Shop in your own words.
People shop for a dinner, an occasion or a budget, not a product ID. A search box cannot hear the difference. A good salesperson can.
Solution / Chat2Shop
A salesperson
A salesperson
for every shopper.
An agentic storefront for any store that sells online. It knows your brand, speaks in your voice and knows every SKU in depth, so each shopper is served the way a well-trained salesperson would serve them.
Request a walkthrough
A search box cannot be a salesperson.
One store, every language.
Your customers do not all shop in one language. The same assistant answers each of them from the same catalog.
Make the next choice useful.
A jar of curry paste should lead to coconut milk and jasmine rice. Good suggestions follow the meal the shopper is cooking.
Understand. Match.
Recommend. Continue.
Understand
Read what the shopper wants: the dish, the language, the budget.
Ground
Match it to products you actually stock, down to the SKU, from an enriched catalog.
Recommend
Show those products, a recipe and sensible pairings, right in the chat.
Continue
Build the list, then hand off to checkout, or to a person.
Shopper intent
Language + context
Conversational storefront
Chat2Shop
Retailer catalog
Product IDs + SKUs
Cart
A conversation that knows the catalog.
RETAILERShopping AssistantIllustrative catalog2
Something to go with green curry
Why these
Coconut milk, jasmine rice and fresh herbs, best match first.
From the pantry and fresh aisles.
Added Coconut milk 400 ml to your list.
Added Jasmine rice 2 kg to your list.
Ask me anything...
Pantry ×+ Category
10 matches, best first
Coconut milk 400 ml฿39Thailand · 400 ml✓ In list
Jasmine rice 2 kg฿129Thailand · 2 kg✓ In list
Green curry paste฿45Thailand · 114 g+ Add to list
Thai basil bunch฿25Fresh · 50 g+ Add to list
Limes฿49Fresh · 500 g+ Add to list
Chicken thigh fillet฿89Chilled · 500 g+ Add to list
Fish sauce 700 ml฿59Thailand · 700 ml+ Add to list
Shallots฿35Fresh · 250 g+ Add to list
Thai aubergine฿29Fresh · 300 g+ Add to list
Palm sugar฿42Thailand · 200 g+ Add to list
My list · 2
Coconut milk 400 ml฿39
Jasmine rice 2 kg฿129
Total฿168
Checkout
Start with what the shopper asked for.
A shopper asks for a gift, a pairing or tonight's dinner. The answer is products from your own shelves.
Illustrative conversation / not a customer record
Customer / illustrative I'm making dinner. What pairs with green curry?
Storefront workflow Read the dish and preferences. Match products from the catalog. Suggest a pairing and let the customer choose.
Recipes connect to real products.
Every ingredient in a recipe maps to an item you sell. So a recipe ends in a basket as well as an answer.
Catalog grounding
Recipe
Ingredients + portions
Catalog
Matching product IDs + SKUs
Basket
Customer-selected products
Pairings that fit the basket.
Suggestions follow the meal: a curry and its sides, a breakfast and its staples. Each one comes with a reason the shopper can see.
Pairing logic / illustrative
Green curry
Coconut milk
Breakfast
Pantry staples
Dinner
Recipe basket
The storefront is the core.
Channels are the reach.
Start on your website. Then take the same assistant to the chat apps your customers already use, like LINE, WhatsApp and Telegram. Which channels go live, and how checkout works, is scoped with you.
Web storefront
A shopping assistant on your site, with suggested prompts, categories and a running list.
Chat apps
LINE, WhatsApp and Telegram can carry the same assistant and catalog. We plan the rollout around where your customers are.
Human support
When a question needs a person, the conversation hands over to your team.
Built for one retailer.
Available to others.
We built Chat2Shop with a grocery retailer in Southeast Asia.
Here is what that work shows, and what it does not.
Case file / Grocery retailer
Built for a client
- Offering
- Chat2Shop
- Purpose
- Product discovery in several languages, recipes built from real SKUs, and pairings with a reason.
- Evidence
- Product workflow and reviewed interface; client unnamed
What we show
The working flow: a question, matched products, a recipe basket, a list and a handoff.
What we do not claim
Sales or conversion results. And not every chat app shown here is live yet.
Evidence: product brief and interface review, October 2026. Client identity and operational data withheld.
Built on a grocery shelf.
Made for any catalog.
Chat2Shop runs on the same enriched catalog, brand voice and themes as Brandloom. Data, creative and conversation come from one source, so the agent sells the way your store already speaks.
Grocery: a recipe becomes a basket.
A shopper asks what to cook tonight. The agent suggests a recipe and fills the list with the items it needs from your shelves.
Fashion: an occasion becomes an outfit.
A shopper has a wedding in June. The agent asks a few questions and suggests the suit, the shirt and the shoes that fit it.
Any store: a question gets a real answer.
Home, beauty, electronics or services. If you can describe what you sell, the agent can explain it, compare it and suggest the next thing.
Tell us what
your customers need.
Tell us about your catalog, your channels and how customers shop today. We scope the assistant around your store, not a generic demo.
Request a walkthroughNo form, no sales queue.Write to the founders directly. Your email goes straight to them.Machine Commerce
ToThe foundersfounders@generalmachines.aiGeneral Machines