Energy providers, industrial manufacturers, and public sector bodies face increasingly complex demands - from real-time infrastructure monitoring and predictive maintenance to intelligent automation and secure data governance. SurrealDB delivers a unified, AI-ready database platform built to meet the data challenges of today and tomorrow.
SurrealDB is written from the ground up in Rust, a language trusted by security-critical industries that provides memory safety guarantees with performance equivalent to C/C++. It offers robust memory safety and high performance for secure deployments.
SurrealDB offers portability to edge devices, browsers (via WebAssembly), and embedded systems, allowing data processing to happen on-site, in-browser, or across remote environments with minimal latency. Ideal for IoT-driven energy grids, smart factories, and field-deployed public systems.
SurrealDB empowers enterprises to build Generative AI and real-time ML applications by combining native vector search for embedding-based retrieval and semantic querying with integrated knowledge graphs for representing complex entity relationships.
SurrealDB supports multiple deployment models without sacrificing performance or consistency. Run it as a cloud service, self-hosted, embedded, or in-browser with WebAssembly. Perfect for offline-capable control systems, mobile maintenance apps, and browser-based dashboards.
| Use case | Description |
|---|---|
| Predictive maintenance and fault detection | Use ML models for equipment wear prediction and fault detection in manufacturing plants, integrated directly in edge-based SurrealDB 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 | Use SurrealDB’s native graph engine for supply chain visibility and root-cause analysis in manufacturing. |
| Cybersecurity risk modeling | Implement anomaly detection in energy infrastructure and fraud detection in smart metering systems. |
| Time-series and historical analysis | Support longitudinal data analysis and compliance audits with SurrealKV for trading systems, risk assessments, and performance benchmarking. |
Energy and manufacturing enterprises must scale to petabytes of sensor, operational, and telemetry data. SurrealDB provides true horizontal scalability without manual sharding, seamlessly scaling across plants, stations, and distributed control units.
SurrealDB bridges transactional workloads (OLTP) and Backend-as-a-Service (BaaS) use cases, enabling secure identity management, low-latency interactions, and integration with AI pipelines for real-time inference at the data layer.
SurrealDB’s native graph engine supports complex, multi-relational data modelling for supply chain visibility, cybersecurity risk modeling, and compliance tracing in smart metering and procurement systems.
For systems that require longitudinal data analysis and compliance audits, SurrealDB supports time-series ingestion from IoT sensors, market feeds, and SCADA systems with historical querying (“time travel”) for rollback analysis.
Ideal for both private sector modernisation and public sector digital transformation with minimal operational complexity and maximum performance.
Whether modernising industrial infrastructure, enabling AI-driven insights at the edge, or supporting secure, browser-based tools for public administration, SurrealDB offers a unified, secure, and AI-native data platform with the performance and flexibility required by the world’s most mission-critical sectors.
Enterprise-grade agentic memory, secured at the core. Deploy AI agents in days, not quarters.