Evidence-led AI with human approval

A local operations system reconstructs work from source evidence, lets models classify bounded material and keeps every external action under explicit human authority.

Evidence moving through reconstruction, model assistance, review and approved actionThe model helps interpret evidence. It does not inherit authority from the workflow.Source evidence01Reconstruction02Human review03Approved action04
The model helps interpret evidence. It does not inherit authority from the workflow.

01 / The field note

An operational AI system becomes safer when it can answer two questions: “what evidence produced this suggestion?” and “who is allowed to act on it?” The architecture described here treats both as first-class data.

This note describes the architecture of a private system in active use. It excludes company data and connected-account details.

01

Collect evidence locally

The system ingests permitted local history from version control, work sessions, memory and exported or read-only work tools. Every item retains its source, time and identity instead of becoming an unattributed summary.

Normal reads do not contact external providers. Refresh is a separate operation with an explicit scope.

02

Separate reconstruction from interpretation

Deterministic code normalizes timestamps, identities and relationships. A model can then classify or summarize a bounded evidence packet without inventing the underlying activity graph.

This makes it possible to correct a model conclusion without losing the evidence that produced it.

03

Preserve provenance and confidence

Every suggestion links back to the evidence used and records what could not be confirmed. Sensitive or ambiguous material stays private and reviewable.

Confidence is not a permission level. A high-confidence suggestion still needs the authority required by the destination system.

04

Keep external writes explicit

Drafting and posting are separate states. The exact message, comment or change is shown before the user approves the external action.

That boundary turns AI into an analysis and preparation layer while the human remains the accountable operator.

03 / Working principles

The reusable part

What to carry into the next system.

  1. 01

    Keep source identity and timestamps attached to every evidence item.

  2. 02

    Use deterministic code for reconstruction and models for bounded interpretation.

  3. 03

    Expose missing evidence alongside confidence.

  4. 04

    Separate drafting from every external write.

05 / Contact

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Need this kind of decision in your system?

A discovery call is enough to map the constraint, identify the evidence still missing and decide on the smallest useful intervention.

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