Shop in your own words.
Customers describe a meal, a budget or a gift, not a product ID. A catalog search alone misses that context.
Solution / Chat2Shop
From a question to a basket.
A multilingual agentic storefront connected to the retailer's catalog. Help customers find products, build recipe baskets and discover useful pairings, in their own words.
Discuss your storefront See how it works
A search box cannot be a salesperson.
The same store, more languages.
The retailer wanted to reach multilingual customers beyond its existing sales channels.
Make the next choice useful.
A bottle of wine can lead to a cheese pairing. A dinner idea can lead to its ingredients, without leaving product discovery behind.
Understand. Match. Recommend. Continue.
Understand
Read what the customer needs: language, occasion, preferences and budget.
Ground
Connect the request to the retailer's catalog, product IDs and SKUs.
Recommend
Show matching products, recipes and useful pairings in the conversation.
Continue
Build the shopping list and support the route to checkout or a human handoff.
Shopper intent
Language + context
Conversational storefront
Chat2Shop
Retailer catalog
Product IDs + SKUs
Cart
A conversation that knows the catalog.
Start with intent, not filters.
Ask for a gift, a pairing or a dinner idea. The storefront turns a broad request into products the retailer actually carries.
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.
The recipe is a shopping entry point. Ingredients map to retailer SKUs, so a suggestion can become a basket rather than a generic answer.
Catalog grounding
Recipe
Ingredients + portions
Catalog
Matching product IDs + SKUs
Basket
Customer-selected products
Pairings, not random extras.
The brief includes relevant upsell: wine and cheese, a cocktail and its ingredients, or a dish and a pairing. The product relationship gives the suggestion a reason.
Pairing logic / illustrative
Wine
Cheese
Cocktail
Ingredients
Dinner
Recipe basket
The storefront is the core. Channels are the reach.
The client brief brings web shopping and messaging channels into the same sales idea. Telegram, LINE and WhatsApp are named in the channel strategy, with Chatwoot connecting conversations. Channel integrations and checkout scope are confirmed for each rollout.
Web storefront
The public shopping assistant shows natural-language prompts, product categories and a shopping list.
Messaging strategy
Telegram reaches an important customer group in the brief. LINE and WhatsApp extend the same multilingual storefront direction.
Human support
The system includes escalation so a person can take over when the agent needs help.
Built for one retailer. Available to others.
Built for an unnamed grocery retailer. The case shows what the system does, without claiming measured revenue uplift.
Case file / Grocery retailer
Built for a client
- Offering
- Chat2Shop
- Purpose
- Multilingual product discovery, SKU-grounded recipes and relevant upsell.
- Evidence
- Product workflow and reviewed interface; client unnamed
What we show
Multilingual product discovery, SKU-grounded recipes and relevant upsell.
What we do not claim
We do not present every messaging channel as a verified live integration. No verified conversion or sales uplift dataset is published here.
Evidence: product brief and interface review, October 2026. Client identity and operational data withheld.
Tell us what your customers need.
Start with your catalog, existing systems and customer journey. We scope the workflow and integrations around the retailer, not a generic demo.
Discuss your storefrontOr write to founders@generalmachines.ai
Explore the other retail systems: On-prem Planogram · ShopBanner