Caneni addresses the challenge of verifying human oversight in AI-driven decision-making processes. It focuses on creating a verifiable record that a human, rather than an automated model, was present and made a decision. This record includes structured, timestamped entries detailing who reviewed a decision, what they considered, and what outcome they determined. The methodology ensures that this documentation is anchored independently of the AI system itself, providing a form that meets the expectations of regulators, auditors, or courts.
Unlike tools that only document the actions of AI systems—such as audit trails, model monitoring, or risk dashboards—Caneni fills the gap by evidencing the conscious presence and accountability of human decision-makers. This approach supports organizations and individuals who need to demonstrate compliance with policies requiring human oversight, and is relevant in contexts such as AI governance, professional liability, and algorithmic decision-making.
The system is designed so that session data and user responses are stored locally in the user's browser, with Caneni’s server not receiving this context. This emphasizes privacy and local control over the oversight record.
Caneni sits in PulseGate's LLM eval & observability category. It focuses on proving and documenting that a human reviewed and approved AI-driven decisions for compliance and accountability. Caneni is a B2B product aimed at compliance officers and AI governance teams. Caneni is available on the web.
Caneni first shipped in 2026. Among its 5 catalogued features are oversight evidence, audit trails, and human verification.
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I built a tool to prove a human reviewed an AI decision verified by the PulseGate indexer
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