Compare products across retailers
Ask for a product, pack size or category and return matched product groups with shelf prices, member prices and unit prices across the four major retailers.
Commercial add-on · Private MCP-compatible access
BasketWatch Agent Access exposes the live grocery dataset through a managed private layer for analyst copilots, internal chat tools and automated reporting workflows. It is designed for commercial teams that want answers with prices, promotions, unit prices and evidence attached.
Live agent session
A real workflow: a question goes in, the agent calls structured BasketWatch tools over MCP, and the answer comes back with retailers, prices and unit prices attached — never a guess.
What it can do
The agent layer is not a general chatbot. It is a focused interface over structured Irish grocery data across Aldi, Tesco, SuperValu and Dunnes Stores.
Ask for a product, pack size or category and return matched product groups with shelf prices, member prices and unit prices across the four major retailers.
Surface current Clubcard, Real Rewards, multibuy, was-price and member-only offers with the underlying offer text preserved.
Turn daily changes into readable summaries: what rose, what fell, by how much, and where the evidence came from.
Identify newly listed products, removed products and range churn by retailer, category or search term.
Build daily category notes, promotion roundups, competitor snapshots and customer-ready summaries from the latest snapshot.
Responses can include scrape dates, retailer names, product URLs, IDs and row-level context so an analyst can verify the conclusion.
Private tool layer
The agent uses controlled BasketWatch data tools, then writes the answer from returned rows. That keeps the LLM away from guessing prices or inventing promotional context.
Retailer, product name, product ID and product URL where available.
Shelf price, member/effective price and unit price basis.
Promotion label, was-price, loyalty pricing and validity text where captured.
Scrape date and freshness context for the data used.
Clear “not enough data” responses when a match is weak or unavailable.
How access works
The public site shows what the layer can do; the implementation stays private so access is controlled, metered and supported. Four steps from scope to live.
Agree the use case: analyst chat, daily brief, watchlist, comparison or internal copilot.
Usage caps, data access, cadence and support level matched to the team and subscription.
Approved customers get private setup details and keys. Connector internals stay unpublished.
Answers check against row-level evidence so teams trust the data behind the summary.
Pricing model
Separate from the public API marketplace. Best for teams that already know they want BasketWatch data inside an AI workflow and need controlled access, support and usage boundaries.