The Context Control Layer
for AI Data Quality
Caber ensures data is fresh, unambiguous, and compliant at the moment of use so you avoid the 3000x cost of fixing problems after the fact.
Not access control. Not just observability. Direct control of data in use.
AI agents retrieve and combine data fragments (sentences, tables, figures, byte sequences...) from across your systems.
80% of the context needed to ensure proper data use and quality can't be found matching keywords and patterns inside documents.
Ensuring data quality requires dynamic context, but fragments are copy-pasted everywhere and AI turns ambiguity into hallucination.
Caber breaks data into its basic building blocks (fragments) and grows context as they move.
Each fragment is enriched with relationships inside and between, documents, agents, and systems of record.
Current tools tell you what AI accessed.
Caber tells you what AI used, what it meant, and whether it was appropriate for that user's task based on the latest buisness context in your environment.
Triangulate
Know exactly what data is being used
Correlate signals, removing noise and ambiguity, to identify any fragment, even when it contains no patterns or keywords
Track
See what systems move and transform data
Follow fragments through APIs, MCP, A2A, and agents, associating each to the user request it serves and every system that touches it
Control
Right data, right user, right purpose
Apply policies dynamically at the moment data enters AI context, based on identity, not guesswork
Learn
Compounding accuracy advantage
Context graph improves with every interaction; relationships between fragments compound over time
Exclusive Early Access
Limited availability for select enterprise partners driving innovation with AI.
Category-defining data identification & control. Built by veterans from Akamai, Cisco, and Riverbed.