Skip to content
Start free
All industries

Ecommerce & retail

Help shoppers find
what fits.

Connect product records, semantic search, keywords, and catalog relationships in one database for your storefront applications.

Build a product catalog Discuss your storefront

Discovery that knows the catalog

One product. Several ways to find it.

A shopper describes a need. Your application retrieves candidates, checks their catalog records, and decides what to show.

From a shopper’s words to a useful productIllustrative catalog · application-orchestrated retrieval
Shopper’s search

“A daypack for wet commutes”

Catalog recordsku_17

Commuter daypack

Price
$89
Inventory
Available
Category
Bags
01Semantic matchVector

Find product descriptions near the query’s embedding.

Candidate ID sku_17
02Keyword intentFull-text

Match words in the indexed product description.

Waterproofdaypackfor daily travel.
03Category relationshipsGraph

Follow the relationships you declare in the catalog.

Commuter gearBagssku_17
Your storefront application

Merge candidate IDs → read catalog rows with SQL → check stock and price → rank results.

Eligible productsOrdered by your merchandising rules

The application combines separate retrieval results. Ranking, stock checks, and merchandising rules stay in your code; index filtering depends on the vector mode you choose.

Explore recommendations

5 use cases / E-commerce & retail

The right modalities for your workflow.

Start with the combination that fits your question. These are recommended starting points; the best fit depends on your data and query patterns.

E-commerce & retail: use cases, recommended query modalities and why each combination fits.
Use caseRecommended modalitiesWhy this combination fits
Product discovery

Find products from exact keywords or a shopper's description.
SQL FTS Vector

FTS matches product terms, Vector finds similar meaning, and SQL supplies price and stock for the application's final selection.
Related-product recommendations

Build a shortlist of similar products and connected accessories.
SQL Graph Vector

Vector finds similar items, Graph follows product and accessory relationships, and SQL provides availability and catalog attributes.
Order and inventory operations

Track orders, product availability and fulfilment status.
SQL

SQL joins order and inventory records, filters operational status and aggregates quantities and sales.
Catalog relationship navigation

Explore variants, compatible accessories and category relationships.
SQL Graph

SQL retrieves product attributes. Graph follows the catalog relationships that your merchandising application defines.
Returns case investigations

Connect returns, orders and customers with their supporting correspondence.
SQL Graph FTS

SQL narrows return records, Graph connects orders and customers, and FTS searches descriptions and correspondence for relevant evidence.

Combine query results in your application using shared record IDs. Keep text indexes and embeddings updated as the source data changes.

Start with the product ID

A catalog your team can maintain.

Keep the ingestion path explicit so a new price, description, or product variant reaches the right query model.

Follow the catalog example
  1. 01

    Give each product a stable identity.

    Use the row’s primary key as the vector ID and full-text document ID. Declare category and supplier relations in the schema.

    Define your schema
  2. 02

    Write each representation deliberately.

    Row, vector, and full-text updates are separate calls. Re-embed and re-index changed descriptions; retry failed steps. Declared graph edges follow the row write.

    Index your embeddings
  3. 03

    Keep purchase rules close to checkout.

    Recheck inventory and price in your commerce system before purchase. Search relevance does not reserve stock or authorize access to a customer’s order.

    Add keyword retrieval

Before the storefront rollout

Evaluate with your own catalog.

Test real search language.

Compare category terms, product names, and descriptive queries against a shortlist your team has reviewed.

Check change handling.

Change a description, remove a product, and interrupt an indexing step. Verify the application handles missing or stale candidates.

Measure the whole request.

Include embedding generation, retrieval, row lookups, and application ranking when you measure response time.

Build around your catalog

Start with one discovery workflow.

Open the working example Explore the database