Quick Answer
Model governance catalogs and manages AI assets — inventory, risk, lifecycle, and policy. Decision governance governs how AI-assisted outputs are reviewed, authorized, recorded, and evidenced when they can influence operational action.
Smart Logic AI focuses on decision governance and governed infrastructure. It does not position itself as a model-inventory or enterprise GRC clone.
What traditional AI governance typically covers
- Model inventory and approved-model catalogs
- Model risk scoring and use-case registers
- Policy management and compliance mapping
- Lifecycle governance for model onboarding and retirement
- Third-party model and vendor diligence
These capabilities are important. They do not, by themselves, reconstruct a specific authorized decision.
What Smart Logic AI decision governance covers
- How AI-assisted outputs are reviewed before action
- Who is authorized to approve, reject, or escalate
- How disagreement across models is surfaced and handled
- How evidence and provenance are preserved
- How decisions are recorded in an audit-ready path
- How audit evidence is exported for qualified review
Model governance vs decision governance
Organizations often need both layers. The mistake is assuming a complete model catalog equals governed execution.
| Dimension | Model governance | Decision governance |
|---|---|---|
| Primary question | What AI exists and how is it managed? | How did this output become an authorized action? |
| Typical artifacts | Inventory, risk tier, policy mapping | Reviewer, authorizer, decision record, audit export |
| When it matters | Program design, procurement, lifecycle | Every consequential workflow execution |
| Failure mode | Unknown or unapproved models in use | Unreviewed output treated as institutional decision |
How the layers connect
Models and agents enter through governance controls — not direct to operators. Human review and authorization gates sit before consequential action. Decision records and audit evidence follow the authorized path.
This is the architecture Smart Logic AI communicates on the corporate site and operationalizes through SmartSolo for multi-model decisions and CaptureIQ for federal capture workflows.
Original research directions
Smart Logic AI is developing corporate research on multi-model disagreement, authorization thresholds, and evidence completeness — without publishing fabricated benchmark scores.
Existing guides such as consensus vs divergence and what to record in a Decision Ledger support this research direction today.
Frequently asked questions
What is model governance?
Model governance typically covers model inventory, risk classification, lifecycle approval, policy alignment, and compliance management for AI assets across the organization.
What is AI decision governance?
Decision governance covers how a specific AI-assisted output is reviewed, who authorizes it, how disagreement is escalated, what evidence is preserved, and how audit records are produced at execution time.
Does Smart Logic AI replace model inventory tools?
No. Smart Logic AI focuses on decision governance — governing consequential AI-assisted decisions — not competing feature-for-feature with model-inventory or GRC platforms.
Where does SmartSolo fit?
SmartSolo operationalizes governed multi-model decision intelligence within Smart Logic AI's architecture — comparison, human authorization, and Decision Ledger records.
Can an organization need both?
Yes. Model governance answers what models exist and how they are managed. Decision governance answers how outputs become authorized actions with evidence. Both are often required in regulated environments.
Apply these ideas in an operational workflow
Educational resources explain governance concepts. SmartSolo helps teams operationalize review, authorization, and decision records.
See governed AI execution in a live workflow
Review how SmartSolo coordinates multiple AI models, routes human authorization, and preserves the decision record.