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AI-driven operations
at scale

Unified, AI-ready database platform for real-time infrastructure monitoring, predictive maintenance, and intelligent automation - from cloud to edge.

  1. Babcock
  2. ING
  3. Nvidia
  4. Samsung
  5. Tencent
  6. PolyAI
  7. Verizon
  8. Walmart
  9. Babcock
  10. ING
  11. Nvidia
  12. Samsung
  13. Tencent
  14. PolyAI
  15. Verizon
  16. Walmart

CAPABILITIES

Security and portability from edge to cloud

Robust memory safety and high performance

Written in Rust for memory safety guarantees with performance equivalent to C/C++, trusted by security-critical industries.

Portability to edge and embedded systems

Process data on-site, in-browser via WebAssembly, or across remote environments. Ideal for IoT-driven energy grids, smart factories, and field-deployed systems.

AI-ready: native vector and knowledge graph

Native vector search for embedding-based retrieval combined with knowledge graphs for representing complex entity relationships.

Flexible deployment: cloud to edge to browser

Run as a cloud service, self-hosted, embedded, or in-browser. Perfect for offline-capable control systems and mobile maintenance apps.

SEE IT IN ACTION

Detect anomalies in sensor data

Use time-series queries to monitor equipment telemetry and flag deviations before they cause failures.

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 for energy and manufacturing

Predictive maintenance and fault detection

ML models for equipment wear prediction and fault detection, integrated directly in edge-based instances.

AI-driven energy load forecasting

Optimise renewable generation assets via embedded AI logic and vector search for real-time grid optimisation.

Digital twin modelling

Model assembly lines using graph and time-series data for comprehensive operational visibility.

Supply chain visibility

Native graph engine for supply chain traceability and root-cause analysis in manufacturing.

Cybersecurity risk modelling

Anomaly detection in energy infrastructure and fraud detection in smart metering systems.

Time-series and historical analysis

Longitudinal data analysis and compliance audits with historical querying for trading systems and risk assessments.

TRUSTED BY

Powering innovation across industries

Samsung

Unlocking insights with knowledge graphs

Samsung uses SurrealDB to power knowledge graphs for real-time audience insights and ad targeting in its ad division.

Learn more
Tencent

Unified infrastructure monitoring

Tencent uses SurrealDB to consolidate nine backend tools into one real-time monitoring platform.

Learn more

WHY SURREALDB

Why decision makers choose SurrealDB

Horizontal scalability without sharding

Scale to petabytes of sensor, operational, and telemetry data without manual sharding across plants and distributed control units.

OLTP, BaaS, and real-time in one

Bridge transactional workloads and backend-as-a-service use cases with AI pipelines for real-time inference at the data layer.

Graph capabilities for intelligent systems

Complex, multi-relational data modelling for supply chain visibility, cybersecurity risk, and compliance tracing.

Time-series and historical querying

Time-series ingestion from IoT sensors, market feeds, and SCADA systems with historical time-travel queries for rollback analysis.

Low operational overhead

Minimal operational complexity for both private sector modernisation and public sector digital transformation.

Frequently Asked Questions

LET'S GOOOO

Start building with SurrealDB

The multi-model database for AI agents. Unify data. Unlock intelligence. Scale anywhere.