AI-ready infrastructure for modern retail

Q: Can SurrealDB replace multiple databases in a retail stack?

Yes. Documents, graph relationships, vector search and real-time updates run in one engine, and retailers commonly consolidate separate document stores, graph databases and caches onto it.

Q: How does SurrealDB handle product catalogue search?

Full-text search, vector similarity and structured filters combine in a single query, which covers type-ahead, semantic discovery and faceted filtering from the database.

Q: Is SurrealDB suitable for high-traffic e-commerce events?

Yes. Built in Rust and horizontally scalable, SurrealDB absorbs flash sales and seasonal peaks with consistent query performance.

Q: Can I model customer journeys as graphs?

Yes. Customer, product, order and review relationships are native graph edges, which powers recommendation engines, loyalty networks and churn prediction.

Start free with SurrealDB Talk to sales

Personalise. Real-time recommendations for every shopper.

Protect. Fraud detection on live transactions.

Scale. From storefront to supply chain, in one database.

CAPABILITIES

Personalisation at scale. Built for responsive, data-driven retail experiences.

Product recommendations

Graph and vector search tuned by behaviour, history and context.

Dynamic pricing

Prices that respond to segments, inventory and market conditions.

Fraud detection

Graph analytics over transactions, logins and checkout, in real time.

Visual intelligence with SurrealML

Vendor images classified, matched and moderated automatically.

SEE IT IN ACTION

Personalise with vector search. Purchase history and similarity, in one query.

The six products closest to the shopper's last purchase.

1-- Get the embedding of the user's last purchased product
2LET $last = SELECT *, out.embedding AS embedding
3    FROM ONLY purchased
4    WHERE in = user:shopper_01
5    ORDER BY at DESC LIMIT 1;
6
7-- Find visually and semantically similar products
8SELECT id, name, price,
9    vector::similarity::cosine($last.embedding, embedding) AS score
10FROM product
11WHERE score > 0.8
12ORDER BY score DESC
13LIMIT 6;

USE CASES

Core use cases. How retailers apply SurrealDB.

Real-time personalisation

Vector and graph search driving AI-powered recommendations.

Product discovery

Semantic search on embeddings for intelligent product matching.

Vendor onboarding

SurrealML tags and validates images for faster integration.

Transaction monitoring

Graph analytics for fraud and anomaly detection on live transactions.

Inventory forecasting

Time-series trends for supply chain optimisation.

In-store kiosks and tablets

WASM and edge deployment, in-browser or on-device, offline-capable.

Mobile shopping apps

An embedded engine that runs offline with full ACID guarantees; replication runs through your application.

Financial compliance

Point-in-time queries with VERSION on a versioned storage engine (experimental) for audit trails and regulatory reporting.

WHY SURREALDB

Why leading retailers choose SurrealDB. The advantages that matter in retail.

Your questions,answered

How does SurrealDB help with real-time personalisation?

Live queries push changes to connected clients instantly, and graph relationships between customers, products and behaviour events drive recommendations and dynamic pricing from the database itself.

Can SurrealDB replace multiple databases in a retail stack?

How does SurrealDB handle product catalogue search?

Is SurrealDB suitable for high-traffic e-commerce events?

Can I model customer journeys as graphs?

THE PLATFORM

Everything an application and its agents know. Five surfaces, one engine.

Database

Document, graph, vector, time-series and relational in one engine.

Five data models as dotted tiles: documents, graph, vector, time-series and relational

Agent Memory

What an agent learns, with its source and its time, in the same engine.

A timeline of remembered facts, each with its source

Cloud

Managed clusters in the regions you choose, scaled on demand.

Clusters in three regions on a world map, each running or scaling

Studio

Query, explore and design the schema from the browser.

MCP

Every model that speaks MCP reaches the database and the memory directly.

Three models connected through MCP to the database and Agent Memory

IN PRODUCTION

Trusted at scale. Samsung, Nvidia, Verizon, Tencent and Walmart run on SurrealDB.

14,000+

Developers building on SurrealDB Cloud

4M+

Developers building on SurrealDB worldwide

FROM THE TEAMS

SurrealDB gives us a foundation where we can unify semantic search, knowledge graphs, and AI-driven decision making without stitching together multiple systems. Collapsing responsibility into SurrealDB has become our default engineering posture.

Justin Foley

VP of Engineering, Later

GET STARTED

Start building with SurrealDB. Catalogue, inventory and personalisation in one engine.

Start free with SurrealDB View documentation