Content Graph is an AI lab pioneering Data Integrity Protection™. A new category of verification infrastructure for high-stakes AI. We enable pharmaceutical, legal, and financial organisations to move beyond probabilistic outputs toward auditable, citable truth that meets emerging regulatory and compliance requirements.

Large language models are stochastic by design. Whether steered by autonomous agents or industry specialists such as financial analysts, medical writers, lawyers, or scientists, these models produce outputs that are plausible but inherently unverifiable. While RAG adds context, it does not ensure accuracy. We build domain-specific pipelines that anchor AI reasoning in authoritative data and validate outputs against multi-source evidence. This transforms plausible guesses into defensible, high-stakes actionable data that can be trusted.

Our founding team brings vast experience in cloud-based in-line DLP and Data Privacy for regulated markets at Cisco. Just as DLP stops data exfiltration, our mission is to prevent 'dirty data' infiltration from contaminating enterprise intelligence. Through foundational research in LLM verification and uncertainty quantification, we provide the mission-critical layer necessary for AI to withstand the highest levels of regulatory and legal scrutiny. 

We call this Data Integrity Protection™.

Content Graph

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