AI-driven operations
at scale

Q: Does SurrealDB run on edge devices?

Yes. SurrealDB is a single binary that runs embedded on edge gateways and industrial PCs, running offline with full ACID guarantees; replication to a central instance runs through your application.

Q: How does SurrealDB support predictive maintenance?

Equipment telemetry sits alongside embeddings from vibration or thermal analysis. Vector search finds similar failure patterns and graph edges map machines, components and maintenance histories.

Q: Can SurrealDB model supply chain and asset relationships?

Yes. Supplier, component, assembly and facility relationships are first-class graph edges, so multi-tier chains, material traces and single points of failure resolve in one query.

Q: Does SurrealDB support historical queries for compliance?

Yes. Point-in-time queries with VERSION on a versioned storage engine (experimental) return data as it stood at any past point, which covers regulatory audits, quality investigations and energy compliance reporting from the same database.

Q: Can SurrealDB act as the data backbone for digital twins?

Yes. A digital twin needs assets as a graph, telemetry as time series, configuration and documents, vectors for unstructured context and historical queries. SurrealDB handles all of them in one query language and one transaction, and Agent Memory extends the same substrate to the agents operating inside twins. See /use-cases/digital-twins.

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Monitor. Real-time infrastructure monitoring across every site.

Predict. Maintenance planned from live and historical signals.

Automate. Intelligent automation from cloud to edge.

CAPABILITIES

Security and portability, edge to cloud. Built for distributed industrial environments.

Memory-safe and fast

Written in Rust, with performance on a par with C and C++.

Edge and embedded

On-site, in-browser via WebAssembly or on field-deployed systems.

Vectors and knowledge graphs

Embedding retrieval and entity relationships in the same engine.

Cloud to edge to browser

Cloud service, self-hosted, embedded or in-browser, from one codebase.

SEE IT IN ACTION

Detect anomalies in sensor data. Time-series queries that flag deviations before they cause failures.

Readings from the last hour compared against the rolling 24-hour average.

1-- Detect temperature anomalies in the last hour
2-- by comparing against the rolling 24h average
3LET $avg = (SELECT VALUE math::mean(value)
4    FROM reading
5    WHERE sensor = sensor:turbine_01
6    AND timestamp > time::now() - 24h)[0];
7
8SELECT timestamp, value
9FROM reading
10WHERE sensor = sensor:turbine_01
11    AND timestamp > time::now() - 1h
12    AND value > $avg * 1.3;

USE CASES

Core use cases. How energy and manufacturing teams apply SurrealDB.

Predictive maintenance

Wear prediction and fault detection running in edge-based instances.

Energy load forecasting

Embedded AI logic and vector search for real-time grid optimisation.

Digital twin modelling

Production lines, energy assets and supply networks as living graphs.

Supply chain visibility

Native graph traversal for traceability and root-cause analysis.

Cybersecurity risk modelling

Anomaly detection across energy infrastructure and smart metering.

Time-series and history

Longitudinal analysis, and compliance audits through point-in-time queries with VERSION on a versioned storage engine (experimental).

WHY SURREALDB

Why decision makers choose SurrealDB. The advantages that matter in industrial settings.

Your questions,answered

Can SurrealDB handle industrial IoT sensor data?

Yes. The Rust engine ingests continuous streams from factory sensors, smart meters and SCADA systems at low latency, with time-series aggregation built in.

Does SurrealDB run on edge devices?

How does SurrealDB support predictive maintenance?

Can SurrealDB model supply chain and asset relationships?

Does SurrealDB support historical queries for compliance?

Can SurrealDB act as the data backbone for digital twins?

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. Telemetry, digital twins and predictive maintenance in one real-time engine.

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AI-driven operations
at scale