the context, once the model knows your brand, iteration drops from hours to minutes. What used to take a photoshoot takes a prompt.

That $2000 in savings is real. But only after you've built the skills and context that make it possible. Not some 8-page SOP.

The shortcuts don't exist.

Illustrated image of a man at a conference podium wearing a t-shirt that says "AI expert" with an exaggerated surprised expression and gesturing hands. The image is intentionally AI-generated with visible artifacts, including slightly distorted hand proportions and an overly stylized cartoon appearance. A blurred presentation screen and audience members are visible in the background. The image satirizes self-proclaimed AI experts teaching e-commerce sellers shortcuts for AI image generation that require significant skill and context-building to actually execute well.

"Amazon AI Experts" are celebrating basic MCP connections as if they'd just split the atom.

Over the last three months, we've deployed 40 Claude automations across the brands we partner with.

33 are still running in production.

The 7 that failed taught us more than any $900 course or summit ever could.

Connecting two APIs isn't engineering. It's page one of the documentation. It's the tutorial. Not the product.

Amazon AI that wins engagement on LinkedIn and gets applause at keynotes rarely reflects reality.

Here is what applied AI actually looks like in the wild:

• The Compliance Warden: Flags suppression risks before Amazon's bots wake up.

• The Review Miner: Scrapes reviews to find (and inject) the exact language buyers use.

• The PPC Sniper: Audits spend daily and tells us exactly where to cut budget.

• The Watchdog: Alerts us the moment a competitor shifts strategy.

Not concepts. Production systems doing the dirty work.

The difference between a Guru and a Builder is simple: One looks good on a slide deck. The other prints time and money.

This is the work that doesn’t fit into a 45-minute talk.

Comment "RIPOFF" and I'll send you a link to my $5,000 Mastermind where I read this post out loud.

Satirical cartoon illustration showing 12 caricatures of stereotypical Amazon and AI influencer personas with titles like Dr. Chip Silicon Amazon AI Expert, Guru Gary Prime AI Strategist, Tech Bro Tony Image Generation Expert, and Nancy Neural Deep Learning Diva, each wearing branded merchandise and holding coffee mugs, representing the gap between LinkedIn AI gurus and actual practitioners who build production systems

Claude skills are the most underrated feature in AI right now.

Most people are still treating Claude like a chatbot. Ask a question, get an answer, move on.

That's not how you build systems.

Skills are different. You package context, examples, and instructions into something AI can call on when needed.

→ Context Retention: Your brand voice persists across every conversation
→ Task Velocity: Skip repetitive setup, jump straight into high-value work
→ Standardization: Your quality standards are enforced automatically

The fundamental concept here isn't going away. Models will get smarter. Interfaces will evolve. But the pattern of structured context packaged for AI to reference on demand is a permanent infrastructure.

Every organization serious about leveraging AI needs to invest time in building these.

Our team has been using a platform we built specifically for creating skills. Ten days in, the outputs are stunning.

I'm sharing access by the end of this week. Let me know if interested.

MajestIQ Claude Skills Builder platform interface displaying a no-code AI skill creation tool on dark background. Main headline reads Build Powerful AI Skills in Minutes with gold accent on Powerful AI Skills. Subheadline explains the process: Create custom AI Skills step by step, define the persona, upload your knowledge, set the rules, and export ready to use skill files. Three primary action buttons shown: Start from Scratch, AI Generate, and Use a Template. Below the buttons is a View Example Skills link. Three feature cards displayed at bottom highlighting core capabilities. First card Context Retention with brain icon explains Your AI remembers your brand voice SOPs and preferences across every conversation. Second card, Task Velocity, with a lightning bolt icon, states Skip repetitive setup and jump straight into high-value work with pre-configured expert.

SHORT BIO:

Dr. Fou has been on the front lines of digital marketing for nearly 3 decades. It is from that vantage point that he studied and documented the nexus of cybercrime and ad fraud. As an investigator, Dr. Fou assists government and regulatory bodies; as a consultant he helps clients strengthen cybersecurity, mitigate threats and risks, including the flow of ad dollars that fund further criminal activity, and improve the effectiveness of their digital marketing campaigns.

Dr. Fou was the former Group Chief Digital Officer of Omnicom's Healthcare Consultancy Group, a $100 million group of 8 agencies serving pharma, medical device, and healthcare clients. Dr. Fou also taught digital strategy and integrated marketing at Rutgers University and NYU's School of Continuing and Professional Studies. 

Dr. Fou completed his PhD at MIT in Materials Science and Engineering at the age of 23. He started his career with McKinsey & Company and previously served as SVP, digital strategy lead at McCann/MRM Worldwide. 

ARTICLES / PUBLICATIONS:
https://www.linkedin.com/today/author/augustinefou
https://www.forbes.com/sites/augustinefou
http://www.slideshare.net/augustinefou/presentations

#FouAnalytics - "see fou yourself"

what is it?
https://www.linkedin.com/pulse/fouanalytics-alternative-google-analytics-fraud/

how is it different?
https://www.linkedin.com/pulse/does-your-analytics-platform-have-cybersecurity-dr-augustine-fou-/

how to use it?
https://www.linkedin.com/pulse/successful-facebook-campaigns-measured-fouanalytics-augustine-fou

shh, don't tell anyone
https://www.linkedin.com/pulse/why-its-hard-bots-avoid-fouanalytics-detection-the-independent

Augustine Fou

YouTube is legit! 

at least a quarter of the clicks were from humans ;-)

(dark blue, FouAnalytics data) - https://lnkd.in/eMz7t8ja

Dr. Augustine Fou

AI-manipulated or not, what I am seeing is a U.S. citizen being shot in the back of the head, while down on the ground. 

Newsguard: AI-Manipulated Image Shows Gun, Not Phone, Held by Killed Protester in Minneapolis - https://lnkd.in/eq8aaiWS

Citing an AI-manipulated image, conservative accounts falsely claim that Alex Pretti had a gun in his hand when he was fatally shot by ICE agents.

Why Does Dr. Fou Hate Agentic AI So Much?  (in the context of digital advertising, where bots plan the media buy and then buy the media, from other bots).

View newsletter: FouAnalytics Practitioners

DISCLOSURE: written by Gemini In the rapidly evolving landscape of advertising technology, the concept of agentic AI has emerged as a high-stakes fron

Open article: Why Does Dr. Fou Hate Agentic AI So Much? by Dr. Augustine Fou • 4 min read

Why Does Dr. Fou Hate Agentic AI So Much?

confirmed, every platform is claiming credit for the conversions; if you added up all the claimed conversions it would be 3 - 4X your actual sales. 

and then there's attribution fraud where attribution is deliberately manipulated to steal more ad budgets https://lnkd.in/ez-fuTps

CEO @ Disruptive Digital, a Top Meta Agency Partner | Ex-Facebook

If you run Meta and Google Ads, this is probably familiar:

• Meta says it drove the conversion
• Google says it drove the conversion
• GA4 disagrees

Nothing is broken.

In-platform attribution was never meant to be truth. It exists to optimize inside each platform.

Meta measures influence.

Google measures intent.

One conversion. Two claims. Expected behavior.

The mistake is comparing platforms in isolation.

The answer is judging performance at the blended level.

Attribution is not about being right.

It is about being useful.

View newsletter: Digital Marketing Navigator

If you advertise on both Meta and Google, you have probably experienced this: Meta says it drove the conversion Google says it drove the conversion GA

Open article: Meta Ads vs Google Ads In-Platform Attribution: How It Actually Works (And Why the Numbers Never Match) by Maurice Rahmey • 5 min read

Meta Ads vs Google Ads In-Platform Attribution: How It Actually Works (And Why the Numbers Never Match)

this is an extremely tame example of 5 display ads on a page (2 display ads didn't arrive in time, didn't render, hence blank) and 1 video ad (lower right)

In-situ screen shots from FouAnalytics helps you see where your ads ran and IF your ads arrived in time to be rendered on screen. more examples here - https://lnkd.in/eHMCxTsM

families of AI generated, templated sites 100% fake content, 100% AI generated images, some domains are days old but already eating millions of programmatic impressions. - https://lnkd.in/e89s5BTX

legacy verification vendors sent press releases last year about their AI slop site detection. Obviously doesn't work, like the rest of their fraud detection which has reported 1% fraud for the last 10 years, with no explanation.

Publishers, heads up --  FouAnalytics for PUBLISHERS

let me know what you think

This is a long time coming. I have seen good publishers get harmed over the years by "adtech." Almost every new tech or service they tried has led to 

Open article: FouAnalytics for Publishers by Dr. Augustine Fou • 4 min read

10 days in(to) 2026 - https://lnkd.in/eneDme9n

three major brand advertisers paid in full for 2026 for FouAnalytics Enterprise
1. CPG
2. financial services
3. pharma

one publisher
4. gaming / entertainment

Happy New Year!

agentic AI = bots

put simply, AdCP is bots buying ads from other bots, all while spending/wasting YOUR ad budgets

Agentic AI, LLM crawlers traffic surges - FouAnalytics data
 - https://lnkd.in/eun5wzY6

this would be such a bad future " the primary user of ad platforms in the future are Agents, and not Humans." 

no control, no transparency, no explanation of why stuff happened, no recourse, no way to fix stuff after the money has been wasted.  oh, I guess "to spend in full" is the goal. so I guess this will just make it easier to "flush" as it were.

At the AdCP NYC meet up 6 weeks ago I told a group of hundreds of executives that Agentic Advertising would kill the ad platform (UI). 

I posted a similar sentiment here, and, well, the comment section was filled with traders and ad operators who thought otherwise. 

And rightfully so! As an ad operator I can tell you that an ad platform's most valuable asset is its UI and UX,  without which, you can never develop the most important thing to an ad platform. 

A power user. 

But I believe, and it appears others as well, that the primary user of ad platforms in the future are Agents, and not Humans. 

And so I ask you now, what do the power users of the agentic advertising future look like?  What UI and UX will traders and ad operators need to become even more powerful agentic traders and agentic operators?

Yahoo seems to agree.

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The Battle

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Augustine Fou

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Joe Nilsen's LinkedIn Performance

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