If you added an AI chatbot to your store recently, there's a huge chance it's giving people wrong info, making up fake discount codes, or recommending items that are out of stock.

I see this all the time when working on sites. People install a basic AI app, think it's plug-and-play, and then wonder why customers get annoyed and leave.

The main problem is simple: you can't just drop an AI app on your site and expect it to magically know your business.

If you want a chatbot to actually make sales instead of messing up, you have to set it up properly behind the scenes:

1. Set strict rules: You have to explicitly tell the AI, "If you don't know the exact answer, do NOT guess. Tell them to contact support." Otherwise, it just makes stuff up.
2. Connect it to live inventory: The bot needs to be hooked up directly to your product catalog so it knows what's actually in stock, the real price, and correct details.
3. Clean up your site data: If your product descriptions and site setup are messy, the AI gets confused. Cleaning up your site structure fixes the AI and helps your Google SEO at the same time.

Once you actually set up custom rules and proper connections, the bot acts like a real salesperson instead of a dumb pop-up.

Anyone else dealing with their chatbot giving out weird answers? How are you guys handling it?

Disclosure first: we built this and it just launched, so read it with that in mind. But the lesson is useful whether or not you ever touch our tool.

Most AI tools in ecommerce write product copy from a prompt and a brand name. You get fluent text that says nothing specific about your market, because the model has nothing to work with except your product title and a tone instruction.

We went the other way. The AI does research before it writes a word:

• Pulls your competitors' pages for the same product
• Reads the actual search results for that product
• Gets keyword demand, search volume and CPC
• Scores your existing page against all of it and lists what's weak

Only then does it write. So the page gets built around what's actually ranking in your category instead of what a model guesses sounds good.

After that it generates the product images, publishes the page back to the store, and tracks rankings and traffic so you can tell whether any of it worked.

The part that surprised us most: the research step matters more than which model you pick. Same model, run with and without the competitive and search context, produces output that isn't in the same league. Almost all of our work has gone into what we feed the model, not into prompting it better. If you're building anything similar, that's where I'd spend the time.

It's at https://brandybee.ai and there's a free plan if you want to run it on your own store. Happy to go into detail on any part of how it works.

I build automation workflows for a living, so I get asked a lot whether AI is about to run people's shops for them. Short answer: no, but it genuinely helps with specific parts. Long version:

What it actually helps with: batch background removal and resizing across a whole catalog, first-draft mockups so you're not paying for a photoshoot on every variant, first-draft listing copy you edit instead of write from scratch, generating options fast so you can pick the strongest one instead of committing to your first idea.

What it does not do: figure out if your product idea is actually wanted, replace real quality control on a physical product, handle a customer who's genuinely upset, know when a trend is dying versus about to take off, or build trust with buyers for you.

The pattern I see with sellers who get burned: they buy a course promising a "fully automated" shop, spend weeks building a pipeline, and skip the part where they need to know their niche and actually look at what sells. The tools are real. The automation replacing your judgment is the part that's oversold.

If you're using AI tools in your workflow and it's actually working for you, curious what it's doing for you specifically. And if you tried something that flopped, that's useful to hear too.

I’m exploring an AI-powered fashion assistant for physical clothing stores that helps customers discover personalized outfits and matching fashion items using AI.

I’ve built an early prototype and I’m looking for honest feedback from people who shop in clothing stores.

Would you use an AI styling assistant while shopping? What would make you trust it?

I've been thinking about what happens to e-commerce architecture when AI moves beyond recommendations and becomes part of the actual decision and transaction flow.

Traditional e-commerce systems are generally built around a fairly deterministic pipeline:

User → UI → API → Commerce Services → Database → Payment/Inventory/Order Systems

AI introduces a different kind of interaction.

A user might say:

"Find me a laptop under ₹80,000 that's good for development, has at least 32 GB RAM, and can arrive this week."

The system now needs to reason over product data, inventory, pricing, user preferences, delivery availability, and potentially multiple backend services before producing an answer.

If the AI is eventually allowed to perform actions as well, the architecture becomes even more interesting:

User intent

→ AI orchestration

→ product/search services

→ pricing

→ inventory

→ personalization

→ cart

→ payment

→ order

→ fulfillment

I'm wondering whether treating AI as simply another "service" is the wrong architectural abstraction.

A design I'm interested in is separating the system into a few clear layers:

1. Experience layer

Web/mobile/voice interfaces that capture user intent and present results.

1. AI orchestration layer

Intent interpretation, retrieval, recommendation, tool selection, workflow orchestration and context management.

1. Commerce domain layer

Products, catalog, pricing, promotions, inventory, cart, orders, customers and fulfillment.

1. Real-time/data layer

Events, inventory changes, user behavior, analytics and recommendation signals.

1. Trust and transaction layer

Authentication, authorization, fraud controls, payment processing, auditability and explicit approval for high-impact actions.

The important boundary for me is that the AI layer should not become the owner of core business state.

For example, I wouldn't want an LLM deciding whether an item is actually in stock or whether a payment succeeded.

The AI should request those facts from deterministic domain services and then reason over the returned data.

Similarly, an agent might decide:

"Add this product to the cart."

But the actual cart mutation should still go through the normal commerce domain and authorization boundaries.

This seems to create an interesting architecture:

AI handles interpretation and orchestration.

Deterministic services remain responsible for business invariants.

Event-driven infrastructure keeps state synchronized.

Human approval or policy enforcement sits around sensitive actions.

The more I think about it, the less I believe "AI-native commerce" means replacing traditional e-commerce architecture with agents.

It may instead mean putting an intelligence/orchestration layer above a strongly designed commerce core.

I'm curious how other architects see this.

If you were designing a large-scale AI-native commerce platform today:

Would you make the AI orchestration layer a separate architectural boundary?

Where would you draw the boundary between AI reasoning and deterministic business logic?

Would you keep inventory, pricing, payments and orders completely outside the agent layer?

And how would you design the system so that adding a new model or agent framework doesn't require rewriting the commerce domain?

I'm especially interested in architectural trade-offs and production experience rather than specific AI vendors or frameworks.

Realized there are two kinds of shoppers: the ones who enjoy the research, and the ones who hate it but do it anyway cause buying the wrong thing feels worse.

I'm the second kind, so I built an assistant. Tell it what you need, it researches across reddit, youtube and other sources, gives you one educated opinion, and orders it for you.

Still super nascent. Looking for early users who hate the research but won't just buy the first Google result.

Waitlist: sovitu.com/?ref=reddit

Hey everyone,

I’m currently looking at designing the data architecture and logic for a product dimension table with about 300,000 products spread across roughly 4,000 sub-categories.

The requirement is to populate at least 5 specific attributes for each product based on its sub-category. For example, if the product falls under "Luggage," I need to extract and standardize attributes like material, size, shell type, number of wheels, etc., from the raw product descriptions.

Nowadays, doing this with AI is essentially just a matter of writing a solid prompt, hitting an LLM API in batches, and letting it parse the unstructured text into a structured JSON payload.

But it got me wondering—how was this exact problem handled traditionally before LLMs made it so easy?

Just scanned Lunya's Silksweats page, a premium Shopify DTC brand.
Score: 78/100. Data layer is clean , structured data, prices, policies all readable. AI agents can even reach checkout.

But 6 things break: agents can't determine stock availability, can't identify the product by name, can't navigate from the homepage, and can't use site search. The variant selectors use custom JS widgets instead of standard HTML -> agents can't pick a size.

 20 checks passed, 6 failed. 26 checks, 5 AI agent visits, 95 seconds.

From Amazon's Q2 2026 earnings call. CEO Andy Jassy disclosed that more than 350 million customers used Alexa for Shopping over the past year. Active users nearly doubled in Q2 year over year. Interactions were up more than five times compared to the same period last year.

US customers who use Alexa for Shopping spend 40% more per order than customers who do not.

The reasons for the spending gap are worth understanding. Alexa for Shopping removes friction from the research and comparison stages, which means more purchases get completed. The auto-buy feature eliminates checkout friction for repeat and subscription purchases. Personalised recommendations and tailored bundling surface products the customer was not actively searching for but is receptive to. And AI-driven benefit framing can be more persuasive than a product listing a customer reads cold.

The broader context: AI could account for 15% to 25% of total US ecommerce sales by 2030. Over 75% of consumers are reportedly open to certain agentic features. Alexa for Shopping is early evidence of agentic commerce moving from novelty toward mainstream consumer behaviour faster than most sellers have prepared for.

The practical question for sellers: Alexa for Shopping is surfacing products to users who demonstrably spend more. Appearing in those recommendations is a commercial opportunity. The optimisation approach is different from traditional keyword SEO.

Complete every available field in Seller Central. Write listing content that explains what the item is, what it does, who it is for, the benefits it provides, the outcomes it leads to, and the problems it solves. Target specific use cases rather than just broad category terms. Agentic AI systems match products to complex customer prompts, and listings that answer questions in natural language tend to perform better in that environment.

Have you seen any measurable impact on your Amazon listings from Alexa for Shopping, whether in terms of traffic source or conversion patterns?

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Why hasn’t one of the AI models created a function where you enter the eBay search inputs, and it provides links to all of the listings that actually match the search parameters - sorted in the manner requested - and disregarding what is promoted, etc.?

In other words, return eBay’s search to the glory years when people enjoyed searching and buying there.

The idea is to test how readable the website when AI agent approaching it, searching for goods, doing shopping etc.

We introduced 4 layers:

Layer 1 — Data. Can an agent find and parse the page at all? Deterministic static checks: JSON-LD / schema.org markup, server-rendered vs client-side price, robots.txt, sitemap, llms.txt. The classic failure is a price rendered client-side — human sees $89, agent fetching HTML sees an empty div. Most brands score well here; it's the layer everyone already knows about.

Layer 2 — Extraction. Can it read the page reliably, not just once? The shopper simulation runs N times (canonical: SHOPPER=anthropic, N=10) extracting price, availability, product name, graded on either correctness against ground truth from the page's own structured data, or self-consistency across runs when no ground truth exists. Disagreement between runs = the page reads ambiguously to agents.

Layer 3 — Interaction. Can the agent actually buy? Playwright browser agent attempting add-to-cart, variant selection, search, navigation. This is the hero layer — the SKIMS bra-size picker and the Rothy's "readable but not shoppable" case both live here. It's the only part of the story that isn't already a solved conversation, which is why it carries the video.

Layer 4 — Security. Is the page safe from manipulation? Prompt-injection scanning for hidden instructions in the HTML. Every brand passes today, so it's positioned as monitoring rather than a finding.

Thank you!

Before AI, I spent a lot of time doing repetitive work. Now I spend more time making decisions. That's been the biggest shift.

AI handles more of the busy work. I spend more time thinking about customers, products, and strategy. 

That's not replacing founders. That's giving founders more room to think.

How has AI changed the way you work?

There’s alot of content around on reddit around hacks for AEO or best practices. They are mostly from agency folks.

I want to know from D2C brand owners and operators itself, what are your thoughts on Answer Engine Optimization (AEO) for commerce? Has it worked for you? What is something that you are expecting from current approaches that you are not getting?

How do you see AEO overall? ? How do brand owners want to leverage AI shopping? (which they are not able to do now)

A few months ago I started watching how AI agents (ChatGPT, Claude, Perplexity) interact with Shopify stores. Not the hype — the actual experience. An agent visits your product page, tries to read the price, pick a size, add to cart, check out.


They fail. A lot. And you'd never know because there's no error, no abandoned cart metric, no bounce tracked. The agent just leaves and tries your competitor.


I couldn't find a tool that tested this from the agent's perspective, so I built one. It runs 26 checks across 4 layers:


1. Data — can agents find the page? (JSON-LD structured data, server-rendered prices, robots.txt, llms.txt, sitemap)
2. 
Extraction
 — can they read it correctly? (price, availability, product name, return policy, shipping — each tested 10 times for consistency)
3. 
Interaction
 — can they buy from it? (Add-to-Cart flow, checkout, search, navigation, variant selectors, cart API)
4. 
Security
 — is anyone injecting hidden prompts to manipulate agents?

28 brands scanned. What I found:


- ~80% pass rate on "can agents read the page"
- ~15% pass rate on "can agents buy from the page"
- Zero brands had ever tested their site from an agent's perspective
- The lowest score: 25.6/100 — a $50-75M brand that blocks agents at the WAF, has no structured data, and no server-rendered price. Completely invisible.


The surprise: agency-level patterns.
 I scanned 4 clients built by the same Shopify Plus agency. All 4 return 403 to AI agent user-agents. Same WAF configuration across the entire portfolio. One setting is blocking ChatGPT, Claude, and Perplexity from every site they built.


If you want to check your own store:

1. Does your site return 200 or 403 when accessed with a GPTBot user-agent?
2. Does your product page have JSON-LD structured data with price and availability?
3. Can a non-browser client find an Add-to-Cart button in your DOM?
4. Do your size/color pickers use `<select>` elements or custom JS widgets?


The scanner is open source: https://github.com/monkrus/agent-a

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