AI TOOL PROFILE
Rocketgraph: In-Memory Graph Analytics Platform
- Data and Analytics
- Graph Analytics
- Enterprise companies
- Federal government agencies
- Cybersecurity analysts
- Financial crime investigators
Pricing
Rocketgraph uses a pay-as-you-go billing model. A 30-day free trial is available.
At a glance
- Best for
- Enterprise companies, Federal government agencies, Cybersecurity analysts, Financial crime investigators
- Key use cases
- Fraud Detection, Anti-Money Laundering (AML), Vulnerability Management, Knowledge Graph Creation, Path Finding
- Integrations
- Oracle, MongoDB, Databricks, Snowflake, TigerGraph
- Official website
- Visit Rocketgraph official website
How AI is used
Rocketgraph is an in-memory graph analytics platform designed to handle large datasets, including hundreds of billions of nodes and edges. Rather than acting as a traditional database, it operates as an analytics engine that supports deep traversals and graph-wide scans to help users identify patterns.
The software is designed for organizations and government agencies operating in environments such as cybersecurity, financial crime investigation, and infrastructure management.
Through an interface called Mission Control, the platform allows analysts to ask questions in natural language. The system then generates Cypher queries, which may reduce the need for specialized data science expertise to perform complex analysis.
Buyers can choose between a fully-managed cloud service called Aurora or an on-premise deployment for specific security or infrastructure requirements.
Key Features
In-Memory Processing
Operates in-memory to reduce latency bottlenecks associated with disk-based systems.
Mission Control AI
A natural language interface that helps translate plain-English questions into Cypher queries and schemas.
Graph-Wide Scanning
Supports searching the entire graph without requiring sampling or partitioning.
LLM Integrations
Supports connections to large language models including OpenAI, Anthropic Claude, AWS Bedrock, and private on-premises models.
High-Scale Capacity
Designed to manage datasets consisting of hundreds of billions of nodes and edges.
Flexible Deployment
Available as a fully-managed cloud service (Aurora) or via on-premise installation.
Use Cases
Fraud Detection
Building entity-centric graphs of customers and devices to identify anomalous links and mule clusters.
Anti-Money Laundering (AML)
Resolving entities across payments and watchlists to expose circular flows and intermediaries.
Vulnerability Management
Mapping assets and SBOMs to exploit signals to prioritize items based on business impact.
Knowledge Graph Creation
Developing knowledge graphs to enhance organizational data offerings.
Path Finding
Using Breadth First Search features to identify specific paths within a network.
Integrations
- Oracle
- MongoDB
- Databricks
- Snowflake
- TigerGraph
- Neo4j
- S3
- OpenAI
- Anthropic Claude
- AWS Bedrock
FAQ
Is Rocketgraph a database?
- No, it is an in-memory graph analytics platform designed to act as an accelerator for existing workflows rather than a primary database.
How does the AI handle data privacy?
- When using agentic query and schema features, only data labels are sent to the LLM, not the actual record values.
What is the difference between on-premise and Aurora deployment?
- Aurora is a fully-managed cloud service, while on-premise deployment is for organizations with existing hardware footprints or strict security requirements.
Can Rocketgraph handle very large datasets?
- Yes, it is designed for scale and has been used for graphs ranging from a few thousand edges to hundreds of billions.
Source category: Data & Analytics
Source subcategory: Graph Analytics
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