Dynamically connect the business significance and relevance to data you give to AI models, to deterministically control data quality and agent security.
AI agents and applications see your data as a collection of individual elements—sentences, paragraphs, tables, and images—nearly all of which are duplicated across multiple documents, databases, and applications.
Caber uniquely tracks these elements as they move through your systems, to identify what they mean, and how they should be used, to ensure AI uses the right data, at the right time, and in the right way.
Caber starts where data classification ends identifying the business context by the relationships data elements have with each other inside and between documents, and the metadata that describes them in storage, databases, and APIs.
Through optional SDK and API integrations, Caber traces the use and movement of data elements in AI applications and APIs. By following the path of data Caber is able to connect API calls and AI agent data use back to the user requests are made for.
Build business logic based on the user and data identity to filter data before it reaches AI models for inference. Caber can also detect and redact junk and duplicate data to compact context windows, reduce AI model costs, and improve performance.
Caber uses patented algorithms to build relationships between elements up to 100,000x faster and more scalably than can be done using AI models alone—all without adversely impacting user experience.
Let us show you how you can turn your data into knowledge with control and scale.