Frequently Asked Questions
What is RelationalAI? What does the company do?
RelationalAI is a cloud AI company on a mission to simplify computing, with a vision for data-centric computing that enables people to tell computers what they want to do — without having to tell them how to do it. RelationalAI has brought to market the industry’s first AI coprocessor, based on relational knowledge graphs, enabling teams to efficiently build intelligent apps with enriched semantics.
RelationalAI’s groundbreaking relational knowledge graph system expands data clouds with integrated support for graph analytics, business rules, optimization, and other composite AI workloads. RelationalAI is cloud-native and is built with the proven and trusted relational paradigm. These characteristics enable RelationalAI to seamlessly extend data clouds and empower organizations to implement intelligent applications with semantic layers on a data-centric foundation.
What market problem is RelationalAI working to solve?
AI adoption is at an inflection point that demands a combination of AI techniques to generate results in the data cloud that cannot be achieved in silos with point solutions. RelationalAI is the only relational knowledge graph technology supporting a diversity of workloads including graph analytics, business rules, and optimization, bringing the power of composite AI into the hands of developers to democratize the development of intelligent applications.
What is RelationalAI building? What is the product?
We have created the industry’s first AI coprocessor for data clouds and language models using a groundbreaking relational knowledge graph system to bring knowledge to where data is or is moving to: the data cloud. RelationalAI brings together data, business logic, and an environment that supports multiple composite AI workloads like graph analytics, business rules, and optimization analytics, all of which can be executed in data clouds. By running these workloads natively where their data is, organizations can drive efficiency and discover important, latent insights that power better decisions.
What is an AI coprocessor for data clouds?
An AI coprocessor for data clouds is a software-based processor used to augment a data cloud’s primary functions with AI capabilities. Just as a graphics processing unit (GPU) helps a central processing unit (CPU) in a computer by enabling specialized workloads like graphics, gaming, and machine learning the RelationalAI service acts as a coprocessor for the data cloud, enabling workloads including graph analytics, business rules, optimization, and other composite AI workloads. The foundation of RelationalAI is a relational knowledge graph system developed using some of the latest database systems research.
What is a relational knowledge graph?
Knowledge graphs model business concepts and the relationships between them. Our innovation is reimagining knowledge graphs as systems that execute your business logic and run multiple composite AI workloads from within your data cloud, eliminating data movement and duplication. RelationalAI is designed with a cloud-native architecture and the relational paradigm, making it compatible with your data cloud.
How does RelationalAI compare to dedicated solutions?
Unlike point solutions, which operate outside your data cloud, RelationalAI is always in sync with your data and enables you to apply multiple AI techniques to solve a problem. The result is a level of insight that is unmatched in the market.
What sets us apart from point solution providers is that RelationalAI is cloud-native and is built with the proven and trusted relational paradigm. These characteristics enable RelationalAI to seamlessly integrate and extend data clouds.
What industries and use cases does RelationalAI support?
RelationalAI extends organizations’ data clouds with integrated support for graph analytics, business rules, optimization, and other composite AI workloads across industries including financial services, retail, and telecommunications. RelationalAI is used in a variety of use cases including:
- Fraud prevention by surfacing meaningful patterns and predictive elements in data to reduce loss and protect customers.
- Supply chain optimization with dynamic recommendations to reduce costs and maximize resource utilization.
- Contextual recommendations that pair behavior-based recommendations with supply chain signals to improve the customer experience and align with business priorities.
How does RelationalAI add value to language models?
As transformative as large language models are, their effectiveness can be further amplified when combined with data clouds and knowledge graphs. The RelationalAI knowledge graph system maps business concepts, rules, and relationships, providing LLMs with context for enhanced accuracy and governance. To learn more, please contact us.
What is your relationship with Snowflake?
Snowflake is the category leader for cloud databases. We have a deep appreciation for what Snowflake has built. They demonstrated that the relational model is the right model for big data, at a time when most of the world thought it would be Hadoop. Some of our colleagues at RelationalAI were part of that early effort, and we have developed a very rich partnership with Snowflake.
What is your pricing model?
RelationalAI offers a consumption-based pricing model: Pay-as-you-go with no upfront costs and no annual charges. You 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 few dollars an hour.