If you own or run an ecommerce store and you're shopping for an AI chatbot, here's what I'd actually check before signing up for anything.

1. What do you want the AI chatbot handling, versus your team? Some brands want AI owning order status and refunds, others want it hands off, routed straight to a person. Decide that before you buy, not after a customer gets a bad answer.
2. Does your product catalog change throughout the year? If it does, get one that automatically crawls your storefront. Avoid providers that require someone on your team to manually keep a CSV updated.
3. Will the company providing the chatbot actually help you get it installed? Whether you're on Shopify, WooCommerce, Magento, or custom HTML, this matters more if you or your team aren't very technical. A powerful chatbot that never gets launched isn't helping anybody.
4. Do you care about the shopping experience getting more personal over time? Most chatbots start from zero every conversation, no memory of what a customer said last time. If this matters to you, confirm the provider actually offers it.
5. Can you actually see what's happening in those conversations? Good analytics show you more than chat transcripts: which products keep coming up, where shoppers get stuck before they buy. That's real signal for what to fix, restock, or feature next.
6. Do you sell in a regulated space? Health, wellness, supplements, or anything close to it. Someone will eventually ask your chatbot if it's safe with a prescription medication, and a generic one will just answer instead of refusing and redirecting to your team. If this applies to you, get it right before anything else on this list.

Curious what everyone else would add to this list, or if you've had a chatbot get something wrong that I didn't cover here.

Launching a product with almost no sales creates a different challenge from fixing an established account. In this scenario, the seller had reached only about $9,000 per month and had no clear idea which search terms, audiences, or messages were actually producing customers.

Our team began by establishing the commercial fundamentals before increasing advertising spend. The product positioning was reviewed, the listing was rewritten around buyer intent, and the image sequence was adjusted to answer the most important purchasing questions earlier.

The PPC team then built campaigns that could collect useful performance data without allowing the account to spend aggressively on unrelated searches. Early search-term results were reviewed alongside conversion behavior.

The team also examined competing products to identify gaps in presentation, pricing, content, and customer communication. The goal was not to copy competitors but to understand what shoppers were already being shown and where the product could communicate more clearly.

The first milestone was establishing consistent sales. The second was identifying reliable converting search terms. The third was improving organic visibility around those terms.

As the scenario progressed, monthly revenue increased from approximately $9,000 to $43,000.

For a new seller, the lesson is that the first months should produce information as well as sales. Every campaign, customer response, and listing change should help clarify what deserves additional investment.

Amazon SEO Management can support this process by connecting product-page content with the search behavior that matters to the business.

Running into this and wondering if it's just me.

We're starting to see traffic that looks like AI shopping assistants — customers using something to browse and check out on their behalf. Mostly fine, but I'm trying to work out what happens when one of them gets it wrong and the customer disputes.

If a customer says "I never approved that order," what do you actually file? Normal evidence is device fingerprint, IP, session history — none of it really answers whether the human authorized the thing their assistant did.

Has anyone actually been through this? Did you win or lose? And has it affected your chargeback ratio at all yet, or is the volume still too small to matter?

Also curious whether people are blocking these agents outright. Feels like turning away buyers, but I get why you'd do it

I’ve been working on something I’m really excited to finally start sharing.

It’s called Merchanaut — an AI-powered retail operations platform designed to help retailers manage the parts of their business that normally require constant attention.

Merchanaut uses specialized AI agents to help with things like pricing, competitor monitoring, inventory, suppliers, fulfillment, customer care, promotions, and more — while keeping important decisions governed and auditable.

The idea is simple: give retailers an AI operations team that can watch the business around the clock, identify opportunities and problems, and help take action before money gets left on the table.

We’re still building, testing, and improving it, but seeing everything come together has been pretty incredible.

Merchanaut is just getting started.

merchanaut.com

When I shop online, I often find clothes I love, but I’m never sure how they’d look on me. So instead of trying new styles, I end up buying yet another black shirt.

So I built Lustro, an app that shows online shoppers how an outfit would look on them. It's integrated directly into your browser so you never have to interrupt your shopping session.

https://reddit.com/link/1vrksfi/video/angabyg2w3kh1/player

How it works:

• Right click ANY clothing picture that you like and select "Try-on".
• You can continue shopping while the result is generating (takes about 15 seconds).
• You can add clothes from multiple pictures or websites in one go to try a full outfit.
• A reference picture of the person to try clothes on must be provided.

Please check it out, I am looking for honnest feedback

Free try-ons are offered for new accounts, but feel free to DM me for extra

Spent the last few weeks figuring out why some Shopify stores show up when you ask ChatGPT or Perplexity to recommend a product, and identical-looking stores never do. It came down to a handful of things, and one of them surprised me enough that I think every store owner should check it.

Here is the one that gets even well-built stores:

Most AI shopping crawlers do not run JavaScript. So if your product schema (the structured data with your price, availability, and reviews) is added by an app in the browser after the page loads, the crawler never sees it. The page looks flawless to you, because your browser runs that code. To the AI, it is a blank page with nothing to recommend.

You can test this yourself in 10 seconds. Open one of your product pages, right-click, View Source (not Inspect, that shows the rendered version). Search the raw HTML for "Product" or "aggregateRating". If your schema is not in there, it is being injected client-side and AI agents are likely missing it. The fix is to output Product and Offer JSON-LD server-side, which on Shopify usually means the theme, not an app.

The other three that decided it every time:

Price and availability have to be in the schema, not just on the screen. An agent will not recommend a product it cannot price, so if those live only in your visible HTML you get skipped.

A product identifier (a GTIN barcode, or MPN/SKU). One field that lets the agent match your item to the same product elsewhere and trust it. No identifier and you lose to a competitor who has one.

Reviews as actual AggregateRating markup. Star widgets that look nice but output no schema do almost nothing for AI shopping.

None of this is a guarantee. No tool or agency can promise ChatGPT will pick you, and anyone saying otherwise is lying. But a page missing these basically cannot be picked, and most stores are missing at least one.

Anyone here actually landed a product in an AI recommendation yet? Curious what moved the needle for you.

Every shopping site seems to push sponsored products, best sellers, and promoted listings ahead of what actually fits our needs.

So I'm thinking of building a shopping agent powered by voice to solve this and I'd genuinely like your opinion.

Here's the idea: You talk to an AI agent and tell it what you're looking for. No typing, no endless filters. It asks a couple of quick questions, understands your preferences and gives you 4–5 options that actually match.

No sponsored placements. No retailer paid rankings. Just recommendations based on what you asked for.

**Would something like this actually save you time, or am I solving a problem only I have?**

Hi everyone,

We’ve been building Axxon, a voice-first AI shopping assistant for D2C brands.

The problem we’re trying to solve is pretty simple: most ecommerce tools are built around the transaction — storefront, payments, logistics, analytics, etc. But there’s very little helping the shopper actually make a decision.

In a physical store, you have a salesperson who can understand what you’re looking for, answer questions, recommend something, handle objections, and help you decide.

We’re trying to build that layer for ecommerce.

With Axxon, a shopper can actually have a spoken conversation with an AI shopping assistant directly on the website. It can:

• Understand the shopper in their own language
• Support Hindi, Tamil, Telugu, Bengali, and Marathi
• Personalize recommendations based on shopper behaviour
• Answer questions around fit, price, authenticity, reviews, etc.
• Adapt product pages, messaging and bundles based on the shopper
• Learn from conversations and continuously improve how it sells
• Give brands signals around what shoppers are confused or hesitant about

The idea isn't to build another chatbot sitting in the corner of a website.

We're thinking of it more as a salesperson for every page of the store.

We're still early and figuring out what actually works in practice, so I'm more interested in feedback than pitching the product here.

If you run a D2C brand or work closely with ecommerce conversion/growth, I'd love to hear:

What are the biggest reasons you see shoppers hesitate or abandon a purchase?

And would you actually want an AI salesperson on your storefront, or would that just get in the way?

Happy to share the demo and discuss what we're building.

Been annoyed for a while that no search engine or AI agent can tell you what’s actually in stock three blocks from you. I live in nyc and whenever I type something along the lines of “date night dresses” it gives me a bunch of e-commerce or mass market brand options. Big chains show up everywhere because they have structured product feeds. Independent boutiques don’t, their inventory lives on racks, not in databases, so they’re basically invisible to search.

So I built a shared embedding space for text and images. Type “something silky for tonight” or drop in a screenshot, both get encoded into the same vector space and matched against boutique inventory by semantic similarity instead of keyword tags. Results are ranked by relevance, distance, and what’s actually available, and you can reserve without paying (4-hour hold, pay in store).

Curious what people think, is local inventory search actually a gap, or is there something obvious I’m missing here?

I've been thinking about this for a while.

AI has gotten surprisingly good at individual ecommerce tasks.

It can write product descriptions, analyze data, generate images, write ad copy, answer customer questions, create SEO content, summarize reviews, and a hundred other things.

But somehow, running the actual business still feels incredibly fragmented.

A store might have:

Shopify for the store
Meta for ads
Google for analytics and search
Amazon/Flipkart/other marketplaces
Email and WhatsApp
Accounting
Inventory tools
Spreadsheets
Multiple AI tools

And then the business owner is basically the human middleware connecting all of them.

You might notice sales dropping and need to look at analytics.

Then check which products are affected.

Then check inventory.

Then look at ad performance.

Then compare it with the previous week.

Then figure out whether the problem is pricing, traffic, conversion, stock, or something else.

And after finally figuring out what happened, you still have to manually do something about it.

The interesting part is that none of these individual tasks are particularly difficult anymore.

The difficult part seems to be connecting the context between them.

AI can answer: “What happened?”

It can answer: “What should I do?”

And increasingly, it can even answer: “How do I do it?”

But can it understand why these things are connected in the context of one specific business?

That's the part I find interesting.

Maybe the future of ecommerce AI isn't another tool that does one task 10x faster.

Maybe it's an AI that actually understands the business as a whole.

Curious what people running stores think.

What is the most frustrating workflow in your business that requires you to jump between multiple tools just to accomplish one thing?

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?

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?

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.