Built with granular security, by default
Memory-safe, security-first language
SurrealDB is built in Rust - a memory-safe, security-first language that protects PHI (protected health information) and meets stringent regulatory demands such as GDPR. Rust's tiny binary footprint also ships effortlessly to browsers (WASM), wearables, and bedside IoT gateways.
Runs everywhere - embedded, in-browser, on the edge
Keeps latency-sensitive AI inference close to the patient while easing offline/remote hospital scenarios. Perfect for rural tele-health kits that store data locally and sync when connectivity returns, or on-device image analysis for dermatology running entirely in a browser.
Unified OLTP / BaaS architecture
Real-time ingest + API server in one component; no middle-tier to manage. Mobile nursing apps can write vitals, schedule medication, and trigger AI triage rules through the same endpoint.
Native vector search + knowledge graph
Seamlessly stores embeddings alongside structured patient data; graphs capture complex biomedical relationships. Enables generative-AI clinical co-pilots that ground GPT-style answers in a knowledge graph of symptoms, drugs, genomics, and previous cases.
Core use cases for healthcare
Use case | Description |
|---|---|
Precision medicine and clinical decision support | Fuse vector search with longitudinal EHR, genomics and imaging data to let GenAI assistants surface differential diagnoses, drug-gene interactions or personalised care pathways in milliseconds. |
Edge-ready medical telemetry and IoT integration | Rust-built binaries run embedded or in-browser (WASM), so bedside monitors, wearables and implantable devices can ingest time-series vitals locally and sync opportunistically. |
AI-enhanced imaging and diagnostics | Store embeddings for X-ray, CT or pathology slides alongside metadata; SurrealDB's native vector index retrieves visually similar cases to aid radiologists or histopathologists. |
Patient-facing chatbots and experience optimisation | Knowledge-graph joins pull lab results, upcoming appointments and relevant literature into a single semantic layer, allowing conversational agents to answer questions in plain language yet stay clinically grounded. |
Fraud detection, billing integrity and zero-trust security | Embedded graph analytics traverse provider-patient-procedure networks in real time, flagging anomalous claim patterns or duplicate records with fine-grained role policies. |
Continuous remote monitoring and time-series analytics | High-frequency sensor streams feed directly into SurrealDB's time-series engine; anomaly detectors alert clinicians to arrhythmias or sepsis risk in sub-second latency. |
Precision medicine and clinical decision support
Fuse vector search with longitudinal EHR, genomics and imaging data to let GenAI assistants surface differential diagnoses, drug-gene interactions or personalised care pathways in milliseconds.
Edge-ready medical telemetry and IoT integration
Rust-built binaries run embedded or in-browser (WASM), so bedside monitors, wearables and implantable devices can ingest time-series vitals locally and sync opportunistically.
AI-enhanced imaging and diagnostics
Store embeddings for X-ray, CT or pathology slides alongside metadata; SurrealDB's native vector index retrieves visually similar cases to aid radiologists or histopathologists.
Patient-facing chatbots and experience optimisation
Knowledge-graph joins pull lab results, upcoming appointments and relevant literature into a single semantic layer, allowing conversational agents to answer questions in plain language yet stay clinically grounded.
Fraud detection, billing integrity and zero-trust security
Embedded graph analytics traverse provider-patient-procedure networks in real time, flagging anomalous claim patterns or duplicate records with fine-grained role policies.
Continuous remote monitoring and time-series analytics
High-frequency sensor streams feed directly into SurrealDB's time-series engine; anomaly detectors alert clinicians to arrhythmias or sepsis risk in sub-second latency.
Why healthcare organizations choose SurrealDB
Horizontal scalability - no sharding gymnastics
Handles national health-service-scale datasets and hundreds of simultaneous AI agents without re-architecting. Perfect for population-wide drug-efficacy analytics or AI-driven patient-recruitment across millions of records.
Flexible and versatile data model
Switch between document, graph, and time-series views with one engine - ideal for evolving research schemas. Rapid-prototype clinical trials DB that starts as docs, later adds graph relations and time-series vitals without migrations.
Graph queries for complex patterns
Detect complex, multi-hop patterns that signal fraud, money-laundering, or cyber-intrusions into hospital networks. Enables insurance claim fraud-detection AI, real-time care pathway optimisation, and zero-trust security graph for medical devices.
Time-series analytics
Sub-second aggregation over high-frequency sensor or market feeds. Perfect for cardiac telemetry anomaly detection, algorithmic trading of healthcare ETFs, and risk-scoring wearables.
Historical querying with SurrealKV
Immutable "time-travel" queries simplify compliance audits and post-incident investigations. Enables regulatory audit trail of every patient-record mutation and pharmacovigilance back-testing across decades of dosage data.
AI-optimised data infrastructure for healthcare and med-tech innovation
Modern healthcare enterprises and medical technology companies rely on fast, secure, and intelligent access to data - from patient records and sensor streams to diagnostic models and regulatory reporting. SurrealDB empowers AI-driven transformation by unifying your structured, unstructured, and real-time data needs into one scalable, secure platform - deployed anywhere.
