RETAIL INTELLIGENCE

Retail & product understanding

Connect product images, attributes and content.

Build consistent categories, attributes and associations for product recognition, catalog preparation and image–text understanding. Turn scattered images, titles and descriptions into searchable, reviewable data.

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Product capture station with complete products, turntable, camera and lighting — RETAIL INTELLIGENCEFrom real-world signals to structured data.

APPLICATIONS

Start with the task your model needs to learn.

Connect each application to its data, processing rules and deliverables.

THE TASK / 01

Product recognition & localization

Define category levels and locate items in product and display images; record occlusion, similar packaging and multi-item sets.

WHAT THE DATA CONTAINSProduct classes, boxes or masks, and visibility attributes

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PRODUCT / 01

From description to consistent fields

Blue storage box · Plastic · 20 centimetres

Category: Storage box
Color: Blue
Material: Plastic
Length: 20 cm

Retain source text, normalize fields and units, and flag missing values separately.

THE TASK / 02

Attribute extraction & catalog cleaning

Extract specifications, materials and colors from titles and descriptions. Normalize aliases and units while preserving missing or conflicting values.

WHAT THE DATA CONTAINSField dictionary, structured attributes, missing-value and conflict lists

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IMAGE ↔ TEXT

One object, two sources to compare

Capture and description of the same blue object

Object in the image

Subject: Blue object
Description match: Compare color and shape
Unconfirmed fields: Size and material need source evidence

Pair by the same object; do not infer attributes that are not visible.

THE TASK / 03

Image–text matching & content review

Check whether images and text describe the same product. Distinguish primary items, accessories and usage scenes; record mismatch reasons.

WHAT THE DATA CONTAINSImage–text pair index, association labels and review reasons

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DATA IN · VALUE OUT

A clear brief. A traceable delivery.

A representative sample helps establish scope before batch work begins. Delivery fields and acceptance criteria are agreed during the pilot.

Missing data?Plan sources and a collection pilot Already have data?Check files, indexes and pairings first

01 / WHAT TO SHARE: Taxonomy, product identifiers, images and descriptions, target attribute fields and permitted content use.
02 / WHAT YOU RECEIVE: Product index, category and attribute files, image–text association table, deduplication records and quality checklist.
03 / WHAT TO REVIEW: Sample by category and field, checking entity associations, attribute completeness and source consistency separately.

QUALITY IN CONTEXT

Keep the same product consistent across data sources.

Category hierarchy

Confirm the taxonomy, distinctions between similar categories and rules for multi-category bundles during the pilot.

Attribute provenance

Retain the source for each field; do not infer attributes unsupported by the image or text.

Entity deduplication

Associate records using agreed product identifiers; distinguish duplicate images, product variants and separate products.

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PROJECT PERSPECTIVE

See how a task becomes a data project.

A mug, its detail photographs and packaging beside other product categories

RTL-001

Connecting multi-view images with product attributes

Associate primary, detail and packaging images with catalog fields. Resolve similar appearances, product variants and conflicting attributes to build consistent product data.

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BEFORE WE START

Common questions

01Can we keep our existing taxonomy?

Yes. Review hierarchy, aliases and uncategorized items, then clarify boundaries in a pilot. Record versions and mappings when categories are added or merged.

02How are attributes that are not visible handled?

Use agreed titles, descriptions or other trusted fields. If an attribute remains unconfirmed, mark it missing or pending review rather than guessing.

LET’S BUILD YOUR DATA

Tell us about your task and data.

Start with your task, the data available and one representative example. We can then discuss a practical pilot scope.

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