AI-driven operations
at scale

Yes. SurrealDB is built in Rust for high-throughput, low-latency ingestion and supports time-series aggregation. It handles continuous streams from factory sensors, smart meters, and SCADA systems with consistent performance at scale.

Yes. SurrealDB compiles to a single binary and runs embedded on edge gateways and industrial PCs. It stores and queries data locally for real-time process control, then synchronises with a central instance when connectivity is available.

SurrealDB stores equipment telemetry alongside vector embeddings from vibration analysis or thermal imaging. Native vector search identifies similar failure patterns, while graph relationships map dependencies between machines, components, and maintenance histories.

Yes. SurrealDB's graph edges model supplier-component-assembly-facility relationships as first-class connections. You can traverse multi-tier supply chains, trace materials through production, and identify single points of failure in a single query.

Yes. SurrealDB's versioned temporal tables let you retrieve data as it existed at any past point. This simplifies regulatory audits, quality investigations, and energy compliance reporting without maintaining separate audit databases.

Yes. A production-grade digital twin needs to model assets and dependencies as a graph, ingest high-frequency telemetry as time series, hold configuration and documents, embed unstructured context as vectors, and answer historical queries about how state has changed. SurrealDB handles all of these natively, in one query language and one transaction. Spectron, our memory and knowledge layer, extends the same substrate to the AI agents that increasingly operate inside twins. See /use-cases/digital-twins.