Practical guides to help shoppers compare products and find the right fit.
A shopper in Manchester and a shopper in Munich can ask for the same kettle and need different answers. This guide covers market codes, local-currency prices, shipping origin versus delivery destination, and how to compare prices fairly when your product search spans more than one country.
A fair product comparison uses the same attributes, the same market, dated prices and matching variants, then shows trade-offs instead of naming a single winner. Rules and a table layout for shopping assistant comparisons.
Four practical ways shoppers use a photo to find products: find this exact item, find something similar for less, identify a replacement part, and match a style. With input tips, confidence handling and when to add a text query.
Shoppers are starting to ask AI assistants what to buy, and those assistants can only recommend what they can understand. If you sell on Shopify, a few fields decide whether your products are shown accurately: titles, variants, barcodes, product type, specifications in metafields, images and availability. Here is what to check in each.
"A quiet dishwasher for a small flat under £400" holds a budget, a size limit, a noise preference and a few unspoken needs. Here is how to split a shopper's sentence into hard and soft constraints, handle units and exclusions, and know when to ask a follow-up question.
A simple way to estimate product-search costs before you build: count calls per conversation, multiply by expected volume, then cut waste with caching and better first queries. Includes a worked example using Simlir's live credit prices.
AI shopping assistants can only describe your products as well as your product pages describe them. A practical guide for UK ecommerce teams on identifiers, specifications, structured data, clear prices and stock, crawlable pages and consistent naming.
A shopper asks for size 9 trainers in white and gets a link to the black size 7. The product was right; the variant was wrong. This guide covers parent products and variants, variant attributes, the GTIN edge cases that trip up matching, and how to link to the exact variant they asked for.
Language models fill gaps with plausible specs. How to ground shopping answers in retrieved product data: answer rules, citing fields, declining claims you cannot verify, and test prompts that catch invented details.
Two retailers list the same headphones with different titles, photos and descriptions. How do you know they are the same product? This guide explains GTINs, MPNs and SKUs, how check digits work, a sensible matching order, and the pitfalls that lead to wrong price comparisons.
Agentic commerce is shopping where an AI assistant does part of the work for the shopper: understanding the need, finding products, comparing them and handing over to a retailer. What it is, how it works, and what product data it depends on.
A step-by-step guide to giving a shopping assistant structured product search over MCP: connecting a hosted server, choosing the right tool, setting answer rules and handling empty or uncertain results.
A practical framework for evaluating product-search results: representative shopper queries, evidence checks, provenance and deliberate failure tests.
What AI search systems can read from public retailer pages, and how crawlability, structured data, internal links and stable identity support accurate discovery.
A ten-part audit for retail catalogues: identifiers, variants, attributes, specifications, descriptions, images, price, availability, links and market scope.
Image product search is only useful when the result is more than a visual match. The stages from photo to structured candidates, and how to handle confidence and ambiguity.
Keyword search matches terms. Natural language search preserves the shopper
Field by field: the identity, specification, media, review and price fields a product response needs for retrieval, comparison and explanation.
A price without its currency, retailer and checked time cannot be explained. How to model price snapshots honestly, and what to do with stale or missing values.
MCP suits tool use inside an agent workflow. REST suits direct service integration. A decision table, auth notes and an implementation checklist for choosing between them.
The catalogue fields shopping assistants rely on: stable identifiers, comparable specs, price and availability context, images and canonical links. With a readiness checklist.
A seven-stage architecture for shopping assistant search: intent, retrieval, structured records, provenance, comparison, MCP or REST access, and failure testing.
A semantic product database retrieves products by meaning and intent, returning structured records an application can inspect, compare and explain.