Architect
Structure & systems
Designs structure, dependencies, and system boundaries.
Enterprise AI governance
Enterprise AI governance, enforced where the reasoning happens.
Most AI governance work stops at policy: an acceptable-use page, a model register, a review board. That governs procurement, not output. The variance enterprises actually feel shows up later — two analysts ask a model the same question, get two differently shaped answers, and neither answer records the assumptions it rests on. GHIA is a governance layer that closes that gap at the point of use.
Four controls that make reasoning repeatable
- 01
Role routing
Intent is matched to a defined reasoning role before an answer is drafted, so the same question is handled the same way by every operator.
- 02
A fixed output contract
Takeaway, structured reasoning, options and tradeoffs, recommendation — in that order, every time, so outputs stay comparable across teams and quarters.
- 03
Auditable assumptions
Assumptions and evidence gaps are stated in the response rather than buried, and faulty premises are challenged instead of quietly accepted.
- 04
Repeatable distribution
The governed answer is issued as the artifact people actually circulate — infographic, deck or memorandum — with no drift between reasoning and distribution.
Governance roles used for routing
Rather than one general-purpose assistant, intent is routed to a role with a stated mandate. The mandate constrains what the response is responsible for.
Analyst
Evidence & assumptions
Tests assumptions, quantifies impact, exposes gaps in evidence.
Strategist
Options & tradeoffs
Frames options, sequencing, and tradeoffs against objectives.
Communicator
Executive delivery
Packages reasoning into executive-grade communication.
How teams adopt it
Start with one decision class — vendor selection, capacity planning, a quarterly review — and run it under protocol for a full cycle. Because every answer returns the same four movements, the second cycle is directly comparable to the first, which is what turns individual output into an auditable record.
Frequently asked questions
- What is enterprise AI governance?
- The set of controls that make model-assisted work predictable: defined roles for reasoning, a fixed output structure, recorded assumptions, and review paths so decisions can be compared and audited later.
- How is governance different from a policy document?
- A policy states intent. Governance is enforced at the point of use — the reasoning path, the output shape and the record of assumptions are applied to every response rather than left to the individual operator.
- Does AI governance depend on a specific model?
- No. Governance is a layer above the model. GHIA applies the same role routing and output contract regardless of which model answers, so outputs stay comparable when models change.
Run your next decision under protocol.
Governed reasoning, auditable assumptions and enterprise deliverables in one session.
Open a governed session