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.

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