AI for Large, Complex Product Catalogs

Millions of products. Tens of millions of variants. Connected knowledge that helps customers find the fitting part.

knowledge-graph product-catalog b2b product-recommendations ai-chatbot

A large catalog is more than a long list. The relationships matter: which part fits which machine, which variant meets a specification, which accessory works with a device, and which document explains the difference.

Flyweight's Knowledge Graph is designed to process and prepare millions of products and tens of millions of variants. It connects the information an AI needs to answer a specific customer question across a complex B2C or B2B assortment.

Connect the details that determine a match

Products and variants: identifiers, dimensions, material, configuration and other available attributes.
Compatibility: supported machines, models, devices or product combinations where your data provides them.
Technical content: manuals, specialist articles, support documentation and approved guidance.
Business rules: questions to clarify, alternatives to consider and cases your team should review.

The AI uses this connected knowledge to narrow a request and explain the result. Product discovery becomes a conversation instead of a customer having to guess your catalog terminology.

Prepare the catalog for useful answers

Scale starts with the source data. Together, we review identifiers, variant structure, relationships, missing attributes and the sources that hold your technical expertise. A large catalog may need a dedicated import and data-preparation process.

We scope the implementation around your catalog, connected systems and retrieval requirements. The standard Shopify Preview setup is a starting point; a custom catalog at this scale has its own project plan.

Baumashop24: find the part through the machine

Baumashop24 serves businesses, workshops and municipalities with construction-machine spare parts and accessories. Its assortment includes filters, bushings, pins, glazing and attachments across several manufacturers.

That creates a concrete discovery challenge: customers often begin with the machine they own rather than the part number they need. The reviewed customer conversations included model-specific parts searches, filter kits and questions about original parts and alternatives.

A useful decision path is:

Identify the manufacturer and exact machine model.
Clarify the component and any required dimensions or reference numbers.
Check the available product and compatibility information.
Present a supported match, or pass the missing details to a specialist.

This example describes the product-discovery challenge. It does not imply that every unlisted part or compatibility relationship is available in the catalog.

One knowledge foundation, several ways to help

The same knowledge supports AI product recommendations, storefront search and customer service. A trade buyer's list can also become the input to a configured quote workflow.

For current order status, the AI uses the appropriate live Shopify connection. Catalog knowledge and a live transaction lookup serve different purposes and need the right sources.

Frequently asked questions

Can you work with millions of products?

The Knowledge Graph is designed to process and prepare catalogs with millions of products and tens of millions of variants. We review your data structure, import needs and connected systems to define the implementation for your catalog.

Do all variants need complete descriptions?

Useful attributes, consistent identifiers and documented relationships improve matching. We identify gaps during data preparation and define follow-up questions or handovers for cases where the available information is insufficient.

How does the knowledge stay current?

Paid Shopify plans refresh the Knowledge Graph every 24 hours; Free Preview uses the initial import. Custom sources and large-catalog synchronization are scoped for the integration. Live order tracking uses Shopify's current order data.