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.

I saw something the other day that I wasn’t supposed to.

In a Seller's case log, a support response accidentally included the AI’s internal chain of thought.

• Template evaluation.
• Decision logic.
• Why each pre-written response did or didn’t apply.

All visible.

In this support flow, your case gets compared against a library of pre-written response templates. Each template has an internal codename tied to a scenario.

The ones visible in this case:

→ brand_protection_asin_reinstate_infringement_violation
→ loa_not_valid_incorrect_format
→ ASIN is tombstoned
→ brand_protection_acknowledge_violation

The AI evaluated each one, documented why it didn’t fit, then generated a custom response because nothing in the library matched.

This explains what sellers experience every day.

When your issue matches a template, the reply is coherent, and the next step is clear.

When it doesn’t, the system starts guessing. That’s when you get generic responses, denials that miss the point, and loops that never resolve.

The wild part is what happened in this case.

The AI flagged the original action as a false positive.

It identified the seller as a valid licensee who was missed in the review process.

Humans made the error. The AI caught it.

Most sellers never get that outcome because most cases do not force a true custom path. They get shoved into the nearest template, whether it fits or not.

This is why you can submit perfect documentation and still get denied.
Wrong template match.

This is why the same appeal gets approved once and rejected twice.
Different template match.

This is why escalation can fix everything instantly. You finally got routed correctly.

We have been treating Seller Support like a conversation. It’s a case processing engine designed to classify, route, and respond at scale.

Write and structure your cases accordingly.

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

A screenshot of an internal Amazon Seller Support case log revealing the AI’s hidden "Thinking" process. The image displays a breakdown of the AI evaluating a seller's ASIN reinstatement request against specific internal template codes, such as "brand_protection_asin_reinstate_infringement_violation" and "loa_not_valid_incorrect_format." The text shows the AI explicitly rejecting these pre-written blurbs because the seller was a "valid licensee" and the violation was a "false positive." The AI concludes that "No blurb template fits this use case" and decides to generate a custom response. The graphic features a stylized robot avatar representing the automated decision-making engine behind Amazon Seller Central support.

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.

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

Your prompt library is worthless.

I've watched the Amazon seller space spawn 40+ "AI experts" overnight.

Same people who were "compliance consultants" not long ago. "Brand strategists" before that. "Listing optimization specialists" before that. "PPC gurus" at one time or another.

Now they're keynoting AI summits. Teaching masterclasses on how to talk to a chatbot. Selling access to their custom GPTs.

These people are capitalizing on business owners' fear of getting left behind. They're selling "AI solutions" to operators who are genuinely trying to figure this out, but they're not equipping anyone with anything real. They're packaging surface-level tactics as expertise and charging for it.

The gloves are off.

If the most advanced thing someone is doing is organizing prompts by color and generating UGC scripts, they are not an AI expert. They are a content creator who figured out how to use ChatGPT.

Prompt engineering is not a skill. It's table stakes. It's the equivalent of knowing how to Google something in 2008 and calling yourself a search specialist.

And if you see someone who suddenly popped up offering "Rufus optimization" packages or "AEO services," run. That is the 2026 scam artist's delight.

Here is the gap between talking about AI and building with it.

While they were color-coding prompt vaults, our team was deploying actual infrastructure. In December alone:

→ Self-Healing Supply Chains: Agents that predict stock-outs and draft supplier orders before a human even logs in

→ Context-Aware CS: Agents that cross-reference shipping data with customer LTV scores to make autonomous refund decisions

→ Persistent Memory: Architecture that remembers everything, injecting context across workflows without human hand-holding

They are playing with chatbots. We are deploying a digital workforce. They are automating text. We are automating decisions. They are teaching inputs. We are engineering outcomes.

They're already obsolete. They just don't know it yet.

We will still be here. Building the machine that puts them out of business.

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Talks about #ai, #amazon, #ecommerce, #fakereviews, and #machinelearning

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.

I've been trying to start a newsletter for over a year. And failing.

Not because I lack ideas.
Not because I'm lazy.
Only because my team and I are busy actually doing the work.

We run real systems inside real e-commerce operations. That time has to come from somewhere.

Which brings me to something I've never been able to ignore.

I don't understand how some people attend every conference, host weekly webinars, go on endless podcasts, post daily, ship flashy infographics, and still claim to be deep in the trenches.

We had a term for that growing up. "Funny Money." Not to be trusted or aligned with.

Here's the irony nobody wants to say out loud: the people doing the work rarely have time to talk about it. The people talking nonstop usually aren't doing it.

This is not a blanket statement. There are real operators here who have my full respect.

But there is also a large class of professional marketers cosplaying as operators.

That matters more now than ever.

It's 2026. This is the year.

We went from chatbots to GPT wrappers to tool-using models to fragile agents. There's been a lot of hype. A lot of "coming soon." A lot of almost.

That phase is over. The systems are now actually capable. 2026 is sink or swim.

The stakes have never been higher. This is when we'll see real separation between winners and losers.

Some people are using this moment to scare others. Some are selling fake shortcuts. Some are repackaging things they learned three months ago.

That is not how you win this decade.

So I'm done waiting.

I'm launching a newsletter within two weeks.

- Not paid
- Not selling services
- Not looking for more work 

It's for sellers, operators, and builders. Whether you're technical or not. Whether you write code or have never touched a terminal. If you're running a business and trying to figure out how AI actually fits into your operations, this is for you.

Real workflows. Real failure modes. Real constraints. No hype.

And while I'm at it:

❌ No, I'm not going to Prosper or any other show. Stopped that years ago.
❌ No, I'm not sponsoring any of your circus events.
❌ No, I don't want to do your podcast.
❌ No, I'm not available for a “virtual coffee”, whatever the hell that is.

We never cared about being in the cool kids club in the Amazon seller space. We're not starting now.

We're too busy building the things others spend all day talking about.

Two weeks. Keep an eye out.

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Google Analytics Academy: Digital Analytics Fundamentals

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• Developed an AI-based system to combat deceptive product reviews on Amazon.
• Collaborated with team to pioneer innovative solutions for online marketplace challenges.
• Implemented strategies to ensure product authenticity and consumer trust.
• Utilized advanced technology to enhance customer experience and drive sales.

E-Commerce Specialist

As the only team to have built the innovative AI-power software capable of identifying abusive and fraudulent reviews on the marketplace, we are now taking things to the next level. No other words needed. 💯

Ecommerce and Machine Learning Specialist

→ Developed and launched one of the first Amazon FBA Reimbursement methods in the industry
→ Assisted with over 100 brand launches on the Amazon platform 
→ Worked with top Amazon sellers to to develop and implement account health related systems 
→ Assisted with competitor attacks, account suspensions, brand protection and more.

Independent Consultant

Innovative online strategies for Amazon sellers, developing e-commerce software solutions, business consulting and brand protection services for retailers in the digital age

Worked side by side with the one founder to build, brand, launch, and operate the firm from day one. Assisting Amazon sellers of every experience level, account type and sales volume. 

Worked with sellers to resolve issues including blocked ASINs, suspended accounts, competitor attacks, product launch strategies, FBA & inventory concerns and much more. Was solely responsible for developing advanced systems, workflows, and SOPs to scale the operation drastically.

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Machine Learning Specialist | AI-powered Miracles 🚀

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AI + E-commerce. The authority.

Most teams buy AI tools to solve a workflow problem they haven't actually mapped.

So the tool becomes a new tab. Not a system.

Here's the trend I keep seeing in Amazon and e-commerce ops right now:

Teams are moving from "prompting" to "pipelines."

The winners are doing three things consistently.

1. They pick one painful metric, not a vague goal.
Return rate. Ad waste. Case response time. Catalog error rate.

2. They force structured inputs.
No free-form chaos. Clean fields, controlled vocab, consistent naming.

3. They build feedback loops.
Every output gets a pass/fail label. Every failure becomes a rule or a missing data requirement.

This is why two brands can use the same model and get radically different outcomes.

The model is not the moat.

Your operational discipline is.

AI rewards teams that already respect process.

Most teams use AI backwards for listings.

They ask it to write copy. Then they argue about whether the copy is right. Then they rewrite it. Then they argue again.

The problem was never the copy. They skipped the alignment step entirely.

Before any generation happens, you need a blueprint. Who this is for. What problem are they solving? The differentiators that actually matter and what proof backs each one up.

A claim map showing what's allowed, what's risky, and what gets you flagged. Keyword architecture. Objections the listing needs to handle without triggering compliance.

Then you generate. Not before.

We cut revision cycles by 60% after making this change. The AI didn't get better. The inputs did.

Copy is execution. Blueprint is strategy.

Most teams skip strategy and then wonder why execution keeps failing.

Prompt frameworks are nothing but productivity theater.

They look smart.
They feel technical.
They waste time.

Here’s what actually happens in the wild:

• The prompt works once in a demo
• Fails the moment data shifts
• Breaks on edge cases
• Gets silently abandoned

Meanwhile, the operator thinks they “implemented AI.”

They didn’t.

They memorized a spell and hoped reality stayed polite.

Slapping an acronym on the spell doesn’t make it a system. It just makes the illusion easier to sell.

AI systems that survive real businesses are not built on clever phrasing. They’re built on data contracts, constraints, retries, and logging.

If your AI breaks because someone reworded an input, you didn’t build a system.

You built a magic trick.

Magic doesn’t scale.

Most “AI experts” collapse the moment you ask operational questions.

I’ve watched it happen live.
Webinars. Courses. Mastermind stages.

Everything sounds tight until someone gets specific.

Ask for current AI-driven traffic. Measured today. Not projected.
Ask if the automation they’re demoing is still running in production.
Ask about maintenance costs. Breakage. Human babysitting.
Ask what failed and never made it into the slides.

The response pattern is consistent.

Silence.
Vague redirects.
“It depends.”

That’s the tell.

You’re not learning from someone who operates. You’re watching someone theorize in public.

Operators can talk about what’s live, what broke, what it costs, and what they killed. They do not need cover stories.

Next time you’re at one of these events, put pressure on the mechanics.

The real ones lean in.

The rest fumble.

Claude's Cowork is the most powerful AI tool ever released for operators.

That's not hype.
That's not a demo take.
That's an execution verdict.

For non-developers, Cowork unlocks capabilities that were previously unreachable.

Most Amazon and e-commerce operators are not technical. That has always been the bottleneck.

They know the business. They know what needs to be built. They could never turn intent into execution with AI.

Until now.

That level of execution lived behind Claude Code. Developers had it. Operators watched from the sidelines.

I've been testing Cowork since it dropped. Here's what it actually does in practice.

I gave it a raw product catalog export. 847 SKUs. I asked it to flag missing attributes, inconsistent titles, and duplicate parent variations.

It didn't return a summary. It produced a structured audit file with every issue categorized and ranked by severity, ready to assign.

Total time: 11 minutes.

Claude took developer-grade execution and put it behind an interface that non-technical operators can actually use.

What this means operationally:

- Competitor pricing reports built from raw data dumps
- Full-catalog listing audits
- Return reason analysis with root-cause classification
- Ad performance summaries without touching a pivot table

The skill shift is no longer technical. It is operational clarity.

If you can define the outcome, Cowork can execute the work.

For the operators who watched Claude Code from the sidelines. Who understood the power but could not bridge the gap. Who have the operational instincts but not the technical chops.

Your moment just arrived. 2026 is going to be a CRAZY and AMAZING year.

Operators who think in outcomes are about to move very fast.

Your return reasons are telling you something. You're not listening.

We built a system that classifies every return into four buckets: product defect, listing mismatch, shipping damage, and buyer's remorse. Then trend them over time.

Last quarter, it caught a defect pattern three weeks before the first negative review.

340% spike in "broken on arrival" comments. No reviews yet. No support tickets.

Just return data sitting in Seller Central.

We pulled the units. Fixed the issue. One bad review instead of dozens.

Returns are leading indicators. Reviews are lagging indicators. By the time someone's angry enough to write a 1-star review, the damage is already done. The silent returner gave you the same information weeks earlier. You just weren't looking.

Returns tell you what reviews won't.

AI will not replace Amazon operators.

But it will replace operators who refuse to change how they work.

The winning teams are redesigning workflows around AI, not bolting tools onto chaos.

What that looks like in practice:

→ Every repetitive task starts with a decision tree
→ Every decision tree becomes structured input
→ AI handles the 80 percent that is deterministic
→ Humans handle judgment and edge cases

The result is not fewer people.

The result is fewer fires, faster cycles, and calmer teams.

The advantage is not the model.

The advantage is the system built around it.

Amazon Ads is no longer an advertising platform.

It's becoming the operating system for e-commerce media buying.

At CES 2026, Amazon quietly unveiled what this actually means:

→ Full Funnel Campaigns launching Q1 that use natural language to build awareness-to-conversion strategies
→ Creative Agent that reads your reviews, analyzes competitor features, and generates campaign storyboards
→ Ads Agent that explains campaign strategy in conversational language
→ Two operating modes: "Smart Mode" for automated everything, "Expert Mode" for manual control

Amazon DSP now connects premium video, CTV, and display inventory into a single platform. They've partnered with Netflix and Disney so advertisers can plan, buy, and measure across those channels directly through Amazon.

The message from Alan Moss: "At some level, you want to be the 'everything store' for advertising."

Here's what this means for sellers still running ads the old way:

The value is shifting from execution to architecture.

Anyone can set up a Sponsored Products campaign. The AI will do that for you now.

The competitive advantage moves to:

→ Building creative systems that produce testable variants at scale
→ Designing measurement frameworks that capture full-funnel impact
→ Creating data pipelines that inform the AI with your business logic
→ Developing feedback loops between ad performance and product decisions

The sellers who treat Amazon Ads as "something the ads person handles" will get outpaced by teams treating it as operational infrastructure.

Amazon is building the system. The winners will be the ones who know how to feed it.

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