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SIA

Architecture

Persistent external semantic governance for models, agents, and AI workflows.

A technical view for CTOs, architects, AI researchers, and evaluators. Less marketing, more mechanism.

An architect working through a system diagram

Canonical sequence

Objective in, evidence out.

  1. 01Human-originated objective
  2. 02Persistent external governance state
  3. 03AI model / agent
  4. 04Observable output or proposed action
  5. 05Same-state comparison / semantic authorization
  6. 06Machine control
  7. 07Governance record
  1. Human-Originated Objective
  2. Persistent External Governance StateExternal
    Outside the model boundary
  3. AI Model / AgentProvider
    Interchangeable · provider-defined
  4. Observable Output
  5. Same-State Comparison
    PRESERVEDREGULATEWITHHOLDREVIEW
  6. Machine Control
  7. Governance Record

Persistent state

Persistent, external, and authorized to change only under recognized authority.

A new system version does not automatically grant authority to redefine the governing objective.

Persistent

Available for later comparison or control across a workflow.

External

Independent of model weights, hidden activations, or provider-specific workspace.

Authorized transitions

Governance-state changes require separately recognized authority with recorded lineage.

Same-state comparison

Output-state representation compared against retained state.

The comparison produces a machine-control relationship.

Release
Regenerate
Change instruction
Withhold
Route for review
Select model / configuration
Create governance record

Claim-level provenance

Provenance travels with the claim.

MeasuredSource-groundedModel-assistedCalibrated / estimatedUnavailableInconclusive

Privacy architecture

De-identify inside the trust boundary.

SIA supports architectures that de-identify regulated identifiers inside organizational trust boundaries before external model processing, then re-associate results under governed control. A deployment pattern, not a compliance guarantee.

Efficiency architecture

Separate meaning from realization.

The architecture allows semantic-content generation and linguistic realization to be separated. Preliminary internal evaluations have indicated potential output-token cost savings on evaluated workloads. Workload-dependent. Updated benchmark results coming soon.

Capability evolution

A research boundary, stated plainly.

Increasing AI capability should not, by itself, constitute authority to redefine the human-originated objective governing that capability.

What SIA does not claim

  • Reads unexpressed human mental states.
  • Requires hidden chain-of-thought.
  • Replaces cybersecurity.
  • Replaces human accountability.
  • Guarantees regulatory compliance.
  • Currently claims to control recursive self-improvement.

Technical overview

Go deeper on the architecture.

Download Technical OverviewRead the White Paper