Joe Nilsen

Machine Learning Engineer 🤖 Revolutionizing E-Commerce with AI

I operate at the intersection of Business Operations and Technical Execution.15 Years in E-commerce | Brand Exits | Systems ArchitectMost companies solve problems by "throwing bodies" at them. I solve them by building systems that don't require human intervention.THE BACKGROUND:I’ve spent a decade and a half operating in the trenches of Amazon and e-commerce. I’ve built, scaled, and sold two Amazon brands. I also founded and built the technical infrastructure for multiple service businesses (TraceFuse, Selletek, MajestIQ, Seller Strategies), pioneering the FBA reconciliation methodologies used by the industry today.THE GAP:Most founders can describe a problem, and most engineers can write code—but rarely do they understand each other.I exist in that gap. I translate messy, manual operational friction into clean, automated code. I don't just manage the build; I architect and deploy it.THE WORK:Today, my team and I work with enterprise brands to build custom internal tools and AI agents. We focus on:‣ Automating high-friction workflows (Account health, compliance, ops).‣ Replacing manual "grunt work" with intelligent agents.‣ Building systems that allow brands to scale without linear headcount growth.THE CONTENT:I write about the "How"—practical, production-level insights on deploying AI in real businesses. No fluff, no theory. Just what we are building and what we are learning.

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.

OpenAI is adding ads to ChatGPT.

Most people are freaking out about privacy. Complaining about the user experience.

I'm looking at something completely different.

This is the first advertising channel in history where the platform knows exactly what problem you're trying to solve. In real-time. In your own words.

Google knows what you searched. Facebook knows what you liked.

ChatGPT knows what you're actually struggling with across an entire conversation.

For e-commerce operators, this changes the math.
 → No more bidding wars on keywords
 → No more paying for clicks from people who weren't ready to buy
 → Placement based on actual problem-solution fit

The code shows "bazaar content" and "search ad carousels." That's sponsored products appearing when someone's already in buying mode.

Sound familiar? It's the Amazon flywheel logic applied to conversational AI.

The brands figuring this out now will have the same advantage early Amazon PPC adopters had in 2015.

Everyone else will be playing catch-up, wondering what happened.

The question isn't whether you like it.

It's whether you'll be ready when it launches.

*Credit to Tibor Blaho for the image and for discovering the code. Out of anybody on any platform he is one of the best if not the best person to follow for the most up to date, relevant, and useful information related to AI

Operator & builder at the intersection of business and technology | Translating AI into operational systems that scale | 15+ years in e-commerce

Behind-the-scenes look at OpenAI's ChatGPT code showing ads are coming. 

Amazon is not a retail company.

We think of it as an e-commerce platform. But that’s just the face they show the public.

Amazon is actually a data acquisition company.

They spend 80% of their annual budget on R&D for one reason: To secure their grip on the market ten years from now.

Every time you sell a product, you are giving them data. Every time you buy a product, you are giving them data.

They use this to pinpoint exactly what sells best. And then?

They create their own version of it (AmazonBasics) and beat you at your own game.

Is it ethical? No. Is it brilliant business? Unfortunately, yes.

Don't be fooled by the "Everything Store" facade. You aren't winning the battle against Amazon.

The only way to survive is to play their game better than they do.

selletek

culinary-connect

seller-suspension

JSESSIONID

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

The most life-changing tech update I've had in months.

Not a new AI model.

Not a new GPU.

Not a new SaaS subscription.

Google Chrome added Split View for tabs.

One window. Two tabs. Side by side.

On the left: whatever I'm referencing.

On the right: whatever I'm building.

No more:
→ Hunting for the "other" window
→ Re-snapping everything after every Zoom call
→ Rebuilding the same layout five times a day

This is what most people miss about productivity.

They chase intelligence and ignore friction.

But the real leverage is almost always here:
→ Fewer context switches
→ Less window hunting
→ Fewer clicks between "see data" and "take action"

Chrome just did what good operators do.

They watched how people actually work and removed drag from the system.

Everyone's obsessed with what the next model can do.

I'm more interested in who's quietly deleting thousands of micro-annoyances from my day.

If you're building tools right now, take a page from Google:

Don't just add features.

Delete friction.

A photograph of a desktop workspace featuring a wide Samsung curved monitor displaying a Google Chrome browser window utilizing the new Split View feature in dark mode. This side-by-side layout demonstrates an optimized developer workflow. The left pane shows the GitHub repository for "amzn/selling-partner-api-samples," serving as reference documentation. The right pane displays the CodeSandbox online development environment, showing active JavaScript code in an "index.mjs" file alongside a live preview of an "Account Health Summary" dashboard indicating a "GOOD" score. The image visually represents reducing friction and minimizing context switching between two different resources and references in order to be more productive. This is relevant to digital marketers, CEOs, founders, and anybody who wants to be more effective at what they do in business.

I spent two weeks building an automation that a $15/month tool could have handled.

Not my proudest moment.

But it taught me something important about how operators should think about AI and automation.

When you're technical enough to build things yourself, everything starts looking like a build opportunity. You skip the research phase because building is more fun than evaluating.

This is expensive.

Not just in time. In focus. In momentum. In the opportunity cost of what you could have built instead.

Now we have a rule on our team:

Before we write a single line of code, we spend 48 hours trying to solve the problem with existing tools. If we can get 80% of the value with something off-the-shelf, we use it and move on.

Custom builds are reserved for problems where:

- The existing solutions don't fit our specific workflow
- The problem is core enough that owning the solution matters
- The maintenance burden is worth the control

Most problems don't meet that bar.

The best operators I know aren't the ones building the most. They're the ones building only what matters.

Everything else, they ruthlessly outsource to existing tools.

A chaotic, messy wooden desk surface completely covered in the debris of an intense two-week coding marathon, illustrating the visual reality of developer burnout and inefficient resource allocation. The workspace is littered with dozens of crushed Red Bull and Monster energy drink cans, several dirty white coffee mugs with dried residue, and mountains of crumpled white paper, symbolizing wasted ideas and effort. Tangled USB cables and charging cords weave through the mess like spaghetti code, partially obscuring a silver laptop computer buried underneath. Harsh overhead fluorescent lighting casts deep shadows, creating a gritty, stressful atmosphere that screams "technical debt." This image serves as a striking visual metaphor for the 'build vs. buy' dilemma in software operations, representing the high opportunity cost of choosing to build custom automation tools instead of utilizing existing, affordable SaaS solutions. It highlights the importance of strategic decision-making in AI.

Before there was AI, before there was a company, there was just me and a spreadsheet.

Back then, it was me, Amazon's Community Guidelines, and an obsession with figuring out how to legitimately remove negative reviews.

I was obsessed with one thing: figuring out how to legitimately remove negative reviews that violated Amazon's own rules.

Yesterday I was digging through my Google Drive and found a 39-minute video I recorded in 2022.

In it, I walk through a crude Google Sheet I’d built after two years of relentlessly testing:

• Every row = a negative review I analyzed by hand
• Every column = specific guideline violations
• Every outcome = what Amazon actually did

No fancy UI. No AI. Just pattern recognition and pain.

The nine months after recording that video were a blur of Red Bulls, sleepless nights, and manually classifying training data until my eyes bled.

The result? The most powerful AI-powered software this space had seen.

From that ugly template came remarkably robust software that went on to do the heavy lifting:

→ AI systems that processed tens of thousands of reviews a day
→ Logic that pinpointed the exact line that violated policy
→ Documentation so precise that sellers knew precisely what to cite when building their cases

I named it 𝐓𝐫𝐚𝐜𝐞𝐅𝐮𝐬𝐞 - I even copyrighted the source code under that name.

The ironic part? The fundamentals haven’t changed.

Amazon still allows honest feedback.
Amazon still removes clearly documented violations.
And the same principles baked into that 2022 template are still how serious operators separate compliant reviews from violations.

I took my team and my software and walked away from the company I founded for ethical reasons. We’ve been busy pushing the limits of AI-powered solutions in other areas ever since.

I’m not sharing that old video because it’s honestly painful to watch now.

But I did take that original template and rebuild it from the ground up:

• 10x more robust
• Much more innovative formulas and scripts 
• Structured so any seller can audit their own reviews

No software subscription.
No black-hat nonsense.
Just a battle-tested framework that works if you use it.

Want the enhanced template that started all of this? 

Comment “TraceFuse” and I’ll send it your way.

how to remove negative reviews from amazon

If anybody tries telling you that selling on Amazon is easy, they are doing one of two things: 

1. Trying to sell you a course
2. Lying

I was sitting across from the executive team of an $800M aggregator.

The mood wasn't celebratory. It was terrified.

They had just lost a top-performing account to a suspension. Because the brand ownership was tied to that seller account, their $9 million investment had just been toasted.

Fear was running the conversation.

I grabbed something to write with and drew this exact diagram in front of them.

I told them: “You have your structure backwards. You’ve handed Amazon a kill switch.”

Most sellers register their brands under the same entity that sells the product. If that account goes down, the brand goes down with the ship.

If you want to sleep at night, your assets and your operations cannot live in the same house.

Here’s the structure I walked them through:

- An IP Holding Company owns all trademarks and brands
- That entity has NO Amazon seller account
- The IP Holding Company enrolls brands in Brand Registry
- Brand Registry assigns roles to merchant accounts
- Merchant accounts are just replaceable operators

Why this matters:

Amazon controls seller accounts.
The USPTO grants intellectual property rights.

When the IP Holding Company owns the marks and controls Brand Registry:

- Your trademarks stay yours
- Your Brand Registry access stays intact
- Your ability to authorize or remove sellers stays intact

Because the real asset on Amazon isn’t the seller account. 

It’s the branded ASINs and the IP behind them:
Sales history, velocity, content, reviews, relevance.

The merchant account is just the vehicle. Amazon owns that vehicle. They can take the keys whenever they want.

Those executives looked at my sketch like I was crazy.

Two days later, they started restructuring exactly this way.

There’s no guaranteed protection on this platform. But if your brand and your selling account are the same entity, you’ve given Amazon a single point of failure for everything.

Separate them.

A hand-drawn red ink flowchart on paper illustrating the "Untouchable" Amazon brand protection strategy. The diagram highlights the critical separation between assets and operations. At the top left, a circle labeled "IP Holding Company" is marked as "[Untouchable]." Notes indicate this entity "Owns All Trademarks/Brands" and explicitly has "No Amazon Account," insulating it from platform risks. An arrow connects this holding company to "Brand Registry." From Brand Registry, lines labeled "ROLES" extend to multiple independent circles labeled "Merchant Account 1," "Merchant Account 2," "Merchant Account 3," and "Merchant Account 4." These merchant accounts act as operators connecting to the central "AMAZON" platform oval. The diagram emphasizes resilience: at the bottom right, "Merchant Account 5" is crossed out with a large "X," symbolizing a suspended seller account. 

Gemini 3 launched today.

You’re about to see 1,000 posts saying it “changes everything.”

The real shift isn’t “smarter model.” It’s that Google is pushing AI out of the chat box and into actual workflows.

Gemini 3 is wired into Search and the Gemini app and is built to handle intent, not prompt tricks. The message is: stop asking for better prompts, start explaining the outcome you want.

Antigravity is where that gets real for non-technical teams: a workspace where AI agents can take a goal, bounce between tools, do the grunt work, and leave receipts you can audit.

For a founder or marketer, that looks like:

“Clean this catalog, flag issues, and show me what changed.”

“Audit last week’s campaigns and tell me what to kill, fix, or scale.”

“Break down why margin dropped here, with evidence.”

The gap from here isn’t who has access to Gemini 3. It’s who can turn their messy, day-to-day processes into something an agent can reliably own.

This post is going to piss off some big Amazon sellers and service providers.

It’s not meant to stir up drama. It’s meant to stop you from getting quietly robbed.

There is an entire industry fleecing 7 and 8-figure brand owners for “Review Removal” services. They’re charging $200–$500 per removal.

But here is the dirty little secret they don’t want you to know: In many cases (sometimes even all), they didn’t remove the review. Amazon did.

Your card is getting drilled by a company taking credit for Amazon’s enforcement.

They are selling you the rain, claiming they did a rain dance.

Here is the reality:

Amazon’s engineering teams have made massive strides in detecting abusive network patterns.

They are proactively cleaning up the marketplace faster and more effectively than ever before.

This is a win for platform integrity.

But it’s a goldmine for scammers.

Because the better Amazon does its job, the easier it is for these agencies to take credit for it.

The Scam Mechanics:
 - Amazon’s sophisticated systems detect and remove a non-compliant review.
 - The agency’s script flags the 404 error (dead link/dog page).
 - They send you an invoice claiming a “win.”

If you are currently paying a vendor for removals, look for these red flags in their pitch deck:

🚩 “100% ToS Compliant” (Great. Prove it.)
🚩 “Lifetime Monitoring Service” (“We commit to taking credit for all reviews we didn’t remove forever”)
🚩 “Pay Per Performance” (The perfect model for this scam)
🚩 “Internal escalation contacts” (They don’t have them. Amazon won’t even let these companies into their Service Provider Network.)

If you have a vendor claiming they are wizards at removal, send them this email today.

Watch how fast they stop responding.

“To verify our invoices, please provide the following for the last five removed reviews:
- The Case ID created BEFORE the review disappeared
- The specific policy violation cited in that case
- The official response from Amazon’s Community Team confirming the removal”

They won’t be able to provide it, because the case never existed.

The only thing they can provide is a screenshot of a case filed AFTER the review was taken down.

We built systems to identify non-compliant reviews years ago. Amazon’s tech has now caught up, and they proactively remove nearly all non-compliant reviews.

If their “proof” is a dead link, you’re not hiring a removal expert. You’re paying thousands for access to a script to capture screenshots of Amazon’s enforcement.

A comprehensive infographic titled "The Amazon Review Removal Scam Exposed" featuring a "Forensic Audit" stamp in the top left corner. The image visualizes how predatory agencies fleece Amazon sellers using the deceptive "Rain Dance" scam. The left panel depicts Amazon’s automated engineering systems and algorithms proactively detecting and deleting non-compliant reviews to ensure platform integrity. The center panel details the scam mechanics: a hooded consultant claims credit for Amazon's internal work by running scripts to identify 404 error dead links, then invoicing the brand owner for a coincidence rather than an actual service. The right panel warns 7 and 8-figure brand owners of specific red flags in vendor pitch decks, including "100% ToS Compliant" guarantees, "Lifetime Monitoring Services," "Pay Per Performance" models, and false claims of "Internal Escalation Contacts."

“Your Amazon Seller Privileges Have Been Removed.”

It’s 3:47 a.m. You roll over, open the app, and your stomach drops.

If you've sold on Amazon long enough, you know that notification is always lurking.

No direct line of communication. Listings suppressed. Cash flow frozen while Amazon “reviews your account.”

It feels like a punch in the ribs.

I’ve sat in that mess with sellers for 15+ years. It’s rarely just the sloppy operators who get hit. I’ve seen seven-figure brands with beautiful metrics get blindsided by a bot flagging “used sold as new” just because a carrier damaged the shipping box.

The waiting is the worst part. Refreshed emails. Zero sales. Copy-paste support replies.

Here is the hard truth: You cannot survive on Amazon in reaction mode.
Your best defense is running offense every single morning:

1. Audit before you optimize: Check Account Health and Performance Notifications before you ever look at ad spend.

2. Watch the smoke detectors: One complaint is bad luck. Two is a pattern. Live in your Voice of the Customer (NCX) dashboard. Catch the smoke before it becomes a fire.

3. Have the “Red Folder” ready: Keep a pre-written playbook and legal templates for IP complaints and authenticity claims. When the notice hits, you shouldn’t be drafting—you should be executing.

4. Define the escalation path: Decide now who you call when it’s bigger than you. Internal team or external counsel? That decision should be made while you're calm, not while you're panicking.

Amazon is not a passive income stream. It’s a contact sport.

The sellers who last aren’t the ones who never get hit.

They’re the ones who treat every punch as data, build systems around it, and show up the next day already on offense.

We found something that shouldn't work.

A pattern in Amazon's enforcement system that contradicts what most sellers believe about compliance.

The conventional wisdom:
→ Follow the rules
→ Stay under the radar
→ Don't draw attention

What we're seeing after tracking nearly 50 accounts over three months:

Accounts that "follow the rules" still get hit.
Accounts that manage one specific signal differently don't.

It's not about compliance. It's about what Amazon's systems perceive.

Here's the uncomfortable part:

Amazon doesn't read your intentions. It reads behavioral patterns. And most sellers are triggering the wrong signals without realizing it.

Without giving away the whole methodology:

Velocity changes. Not just in sales. In everything. Listing edits. Inventory adjustments. Accounts that maintain consistent operational rhythms are flagged less often than those with erratic but compliant behavior.

The system isn't asking "did they break a rule?"

It's asking "does this account behave like accounts that break rules?"

If you've ever been suspended and thought "I didn't do anything wrong" - you might have been right.

You just looked wrong.

Why does Amazon allow "ratings only" reviews now?

Because the average human being in 2025 has the attention span of a golden retriever.

No one wants to write a long, detailed review anymore.

If you see a long review, it's probably fake, incentivized, or written by the seller's mom.

Amazon knows this.

They know real buyers are busy.

They know real buyers just want to click 5 stars and move on.

So Amazon introduced ratings-only to get real data.

It’s better than nothing.

Sellers hate it because they can't control it.

But if Amazon didn't adapt, the only reviews left would be the fake ones.

It’s all about data.

It always is.

We actively avoided AI image generation for two years.

Not because we didn't want to use it. But because it simply wasn't good enough.

As operators building software for enterprise brands, we couldn't risk integrating tools that were "fun" but fundamentally broken. We tested everything. But every time, the text came out garbled or the product looked slightly "off."

It was usually faster to just hire a designer than to spend hours fixing the AI's mess. So we kept that work manual. We refused to automate mediocrity.

That changed this week.

When Google released Nano Banana Pro, we didn't just "try it out." We hammered it.

For the past week, we’ve been stress-testing it with real products from some of the largest brands on Amazon. It is the first model to finally cross the line into production use.

Here is what we found:

• It reads: You can tell it to add a "48-Hour Battery" callout, and it spells it correctly.

• It listens: You upload your reference photos, and it respects them. It doesn't invent a weird version of your product.

• It understands: It knows what a comparison chart looks like. It knows what converts on Amazon.

Last week, we built a complete 7-image listing set for a partner.

Total time: 24 minutes. The old way: 3 days and $2,000.

That isn't a slight improvement. That is a paradigm shift.

An Amazon Seller's Guide to Google's AI Image Model Nano

My biggest weakness has never been what I build.

It's who I trust to sell it.

I've spent years making this mistake. Building something that works, then handing it to the wrong person because I convinced myself I needed them.

I build. I'm good at it.

 - Systems from scratch
 - Bridging tech, marketing, ops, etc.
 - Turning chaos into something that actually runs.

What I've never been good at: sales.

I'm not the guy who charms a room of executives into a six-figure deal. So my answer was always the same - find a partner who can.

One of my last attempts was the worst.

On paper, this guy was a unicorn.

"Former professor" at a prestigious university. Featured all over the internet as a top expert. Flawless story. Perfect polish.

So I handed him the keys.

Six months in, the charm cracked.

Erratic behavior. Excuses. Stories that didn't line up. It got bad enough that I finally started digging.

What I found wasn't a red flag.
It was a smoking gun.

→ The professor thing: Made up. Never taught a single day at the university he claimed.
→ His previous "successful" business: Shut down by authorities for running a large-scale fraud scheme.
→ The publications: Ghostwritten

You can't make this stuff up.

By the time I uncovered all of this, the damage was already done.

Here's what I took from it: Self-awareness isn't just "know your strengths and outsource your weaknesses."

Sometimes the weakness is too critical to pass on.

Sales isn't just closing deals.

It's who represents your name. Who stands between your work and the market. Who has the power to burn what you've built.

No one will protect it the way you will.

If I have the skill to build the engine, I have the obligation to learn how to drive it.

I'm done handing over the keys.

There are three kinds of Amazon sellers.

The Gambler
 • Lists anything that looks profitable
 • Hopes no one notices the gray areas
 • Has no records of where half of their inventory originated from
 • Thinks suspensions happen to “other people”

The Operator
 • Has some SOPs... somewhere
 • Reactively addresses issues
 • Survives, but constantly feels one email away from disaster

The Strategist
 • Chooses products based on risk/reward, not just margin
 • Designs listings, logistics, and documentation around policy, not convenience
 • Treats every appeal, complaint, and flag as data about how Amazon thinks
 • Builds systems so one person’s mistake doesn’t tank the entire company

Here’s the harsh part:

The Gambler is already on borrowed time.

The Operator will get exhausted.

The Strategist will still be thriving 5–10 years down the road.

Get real honest about where you stand today.

If you want to be part of the minority that withstands the test of time, upgrade your systems, not just your catalog.

The three different types of Amazon sellers:
1. The Gambler
2. The Operator
3. The Strategist
All three of them have different trades, however the outcomes of how they sell are very different.

"AI-free" might soon become the most valuable label on your website.

In a world obsessed with "AI-powered," that's a strange new flex.

Here’s what’s actually happening. As automation eats everything, the absence of AI is turning into a luxury signal.

We've seen this before with trust labels.

Organic. Fair Trade. Handmade. Small Batch.

Now we're seeing the AI-era versions:
→ "Human-Verified Resolution"
→ "No bots between you and your money"
→ "Real person. Every time."

This isn't clever marketing. Some companies are issuing promises that when money, risk, or emotion is on the line, there's a real brain on the other side.

AI still runs backstage. Routing tickets. Summarizing history. Flagging risk before a human steps in.

The part customers actually touch is moving in the opposite direction. AI fatigue is real.

Everyone has been scrambling to figure out how to innovate and automate.

Some of the most critical decisions in 2026 won’t be about where to add more AI.

It will be where to deliberately withhold it.

A dark, glossy, circular digital badge or button with a prominent silver rim. In the center, large, bold white text reads "AI-FREE", and directly below it, in smaller white text, it says "Real human support". This graphic highlights a commitment to authentic, human-centric customer service in a technology and SaaS environment. The background is a dark, blurred software interface that emphasizes a premium, trustworthy user experience (CX) over automation. It represents a business focus on genuine connection and quality support in the digital age.

A founder told me last week they wanted to “automate the entire listing optimization process.”

Sounds simple enough. So I asked: “Which part?”

• The keyword research?
• The copy updates?
• The compliance checks?
• The competitive monitoring?
• The seasonal adjustments?

Turned out they meant all of it.
Plus a bunch of intuition they couldn’t even articulate yet.

This is exactly where most AI and automation projects go to die.

Not because the tech is impossible.
Because “automate it” is not a requirement. It’s a fantasy.

Here’s what actually works for us before anyone writes a single line of code:

1. Shadow the human: watch what they actually do, not what the SOP says they do.

2. Map the friction: capture every click, hesitation, and “exception” they handle manually.

3. Find the hidden 47: once it’s on a whiteboard, the “simple” flow turns into a mess of edge cases nobody mentioned in the kickoff.

When you can finally see the real process, you don’t try to automate the whole thing.

You automate the boring, repeatable decisions that actually move the needle. The weird edge cases stay manual until they hurt enough to warrant automation.

If you’re trying to turn a business vision into a technical solution, slow down.

Map reality first.

Automation and AI won't fix the messy process.

They will just create high-speed chaos.

Here’s a question I ask every established seller: 

“If Amazon shut you down tomorrow, what would still be alive 90 days later?”

Most don’t like their answer.

Run this quick stress test on your own business:

Revenue:  
- What % of your total $ comes from Amazon?  
- Would your other channels cover payroll without it?

Operations:  
- Does anyone outside of “the Amazon person” understand how the account actually works?  
- Could someone else step in and respond to an emergency in 2 hours, not 2 weeks?

Documentation:  
- If Amazon asked for invoices, supplier info, and SOPs today, could you send them within 24 hours?

Brand:  
- Do customers know you, or do they just know “I bought it on Amazon”?

If this exercise makes you uncomfortable, good.

The time to build resilience is before a performance notification hits your inbox at 3 am.

If you haven't already started, the next best time is now.

AI isn't your edge. Implementation is.

Every Amazon seller I talk to says the same thing:

"We're already using AI."

Then I look under the hood and see:
- ChatGPT open in one tab
- A few “AI-powered” tools
- VAs still copying ASINs into spreadsheets by hand

That's not an AI strategy. That's duct tape.

When we go into an account, we start with three brutal questions:

1) Where are humans doing copy/paste work?
Performance notifications into Slack.
ASINs into Google Sheets.
Returns to random trackers.

2) Where does context actually matter?
Appeals.
Escalations.
High-value catalog changes.

3) What breaks when one person gets sick?
If the answer is “everything,” you don’t have systems. You have heroes.

Here’s the simple model we’re rolling out with brands right now:

- Machines do the boring work:
Parsing notifications, clustering issues, drafting first-pass responses, flagging patterns

- Operators do the thinking:
Deciding risk, rewriting arguments, pulling evidence, pushing the right escalation paths.

- Systems keep score:
Dashboards that show issues per ASIN, win rates, response SLAs, and where tickets die

The “AI-enabled” sellers who win won’t be the ones with the most tools.

It’ll be the ones who are ruthless about workflows.

If you turned off every AI tool tomorrow, what would still work in your business?

A dramatic, high-resolution vertical close-up of a single, ornate wooden chess King standing alone on a vast, polished wooden chessboard. The King is tilted slightly to the side, capturing a moment of exhaustion or impending collapse, symbolizing the fragility of leadership and traditional business models in the face of rapid AI disruption. A single source of warm, golden-hour spotlighting hits the piece from behind, casting a long, sharp shadow that stretches forward across the rich honey-toned wood grain, evoking the high-stakes pressure of Amazon marketplace dynamics and the "Settlers" concept of territory control. The surrounding board is completely empty, suggesting a cleared competitive landscape or a "winner-takes-all" scenario. The image uses a shallow depth of field, keeping the weathered texture of the King in sharp focus while the edges fade into a soft, amber-brown blur.

Some days I feel like I’m protecting sellers from systems I helped design.

That’s what AI in e-commerce actually looks like up close.

On one screen, I’m watching dashboards light up: refund spikes, weird traffic patterns, velocity that doesn’t make sense, risk scores turning from green to red.

On the other screen, there’s a long 2 am message from a seller whose hero ASIN just lost Prime, ads are still spending, and today’s payout basically evaporated in a few hours.

AI is the reason we can keep marketplaces from getting overrun. It’s how we catch real fraud without hiring a small city. It’s how we protect buyers who never see the mess behind the “Buy Now” button.

But those same systems don’t care about context. They don’t care that the “suspicious pattern” is actually a seasonal spike. They don’t care that the “policy flag” came from one sloppy listing edit. They don’t care that a real person is sitting there watching their inventory turn into an expensive hostage situation.

I've seen both sides: I’ve seen the fraud, the bad actors, the stuff that absolutely should be shut down. I’ve also sat with good operators who did 99 things right, made one mistake, and got treated exactly the same as the worst actors on the platform.

That’s the tension we live in: move fast enough to stop abuse, slow down enough not to crush the wrong people.

The risk isn’t “AI is evil.” The risk is when a dashboard becomes “truth,” and human review turns into a checkbox. When “the system flagged it” becomes the beginning and the end of the story.

E-commerce runs on trust: buyers trusting the platform, platforms trusting sellers, and sellers trusting that the rules won’t quietly change at 3 am with no way to appeal that actually feels human.

If we forget there are real businesses, real families, and real payrolls on both sides of the API call, the whole thing breaks.

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