Built for financial intelligence

Q: Is SurrealDB suitable for regulatory compliance in finance?

Yes. Field-level access control, encryption at rest and in transit, and point-in-time queries with VERSION on a versioned storage engine (experimental) support audit trails, data lineage and regulatory reporting.

Q: Can SurrealDB handle high-frequency financial data?

Yes. Built in Rust for low-latency, high-throughput workloads, SurrealDB sustains continuous streams of market data, transaction events and risk calculations under load.

Q: How does SurrealDB model complex financial relationships?

Accounts, counterparties, instruments and beneficial owners are first-class graph edges, so ownership chains, hidden connections and KYC network analysis resolve in a single query.

Q: Can SurrealDB power AI-driven financial advisors?

Yes. Embeddings for research, regulations and client communications sit beside structured portfolio data, so semantic search and graph traversal combine in one retrieval query.

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Performance. Built in Rust for low-latency, high-throughput fraud detection, trading and market data streams.

Modelling. Flexible models for every instrument and account, starting schemaless and adding structure later.

Compliance. Field-level access control, encryption and point-in-time queries for audit trails and reporting.

CAPABILITIES

Security built in. Designed for compliance-driven, high-throughput financial systems.

Built in Rust

Memory and thread safety for a regulated industry.

Data integrity

Reliability and performance where a failed write costs money.

AI-native

Vector search and knowledge graphs for assistants and predictive analytics.

One platform

Relational, graph, time-series and vector data in a single engine.

SEE IT IN ACTION

Detect fraud with graph traversal. Suspicious patterns across entities in a single query.

Accounts connected to a flagged entity through two or more hops of risky transfers.

1-- Find accounts connected to a flagged entity
2-- through 2+ hops of transactions
3SELECT
4    ->(transferred WHERE risk_score > 0.8)->account AS recipients,
5    ->(transferred WHERE risk_score > 0.8)->account.{2} AS second_hop
6FROM account:flagged_001;

USE CASES

Core use cases. How financial institutions apply SurrealDB.

Fraud detection and AML

Graph models that surface hidden connections for KYC and identity fraud.

Market data and risk

Tick-by-tick feeds, portfolio risk metrics and strategy backtesting.

Audit and compliance

Point-in-time queries with VERSION on a versioned storage engine (experimental) for regulators and governance.

Generative AI for finance

Reasoning across embeddings, structured data and semantic relationships.

Real-time trading platforms

Banking apps, trading dashboards and compliance alerts from one data layer.

Document processing

Contracts, reports and filings processed and served to customer chatbots.

WHY SURREALDB

Why decision makers choose SurrealDB. The advantages that matter in financial services.

Horizontal scalability

Grows with customers and transaction volume, with sharding handled by the engine.

Adaptable schema

Start schemaless, then add structure as products and regulations change.

Real-time capabilities

Trading dashboards and compliance alerts from one operational data layer.

Regulatory compliance

Point-in-time queries with VERSION on a versioned storage engine (experimental) and audit capabilities built in.

AI-native features

Vector search and knowledge graphs for the next generation of fintech.

Your questions,answered

How does SurrealDB support real-time fraud detection?

Native graph traversals analyse transaction networks as they form, catching circular transfers, velocity anomalies and coordinated accounts, and live queries push alerts to analysts as activity occurs.

Is SurrealDB suitable for regulatory compliance in finance?

Can SurrealDB handle high-frequency financial data?

How does SurrealDB model complex financial relationships?

Can SurrealDB power AI-driven financial advisors?

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

Dedicated clusters by region, or serverless on SurrealDB Earth.

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. Transactions, risk graphs and real-time analytics with the governance regulators expect.

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