A Knowledge Graph for the Modern Data Stack
RelationalAI is a next-generation database system for intelligent data applications based on relational knowledge graphs.
Build Intelligent Data Apps with RelationalAI's Knowledge Graph Management System
Productive
RelationalAI’s expressive, declarative language (Rel) leads to a 10-100x reduction in code. Applications are developed faster, with superior quality by bringing non-technical domain experts into the creation process and by automating away complex programming tasks.
Data-centric
Take advantage of the extensible graph data model as the foundation of data-centric architecture. Integrate models to discover new relationships and break down barriers between applications.
Cloud Native
Deliver effectively unlimited scale, workload isolation, time travel, versioning and instant sharing of knowledge through RelationalAI’s cloud native architecture. On-demand pricing with separation of compute and storage allows for elastic knowledge graph solutions.
RelationalAI Provides
Relational Graph Data Model
Efficiently represent labeled property graphs, RDF and relational data models as fully normalized (Graph Normal Form) relations. Leverage RelationalAI’s worst-case optimal join (WCOJ) algorithm and automatic index selection to efficiently query graph data without managing indices or providing hints for query optimization.
Declarative
Use RelationalAI's declarative, relational language Rel to model domain knowledge and reflect over data, meta-data, and logic. Rel is designed to be human-readable, allowing domain experts and developers to work together to specify intent when modeling business logic without dealing with procedural logic and control flow.
Incrementality and Reactivity
Take advantage of RelationalAI's declarative semantics and incremental computation to ensure models are always up-to-date in real-time when underlying data changes. RelationalAI’s incremental view maintenance minimizes the amount of information that needs to be restated after data inserts or updates.
Accelerated ML and Advanced Analytics
Complete support for machine learning and analytics through native libraries and integration of popular frameworks. Built-in ML and analytics algorithms enable a wide range of data processing capabilities including relationally accelerated machine learning, graph analytics, reasoning, and mathematical optimization.
Scalable Data Sharing
Instant cloning, versioning and branching of databases. Data sharing allows applications to scale out with virtually unlimited compute, offering full isolation for users and workloads while operating over a shared knowledge graph.
Pricing
RelationalAI offers pay-as-you-go pricing with no upfront costs. Only pay for the resources you consume, billed at per hour granularity. Compute and storage are metered separately, with the ability to dynamically add and remove compute resources. Get started for as little as a couple dollars an hour.
Contact RelationalAI for pricing details.