Governance for How AI Is Used — and How It Interacts
AI creates governance challenges on two fronts: the systems your organization deploys to interact with the people it serves, and the tools your teams use every day in their own work. Both require clear boundaries, defined authority, and structures that keep responsibility where it belongs.
Aluma addresses both — through architectural frameworks for client-facing AI systems and collaborative governance development for internal organizational use.
Governance on Two Fronts
Aluma provides governance resources for both dimensions of AI responsibility — the systems your organization deploys to interact with the people it serves, and the tools your teams use in their own work. Each pathway offers a different set of frameworks, tools, and collaborative support.
Client-Facing AI Systems
The Aluma Brain — a governance architecture that defines authority boundaries, escalation logic, and interaction structure for AI systems that communicate with the people you serve.
- Authority ceilings and bounded empathic expression
- Domain-specific architectures across five sectors
- Monthly governance briefs on emerging risks
Internal Organizational AI Use
Governance binders developed collaboratively with your team — tailored frameworks that clarify where AI assistance ends and human responsibility begins.
- AI Responsibility Health Check™ assessment
- Collaborative binder development with your teams
- Executive adoption and ongoing refinement
Client-Facing AI Governance
The Aluma Brain & Emotional Boundary Architecture
The Aluma Brain is a governance architecture — not a chatbot, not a model, not a platform. It defines the authority boundaries, interaction structure, and escalation logic that allow AI systems to remain useful without being mistaken for decision-makers, advisors, or sources of care.
Each Brain establishes clear authority ceilings, bounded empathic expression, structured escalation pathways, and preservation of organizational accountability. The architecture is preventive — designed into systems from the outset, not retrofitted through moderation after harm occurs.
Domain-Specific Architectures
AI systems don't operate in one universal context. The framework provides governance architectures tailored to five domains, each with distinct expectations, risks, and forms of authority:
Internal Organizational AI Governance
Governance Binders Built Around How Your Teams Actually Work
Most organizations don't have a clear answer to a simple question: where does AI assistance end and human responsibility begin? Not for the AI they deploy to customers — but for the AI tools their own staff use every day.
Through ThinkSpace, Aluma works with organizations to build that answer — not as a generic policy document, but as a governance binder tailored to how your teams actually work. The result is a living reference that clarifies decision authority, sets operational boundaries, and gives your people confidence in how they use AI.
Three-Stage Development Process
Governance Readiness Assessment
Map how AI is actually being used across your organization — formally and informally. Surface assumptions, align leadership on scope.
Collaborative Binder Development
Through workbook sessions with your team, translate operational insight into decision frameworks, thresholds, and escalation paths.
Executive Adoption
Formalize leadership adoption and adapt the governance architecture to your industry's specific ethical pressures.