A Knowledge Graph for the Modern Data Stack

RelationalAI is a next-generation database system for intelligent data applications based on relational knowledge graphs.

Data-centric application design brings data and logic together into composable models. Intelligent data applications understand and make use of each relation that exists in a model.

RelationalAI provides a knowledge graph system to express knowledge as executable models. These models can be easily extended through declarative, human-readable programs.

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.