A client needing a full UK luxury department-store catalogue to power AI shopping tools.

Use Case

Product catalogue enrichment for AI shopping and discovery

Region

United Kingdom (GBP pricing)

Sources

Harrods (harrods.com, UK storefront)

Number of sources

Key metrics

Full Category Coverage – Brand by Brand

Key metrics

Size & Colour Availability – Variant by Variant

Key metrics

Price & Was-Price – Product by Product

Contact Sales

Key Takeaways

Actowiz Solutions extracted the full Harrods product catalogue for a commerce-technology company building AI shopping tools. We covered every category and brand page and captured product-level detail – GBP prices, was-prices, colours, per-size availability, descriptions, materials, care notes and image URLs – then delivered it as clean, structured Harrods product data under an agreed service level.

What did the client need?

The client builds AI-driven shopping tools, such as conversational search and styling assistants. Those tools are only as good as the catalogue behind them, so they needed Harrods product data that was deep, current and consistent.

They wanted the whole catalogue at full depth: every department, designer and product with its variants. UK shoppers are already used to buying online – the ONS reports that 28.8% of retail sales were made online in August 2026 – so AI tools need product facts that match what the live site shows.

What made this hard?

A deep category tree. Products sit under departments, sub-categories and designer pages, often several at once. Each had to be counted only once.
Variant-level availability. One coat can come in several colours and many sizes, each with its own stock status.
Rich product pages. Luxury listings carry long descriptions, materials, care and fit notes that had to land in separate fields.
JavaScript-rendered pages and load management. Much of the content loads in the browser, and request rates had to stay polite and steady.
Currency and region settings. Prices and availability can change with the shopper's country, so every crawl had to stay on the UK storefront in GBP.
Image URLs, not image rights. We delivered public image URLs only; image rights stay with their owners, and licensed image datasets are not offered.

How did Actowiz solve it?

We built a dedicated single-retailer pipeline tuned for depth and data quality.

Approach

Full category mapping first. We mapped every department and designer page, then de-duplicated product URLs while keeping all category paths.
Browser-based rendering with polite pacing. Pages were rendered as a normal visitor sees them, with rate limits and off-peak scheduling. Public pages only – no logins.
Variant explosion. Each product became colour-and-size rows with stock status, linked to a parent style code.
Structured PDP parsing. Descriptions, materials and care notes were cleaned of layout text and split into their own fields.
Fixed UK context. Every run used the UK storefront and GBP, with price, was-price and a timestamp on each row.
Quality checks before delivery. Automated checks flagged missing prices, empty variants and category drops, plus a manual review sample.

Data Attributes / Fields

Field	Description
product_name	Product title as shown on the product page
brand_designer	Brand or designer name
category_path	Full category path(s), e.g. Women > Bags
price_gbp / was_price_gbp	Current price and previous price when on sale, in GBP
colour / size	One row per colour and size combination
size_availability	Stock status for each size and colour variant
description / materials / care	Description, material composition and care as separate fields
image_urls	Public URLs of product images (URLs only – no image rights)
product_url / sku_style_code	Product page link and the retailer's SKU or style code
scraped_at	UTC timestamp of capture

What were the results?

Full-depth catalogue. The client received the whole UK catalogue, covering all categories.
Variant-ready data for AI. Per-size, per-colour stock lets the client's assistants avoid recommending sold-out sizes.
Clean product content. Tidy content fields made the Harrods product data ready for search indexing and model inputs.
Reliable repeat delivery. Data was delivered on a recurring schedule under the agreed service level.

Project at a Glance

Metric	Value
Use Case	Luxury catalogue data for AI-driven shopping experiences
Region	United Kingdom (UK storefront, GBP)
Sources	Harrods (single retailer, all categories)
Metrics	Prices, was-prices, variant availability, content, image URLs
Reconciliation / Method	URL de-duplication; variant rows linked to style code; automated and manual QA
Cadence	Recurring schedule (agreed per project)
Output	Structured catalogue with one row per variant

Client Feedback

“Actowiz delivered the full Harrods catalogue at variant level — every colour, every size, with clean content fields. It's exactly the depth our AI shopping tools needed.”

— Head of Product, Commerce-Technology Client

Frequently Asked Questions

Q: What Harrods product data can Actowiz collect?

A: Public product-page details: name, brand, category path, GBP price and was-price, colours, sizes, per-size availability, description, materials, care, image URLs, URL, style code and timestamp. No personal data, nothing behind a login.

Q: Do you provide product images or image rights?

A: Public image URLs only. Image rights stay with the retailer or brand; we do not offer licensed image datasets or IP indemnity.

Q: How often can a luxury catalogue be refreshed?

A: Cadence is agreed per project – for example weekly full runs with more frequent price and stock checks. Crawls are paced to keep site load light.

Q: Is collecting public product data legal in the UK?

A: It depends on the site's terms, database rights and how the data is used. We collect public pages only, respect site terms and avoid personal data. This is not legal advice; check with your own counsel.

Need a full luxury catalogue feed?

See our Harrods data service and luxury goods data scraping, or ask for a free sample. Visit actowizsolutions.com to discuss your data requirement.

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