What Is AI Evidence Infrastructure?
AI evidence infrastructure preserves the information required to reconstruct, inspect and verify how an AI-assisted process moved from source material to an outcome.
Read technical note →Research / Technical Notes
Verification, evidence and AI infrastructure.
Technical notes on reconstructability, provenance, verification boundaries and evidence-aware AI systems.
Foundations
Evidence and verification foundations.
Why AI Audit Logs Are Not Enough
Audit logs can show that an event occurred. Verification requires enough evidence to reconstruct what happened, which inputs were available, what changed and under which system state.
Read technical note →Provenance, Traceability and Verification: What’s the Difference?
Provenance, traceability and verification are related but distinct. Together they form a stronger evidence model for reconstructing and independently reviewing AI-assisted workflows.
Read technical note →What Is a Verification Boundary?
A verification boundary defines which identities, inputs, state, transformations and claims are actually covered by a verification result.
Read technical note →From Evidence to Decision: Where Meaning Can Change
Information can remain technically intact while meaning changes across summaries, transformations, AI analysis and decision records. This article examines where those shifts occur and how to make them inspectable.
Read technical note →Verification & Reconstructability
From reconstruction to practical verification.
What Makes an AI-Assisted Decision Reconstructable?
A decision is reconstructable when an independent reviewer can recover the evidence basis, participating identities, relevant state, transformations and decision path without relying on undocumented context.
Read technical note →Deterministic Verification in AI Systems
Deterministic verification does not require a generative model to produce identical free-form output. It requires the same declared verification inputs and rules to produce the same canonical verification result.
Read technical note →Replayability vs Reproducibility in AI Workflows
Replayability and reproducibility are related but different. A workflow may be replayable without reproducing identical model text, while still reproducing the same evidence basis and canonical verification outcome.
Read technical note →What Is a Structured Verification Result?
A structured verification result records not only whether a check passed or failed, but also what was verified, which evidence and state were used, which rules were evaluated and what the result actually proves.
Read technical note →How to Design an AI Verification Pilot
A useful AI verification pilot starts with one bounded decision path, one explicit verification question and a small evidence package that can be inspected, replayed and independently reviewed.
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