Connect & Support PoCiSys
Evidence should be inspectable, reproducible, and honest about its limits.
PoCI evolves alongside new models, new threats, and better verification methods.
Trust should come from verifiable records—not promises.
Track the origin and evolution of AI-generated outputs, ensuring transparency and accountability in AI systems.
Verify that computation occurred as claimed with rigorous auditing processes to ensure data integrity.
Generate tamper-evident proofs using modern cryptography to secure the integrity and authenticity of data.
Explore the code on GitHub or help fund the hardware and infrastructure behind PoCiSys.
Bitcoin (BTC): bc1qc90t4k7mknx8ea4fqzzq82nqtrfypzwvjrym9d
Kaspa: kaspa:qrja43sagmduqxqc4zf78zesg4tpv36t0a9va5v9kkffnlxha25lkqrcre8m9
PoCI is an open-source, black-box auditing framework created by 12GaugeKenshin for a hard problem: how do you verify an AI system without forcing its owner to reveal the model, prompts, training data, or intellectual property?
Instead of judging only an AI system’s output, PoCI watches the operational signals around a computation—timing, token-generation behavior, execution patterns, pathway signatures, and related non-content telemetry. Those signals establish a trusted behavioral baseline.
When the system drifts, changes configuration, swaps models, encounters prompt injection, or behaves unexpectedly, PoCI can flag the deviation and create a tamper-evident record. Cryptographic proofs can be anchored to Kaspa for public ordering and permanence, while sensitive event data remains off-chain.
PoCI does not claim to decide whether an answer is correct. It provides evidence that a computation came from the expected system and followed its known operating profile.
PoCI is an open-source framework for producing verifiable records of AI computation. It captures non-content operational metadata, creates cryptographic proofs, and preserves provenance without requiring access to private models, prompts, or training data.
