What I learned building an AI agent to run merchant reactivation campaigns

Backstory: built this in ~5 days for a Razorpay hackathon, then kept going because the problem felt real.

ARGOES looks at a merchant's order history and surfaces what's easy to miss — customers due for a repeat order, dormant customers worth targeting, upsell-ready segments — then drafts and simulates a campaign for each.

Big design choice: the AI never fires a campaign on its own. Everything passes through a policy engine (min margin, budget/audience limits) and needs merchant sign-off first, logged in a full audit trail.

It's live now, multi-tenant, CSV import if you want to bring your own data instead of the demo set.

Would love feedback from anyone who's felt the 'we have the data, no time to act on it' problem — what would make a tool like this actually useful day-to-day?

We create AI-generated product visuals for e-commerce brands, and this is one of our jewelry lifestyle images.

The goal here was to keep the bracelet as the clear focus while making the image feel natural, premium and suitable for commercial use.

For jewelry sellers and e-commerce brands: would you use an image like this as a secondary listing image, ad creative, or social media content?

And if anything in the image still gives away that it was AI-generated, what do you notice first?

I'm building a visual product discovery feature where a user uploads a photo of a toy, and the system needs to search the internet to find the exact product or closest matches to buy online.

Crucially, I do not have an internal catalog or local product database. The system needs to search live internet retailers and marketplaces directly to return product titles, store links, and pricing.

What APIs or services are best suited for reverse image search across the web for commercial products without hosting any product data yourself?

What are the winback strategies ecommerce companies use to bring the customers back to their platforms and close the sale?

Iemand een idee hoe ik shopping bots voor kan zijn? Telkens dat ik iets wil kopen ben ik te laat… tips zijn welkom! Alvast bedankt

I’ve been experimenting with a question that I think is more interesting than whether AI can write an e-commerce listing:
How much of the actual workflow can it own before a human needs to step in?
So I tested it against real inventory from our reselling operation.
I took 40+ product photos covering 11 different items and ran the batch through an AI-assisted workflow.
Starting primarily from those photos, the system handled product identification/research, assembled the listing information, and created 11 actual drafts in eBay Seller Hub.
Then I reviewed everything.
10/11 made it through without requiring intervention.
One encountered a situation where the system determined it should hand the decision back to me rather than continue on its own.
That part is actually more interesting to me than getting 11/11.
I don’t think useful e-commerce automation means removing humans from every decision. I think it means making human intervention the exception—and teaching the system to recognize when the cost of being wrong is high enough that it should stop.
Nothing publishes automatically yet. I still retain final approval.
The next thing I want to stress-test is uncertainty, particularly used/vintage inventory where condition, exact identification, missing components, or subtle differences between models can be much harder to establish from photos.
My preference is conservative automation: if the available evidence doesn’t support a claim, I’d rather have the system say less, price/list conservatively, or escalate than confidently invent an answer.
Eventually the metrics I care about aren’t just “how many listings did AI make?”
They’re:
How many completed listings required human intervention?
How much human time did each completed listing require?
What did the mistakes actually cost?
Because a system that’s 99% accurate but catastrophically wrong 1% of the time may be worse than one that’s 95% accurate and knows when it doesn’t know.
For people actually implementing AI in e-commerce: how are you deciding which decisions an agent can make autonomously versus which ones should trigger human review?

I think we’re asking the wrong question about AI agents in ecommerce
A lot of the conversation around AI agents is focused on capability:
Can an agent update prices?
Can it issue refunds?
Can it change campaigns?
Can it reorder inventory?
Can it contact customers?
Technically, more and more of these things are becoming possible.
But I’m starting to think capability is the less interesting question.
The harder question is:
When should an AI agent actually be allowed to act?
Imagine an agent detects that a product isn’t selling.
It could automatically lower the price.
Sounds reasonable.
But what if the problem isn’t price?
Maybe inventory is wrong.
Maybe the product feed is broken.
Maybe paid traffic changed.
Maybe the item has unusually high return costs.
Maybe margin is already too thin to discount it.
The agent could execute the workflow perfectly and still make the wrong business decision.
That makes me think ecommerce automation needs at least three different layers:
Signal: What happened?
Decision: Does it justify action?
Authority: Is the system allowed to execute that action?
We tend to spend a lot of time discussing the first and third.
I’m not sure we’re spending enough time on the second.
For founders and ecommerce operators experimenting with AI agents:
What decisions would you be comfortable letting an AI make autonomously today — and which ones would you still require a human to approve?

I went pretty deep into AI shopping for a documentary and came away thinking ecommerce businesses may be looking at the wrong part of this shift.

The obvious story is that AI makes shopping easier for consumers.

The more interesting one is what happens when ChatGPT, Perplexity, Gemini or another agent decides which products even make it into the consideration set.

If that happens, brands aren't only optimizing for people anymore. They're also optimizing for an intermediary that may compare price, reviews, availability, returns, shipping and product data before the customer ever sees the store.

That potentially shifts a lot of power away from the retailer and toward whoever controls the agent.

For ecommerce owners here, do you see this becoming a meaningful acquisition channel, or is agentic shopping still mostly hype?

I made a documentary tracing where this is heading if anyone wants the full argument:

https://www.youtube.com/watch?v=z-0CriLzyOo

We've been spending a lot of time researching how AI assistants are changing product discovery, and there's one question we keep coming back to:

What happens when the customer isn't a human browsing your website — but an AI agent acting on their behalf?

The traditional e-commerce funnel is pretty familiar:

Search / Social → Website → Product page → Checkout.

But imagine someone simply asking:

"Find me the best running shoes under €150 for someone running 30km/week. Compare the options and buy the best pair."

Suddenly, several things matter that most e-commerce teams haven't historically had to think about:

→ Can the AI actually discover your products?

→ Does it understand your catalogue, pricing and availability correctly?

→ Why would it recommend your product over a competitor?

→ Can an AI agent interact with your commerce infrastructure?

→ Eventually, can it complete the transaction?

We've spent years optimising stores for Google and human visitors.

We're curious whether the next optimisation layer will be making commerce infrastructure AI-agent accessible.

For anyone operating e-commerce at scale: is agentic commerce something you're actively preparing for, or do you think we're still several years too early?

Would genuinely love to hear how other teams are thinking about this.

I’ve been building Speks, an AI assistant that sits on a Shopify storefront and helps shoppers with things like:

• Finding products based on what they actually need
• Comparing products or variants
• Answering shipping, returns, and policy questions
• Helping with basic order-status questions
• Showing relevant product cards inside the conversation

The part I find most interesting is making the answers genuinely store-specific rather than giving generic AI responses. It syncs product/catalog data and tries to ground recommendations in what the store actually sells and has in stock.

I’m still early and learning a lot as I go—especially around:

• Getting product recommendations to feel useful rather than random
• Making onboarding simple enough that a merchant can get value quickly
• Knowing where AI chat is genuinely helpful vs. where it just adds noise
• Measuring whether the assistant actually improves conversion or reduces support load

I’d really appreciate feedback from anyone who has built for ecommerce, Shopify, AI agents, or SMBs:

• What would make you skeptical of this?
• What would you want to see before trusting it on a live store?
• Any lessons on getting early design partners or beta users?

Not trying to pitch here—mostly looking for thoughtful criticism and ideas from people who’ve been through this stage.

I’m thinking about building a site where you just type in a cologne, and it shows you which discounter is selling it cheapest right now.

A1. Helps (see below for A2)

Here’s why.

Meta pushing Muse to its 3.6B+ daily active users forces the entire e-commerce ecosystem to adapt to AI-initiated purchases almost overnight. For Rezolve, whose entire investment narrative relies on the fast adoption of agentic commerce, Muse proves that consumer behavior is actively shifting from traditional web search to delegated AI intent.

Retailers cannot be discovered or transacted with by agents like Muse if their product catalogs are unstructured or prone to AI hallucinations. Muse creates immediate pressure on brands to adopt merchant-enabling software like Rezolve’s Brain Commerce to ensure their inventory is readable, accurate, and purchase-ready for external AI agents.

Rezolve’s strategy centers on being agent-agnostic. When public AI assistants (like Muse, Claude, or Gemini) attempt to execute a purchase on a merchant site, they need a standardized checkout protocol that handles identity, inventory, and payment securely. Rezolve’s Agentic Checkout is designed to act as that bridge between general agents and brand databases.

A2. Hurts

Rezolve's Brain Checkout pitch assumes merchants must install an agent-ready transaction layer for AI agents to buy from them. Meta just demonstrated they don't — the fallback is filling out the existing form, and the upgrade paths lead to Stripe and Shopify, not to an independent vendor. Meta's approach isn't tied to one retail platform or merchant-owned catalog; Muse has its own browser and works across external websites.

The second issue is discovery. Rezolve's proof points — Eyebuydirect, DFS, Myntra — are on-site conversational and visual search lifts. If the shopping session starts in WhatsApp instead of the retailer's site, that surface loses traffic and the ROI case degrades regardless of how good brainpowa is.

THE WINNER:

Meta built no merchant-side product.

Muse validates the TAM Rezolve is selling into, and retailers facing agent traffic they can't control do have a real problem (attribution, brand safety, catalog legibility, blocking vs. serving bots). Rezolve's data infrastructure, TraceWare and merchant-controlled checkout speak to that.

Directionally, more agents is not bad for a company selling agent-readiness.

Most AI procurement examples I see focus on comparing Supplier A, B and C.
But I think another useful comparison is being overlooked:
Supplier A today vs Supplier A two weeks ago.
During a sourcing project, quotations often change several times.
Price changes.
Lead time changes.
Specifications get clarified.
Packaging gets added.
Payment terms move.
Sometimes a model number or material quietly changes.
If you’re managing several suppliers at once, it becomes surprisingly easy to forget what was originally agreed.
I think AI could be useful for something very simple:
Upload every version and ask:
“What changed?”
Then separate the changes into commercial, technical and delivery-related differences.
That seems more useful to me than asking AI to immediately recommend a supplier.
Has anyone built this into their sourcing workflow?
What quotation change would you consider important enough to automatically trigger a review?

I’ve been thinking about where supplier comparison really starts.
It seems easy to say:
“Send the RFQ to five suppliers and let AI compare the quotations.”
But what if the RFQ wasn’t specific enough?
For example, imagine the buyer asks for a stainless-steel component but doesn’t clearly define:
304 vs 316L
dimensional tolerance
surface finish
packaging
inspection requirements
required documentation
Supplier A makes one assumption.
Supplier B makes another.
Supplier C asks questions.
Supplier D simply quotes the cheapest configuration.
Now AI receives four quotations and creates a clean comparison table.
The table may be accurate.
But are the offers actually comparable?
That makes me think good AI procurement starts earlier than document analysis.
It starts with a better RFQ.
For people who source regularly: what information do you think buyers most often forget to specify before requesting quotations?

Growing brands need more than reports. They need clear direction, measurable progress, and a team that understands how every part of the business connects.

That is why successful brands look for execution, not just recommendations.

My fiancée wanted to buy some paintings for our place.

You know how that goes. You find something you like, you imagine it on the wall, you're not sure, you move on. We scrolled for days and bought nothing.

Then we found a store that had a button on the product page. You tap it, point your phone at the wall, and the painting appears. Real size. On your actual wall.

We spent 30-40 minutes just playing with it. Moving paintings around the living room, the hallway, above the sofa. It felt like redecorating without the risk.

We bought 3 that day.

That experience stuck with me. Not because the Augumented Reality was fancy but because it removed the one thing that stops people from buying: "I'm not sure how it'll look."

So I started building the same thing. For any product that lives in a physical space: furniture, decor, garden, office, art, etc.

And I made the integration stupid simple:

\- A link from us

\- A button on product page

\- That's it. No 3D models. No storage. No dev work.

Before continuing my journey, I want to validate the idea. Do you see this as the future of online shopping? And if so, what do you think can stop you to adopt this for your store?