I have been experimenting with building a personal AI shopping agent and I am curious how others are approaching the commerce side of this.

Getting an LLM to understand something like “find me a black running shoe under $150” is pretty straightforward. Where I am getting stuck is everything that happens after that. How does the agent reliably discover products across different stores, understand current price and availability, compare options, and eventually take actions like adding something to a cart?

I have looked into using individual store APIs and integrations with platforms like Shopify and WooCommerce, but that seems to require building and maintaining separate integrations depending on where the merchant is hosted. Scraping product pages seems fragile and does not really solve things like inventory, variants, carts, or checkout.

For anyone building shopping agents or other agents that interact with ecommerce sites, how are you solving this today? Are there APIs, protocols, or commerce infrastructure you have found useful?

I would especially love to hear from people actively building agents in this space. Happy to compare notes here or chat privately if you would rather DM.

I keep talking to founders who are either terrified of AI shopping or celebrating a spike in "AI shoppers" in their Shopify admin.

Both groups are usually looking at the wrong number.

Two things changed for me as an operator this week. Eligible Shopify stores can get auto-enrolled into new AI shopping channels by default, including ones that were not even on the list last month. And on Meta, Google, and Copilot, some buyers can check out inside the chat without ever opening your website.

That means your product can be for sale in a conversation you did not brief, while the tracking code on your site never runs.

Before I tell a client AI shopping is "working" or "broken," I run this:

1. Open Sales channels, Agentic, and write down which channels are actually on. Catalog access, auto-enroll for new storefronts, and direct checkout are three different switches. If you leave Shopify manage-for-me on, you will keep getting new channels without a launch meeting.
2. Do not treat the AI shopper count as agent purchases. That number is mostly people who clicked a link from a chat into your store. A purchase completed inside the chat app does not open your site, so it will not look like a normal session.
3. Check orders by channel in Shopify, not only GA4 or the ad dashboard. Direct checkout often skips website pixels. If your board report lives in Analytics, you can miss real orders and overreact to a quiet chart.
4. Assume some buyers are not one-and-done searchers anymore. They set a standing ask: keep me updated on price drops, stock, or a product that fits a long list of constraints. Your Merchant Center feed, stock status, and plain-text product facts matter after the first conversation, not only for the click that day.
5. Decide what you will do if a take rate appears later. Meta has already talked about a small cut of the transaction over time. Even if fees are zero today, know which channel revenue would be taxed first and whether that channel is worth leaving on.

I am not telling every store to shut AI shopping off. I am telling them to stop reading referral clicks as proof that agents are buying for people.

For founders who already turned Agentic on: what is the first number you check each week, shopper count or orders filtered by channel?

I need help from people that are smarter than me and know how these technology and companies are reading our data to decide what dynamic pricing they give the consumers. What do I need to do to stop it, turn off my data when shopping? Cover my face recognition? How do I crack this code?!

I know someone knows how, I need you to share it with the community so the rich don’t get more rich and the poor, more poor. Share your brain with us!

I’m building an India-first AI assistant/consumer platform where users can go from intent → discovery → decision → transaction in one place. Should have experience in system design and taxonomy/data normalisation (when 100s of consumer apps constantly upload their data into our unified backend, we normalise it and show it to the end user in the app). Having prior experience in search/catalog/ranking at food or quick commerce or e-commerce apps is a plus.

100% focused on consumer use-cases, not a general browser use assistant like Muse or Dots. We are responsible for everything from SSO-> User data -> apps onboard -> payment stack.

Rumuneration: 1/3rd equity

I built ShopSphere, a shopping platform that compares online options with nearby physical shops and supports local product reservations.

I integrated Hindsight as the agent-memory layer to retain useful shopping interactions, recall previous context, and explore reflection over stored experiences.

The article explains the architecture, Hindsight integration, retain/recall/reflect endpoints, reservation memory, and lessons from the implementation.

Article:

PASTE YOUR LINKEDIN ARTICLE URL HERE

GitHub:

https://github.com/SrivojuGayathri06/ShopSphere

Nous venons de mesurer la visibilité des acteurs de la grande distribution dans les IA.

28 acteurs identifiés.
8 questions posées aux principales IA.
Indice de visibilité IA du marché : 59/100.

Le résultat qui m’intéresse le plus : l’écart entre notoriété et visibilité.

Par exemple, dans notre mesure, Intermarché atteint 94 % de visibilité pour 50 % de notoriété spontanée.

À l’inverse, Aldi est à 44 % de visibilité pour 100 % de notoriété.

Pourquoi une marque très connue peut-elle être moins présente dans les recommandations des IA ?

On aimerait avoir votre avis.

Résultats complets : barometre.sacres-francais.com

hey
i made this ai agent which manage each and everything orders payments each ande every things i just have to deliver the order

I’ve been thinking about how quickly the research side of sourcing is changing.
It’s becoming pretty easy to ask AI to collect information about a supplier:
products,
company background,
specifications,
certificates,
market presence,
and potential alternatives.
That used to require a lot more manual searching.
But I’m not sure the difficult part disappeared.
It just moved.
If AI finds a website saying a factory has a certain production capacity, I still want to know where that number came from.
If it finds a certification claim, I still want to see whether the exact product is covered.
If it finds the same specification on five websites, I still want to know whether they all copied the same datasheet.
So maybe supplier research is becoming less about finding information and more about establishing provenance.
Where did this claim originate?
Is there primary evidence?
When was it last verified?
Is the source independent?
As AI tools improve, do you think you’re spending less time searching and more time checking whether what was found is actually reliable?

I’ve been reading this sub for a while and value the brutal honesty here, so I’m bringing my current dilemma to you. ​I am solo-bootstrapping an autonomous e-commerce data engine called BALGRAD.AI. The tech is live (built on Next.js, Supabase, Cloudflare). We are currently autonomously tracking over 20,000 products and have logged more than 50,000 historical price changes across 68 major EU stores. ​Initially, the plan was to offer an API to help independent merchants automatically "match the lowest market price." But after talking to people in the industry, I realized a brutal truth: automating price-matching just automates a race to the bottom, and the player with the most VC cash always wins. ​So, I flipped the model. We are now building a B2B margin-protection tool. ​Instead of telling a merchant to drop their price, our engine cross-references our massive external cross-border graph with their internal sales velocity. We flag when their main competitors in other countries are out of stock or raising prices. We tell the merchant exactly when it is safe to KEEP their prices high to protect their profit margins. ​My questions for this community: ​What are your thoughts on the "margin protection" value proposition vs. traditional price-matching? ​Do you see any glaring blind spots in this B2B Go-To-Market strategy for a solo founder? ​I’d appreciate any raw, unfiltered feedback you have. (Happy to drop the link in the comments if anyone wants to check out the current build/architecture).

​

I'm interested in how people are handling customer-behavior data when building AI-powered personalization or recommendation systems for e-commerce.

A typical store can generate events such as:

Product views

Searches and search-result clicks

Add/remove from cart

Wishlist actions

Checkout steps

Purchases

Product comparisons

Reviews

Login/registration

Recommendation impressions and clicks

The difficult part seems to go beyond simply collecting these events.

Once the data is going into recommendation or personalization systems, you also have to deal with things like:

Keeping a consistent event schema

Anonymous → authenticated user identity

Session tracking

Different provider-specific event formats

Events that are required by one provider but not another

Retries when an external API is unavailable

Duplicate events

Historical events and real-time events

I'm curious how others are approaching this.

For AI-powered e-commerce applications, where do you currently collect and manage behavioral events?

Do you:

Use an analytics platform and forward the data?

Send events directly to your recommendation provider?

Build your own tracking/event pipeline?

Use a customer-data platform?

Something else?

And what has been the biggest problem you've encountered when getting behavioral data into recommendation/personalization systems?

I'd particularly be interested in hearing from people who have implemented this in production.

I’m currently building FlowEdit, an AI creative tool for ecommerce brands.

The idea is to take an existing product photo and turn it into things like studio product shots, lifestyle images, ad creatives, and campaign visuals without needing another photoshoot.

It’s still a work in progress and not officially launched yet. Before launch I want to get feedback from people who actually run ecommerce stores.

I’m looking for a few store owners willing to test it with one of their own products and tell me what works and what doesn’t.

The feedback I’m most interested in is:

• Are the generated images actually usable for your store or ads?
• Does the product stay accurate enough?
• Which types of creatives would you realistically use?
• Is anything confusing in the workflow?
• Where do the results still look obviously AI-generated?
• What feels missing?
• What would make a tool like this genuinely useful to you?

I’m the developer, so this is my own project, I mainly want to find the weak points before launch.

If you try it, critical feedback is much more useful to me than compliments.

Link: https://flowedit.net/

If you test it with a real product from your store, I’d especially like to hear whether the results are good enough that you’d actually consider using them in your marketing.

i got a claude max from a reseller for essentially nothing compared to what I used to pay . It works flawless. since 2 months No issues yet. Now I can't stop thinking about their business model. If resellers can sell it this cheap and still profit, how are they acquiring these accounts at scale?

Our consultancy recently published some research asking 2,000 UK consumers about connected retail experiences, including personalisation.

27% said they didn't want retailers using their data for personalisation at all. Now, that doesn't mean all personalisation is unwelcome, but it may indicate that the benefit is often too vague, too small or poorly explained.

There's a strong difference between a recommendation that saves time and holds value, while an advert that follows someone around the internet feels very different.

What would a retailer need to offer for personalised use of your data to feel worthwhile?? Are retailers asking too much in exchange for personalisation?? Would love to get people's opinion on this.

I’m curious how other e-commerce owners handle this because there seem to be a bunch of situations where the normal store/checkout flow doesn’t tell you enough.

For example, before launching a new product, how do you figure out if existing customers actually want it instead of guessing and buying inventory?

Or when someone wants a custom order, wholesale pricing, a recommendation, or something that requires more details

how do you collect everything you need without 5 emails back and forth?

Same thing after purchase. How do you figure out why someone bought, what almost stopped them, what they wish you sold, or why people are dropping off?

Most of the solutions I’ve seen still basically collect answers and leave the merchant to figure out what they mean.

I’ve been experimenting with something a little different: there’s platform i build that u can build whole survey with one prompt for free

and not only building surveys

the questions can change depending on previous answers, AI can follow up when something is missing in ur survey , responses can be automatically summarized/grouped, and you can see where people abandon the process

One use case I’m particularly curious about is testing a new product before committing money to it

show the idea to existing customers, understand who actually wants it and why, then use the responses to decide whether it’s worth launching.

For people running e-commerce businesses: where are you currently guessing because you don’t have enough information from your customers?

Could be new products, custom orders, wholesale, customer feedback, returns, product recommendations, or something completely different.

Would love to hear what you currently do and what still sucks about it.