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AI Product Recommendation Agent Development
The right product, with the reason why
An agent that helps a shopper choose.
An agent that helps a shopper choose. It asks what they need, matches the answer to your actual catalogue and stock, and explains in plain words why each suggestion fits, using only product facts you approved.
A large catalogue is hard to shop. Filters assume the customer knows the vocabulary, and "customers also bought" recommends what is popular, not what suits. In a shop, a good assistant asks two questions and narrows forty products to three. Online that conversation rarely happens, and the customer leaves with the wrong item or with nothing.
This page describes a workflow we propose to build as a custom project. It does not exist as software yet. It would reuse the question-and-answer logic and the catalogue lookup that run in our conversation demonstrations on test data. Matching needs to product attributes, and explaining the match, are not built.
One Case, Start to Finish
An example of one case, in 6 steps. The same steps play in the picture at the top of this page.
Shopper asks
"I need running shoes for wet trails, wide fit" arrives in the shop's chat.
Needs narrowed
Two questions follow, about usual distance and whether they prefer more cushioning or more grip.
Catalogue filtered
Products without a wide fit or a trail sole are ruled out, and the rest are ranked.
Stock checked
One strong match is unavailable in the shopper's size and is left out, with a note saying so.
Options explained
Three options are shown, each with the reason it fits and how it differs from the others.
Choice completed
The chosen pair goes into the cart, and the conversation is saved with the order.
Six jobs it carries through
Needs questions
Finds out what the shopper actually needs.
- Asks a few questions you define per category
- Accepts answers in the shopper's words
- Skips what was already said
- Stops when the choice is narrow enough
Catalogue matching
Matches needs to product attributes.
- Uses your structured product data
- Filters on hard requirements first
- Ranks on stated preferences
- Shows what ruled a product out
Stock and price check
Checks availability before suggesting.
- Reads live stock for the variant
- Reads the current price
- Leaves out what cannot be bought
- Says when the best match is unavailable
Explained options
Says why each option fits.
- Two or three options, not ten
- The reason for each, from product facts
- The trade-off between them
- A link to each product page
Compatibility check
Checks fit with what the customer owns.
- Asks for the model or size
- Uses your compatibility data
- Says when compatibility is unknown
- Warns before an incompatible choice
Hand to checkout or a person
Completes the choice or passes it on.
- Adds the chosen item to the cart
- Passes complex cases to a specialist
- Records what was asked and recommended
- Notes questions the catalogue could not answer
Where the Time Goes Now
Filters in the shop's language
The customer does not know whether they need the 40 or the 60 series.
Popular is not suitable
The best seller is recommended to someone it will not fit.
Out of stock after the choice
The customer decides, then learns it is unavailable in their size.
Claims the product page does not make
An assistant promises a feature the manufacturer never stated.
Compatibility mistakes
The accessory does not fit the model the customer already owns.
Returns that were predictable
The wrong item is bought, used once and sent back.
What We Need to Scope This
- The process as it runs today, in a few lines.
- The systems it touches, for example catalogue, stock and price, product knowledge, where shoppers ask.
- Rough monthly volume, and who approves what.
What It Reads and Writes
The systems named here are the kind this agent works with. Which ones it connects to in your case is settled in the scope.
Catalogue
- Shopify
- WooCommerce
- Magento
- or your product information system
Stock and price
- Live availability and pricing from your shop or ERP
Product knowledge
- Specifications
- size guides
- compatibility tables
Where shoppers ask
- Website chat
Cart
- Your shop's cart and checkout
Specialists
- A queue for questions that need a person
Recommendations Come From Your Catalogue, Not From Guesswork
The agent can only suggest products that exist in your catalogue and are in stock, and can only describe them with facts from your product data. If the data does not say a jacket is waterproof, the agent does not say it either.
It does not set prices, apply discounts or make promises about delivery. Health, safety and fit-for-purpose questions that need expertise go to a person.
- Only products in your catalogue
- Stock and price read live
- Statements from approved product data
- Unknown compatibility is stated
- No discounts or price changes
- Specialist questions go to a person
What Buyers Ask First
How is this different from "customers also bought" recommendations?
Those are statistical: they show what other people bought. This agent asks the shopper what they need and matches it to product attributes, then explains the match. It works for a first-time visitor with no history, and it can say why.
How is this different from the customer support agent?
Support resolves a request about something already bought. This agent works before the purchase and its job is choice: narrowing the catalogue to a few suitable products. They can share the same chat window and hand over to each other.
What does our product data need to look like?
Structured attributes are what make the matching honest: sizes, materials, compatibility, intended use. Where the data is thin, the first part of a project is improving it for the categories that matter most. The agent also reports the questions the catalogue could not answer.
What exists today?
Not as software yet. This page describes a proposed workflow. It would reuse the question-and-answer logic and the catalogue lookup that run in our conversation demonstrations on test data. Matching needs to product attributes, and explaining the match, are not built. A project starts by mapping your process, then proving the first steps on test data before anything touches a live system.
Start With One Category That Is Hard to Shop
Pick the one where customers ask the most questions or return the most. We will map the questions a good shop assistant would ask.