Systems and context
Identify systems of record, relevant data and events, business definitions, owners, and refresh expectations.
Fit the operating model to the environment
Define boundaries and prerequisites around the customer’s systems, business context, identity, deployment, operations, and recovery.
Architecture evaluation begins with a bounded operating area and the responsibilities it creates. Detailed design follows the sources, environment, controls, and acceptance evidence agreed for that scope.
Universal inference and automatic portability
Use any language or analytical model and inference provider. Aevah routes work and automatically preserves Operating DNA, policy, workflow state, and evidence when the selected model changes.
Production-ready deployment continuum
Run the complete offering with customer-selected subscriptions, customer-controlled cloud inference, Aevah-managed private AI, Aevah datacenter, customer datacenter, or universal air-gapped operation.
Implementation effort
Document the selected use case, source and environment readiness, integration work, identity and policy dependencies, customer responsibilities, Success Capacity, and what is excluded from the first scope.
Identity, source, and inference boundaries
Map human and service identities, least-privilege authorization, privileged access lifecycle, permitted sources, inference context, data movement, and review. The constraint layer is evaluated before an action is taken, not audited after it.
Assured operations and recovery
Contract-backed commitments cover the agreed performance, scale, recovery, and implementation targets. Complete rebuild, model replacement, operational handoff, and customer-run readiness preserve the customer’s Operating DNA.
Validated security and certifications
Independently validated security enforcement and regulatory certifications extend across identity, isolation, policy, source, inference, action, and deployment boundaries.
Responsibilities
Record what the customer owns, what Aevah drives, what is shared, and what remains excluded across infrastructure, inference, data, identity, operations, support, and acceptance.
Consistent Aevah operating layerOperating DNA · orchestration · policy · evidence · change control
Bring your cloud and inference subscriptions
Use customer-selected services inside an agreed operating boundary.
Customer-controlled cloud inference
Keep inference accounts, access, and policy under customer control.
Aevah-managed private AI
Use a privately operated footprint with responsibilities defined for the scope.
Aevah-datacenter deployment
Place the agreed footprint in an Aevah-controlled facility.
Aevah Private Infrastructure
Own the complete data, Aevah platform, AI, and inference environment for production operation in your datacenter, including air-gapped environments.
Every option carries the same Aevah operating layer. Environment prerequisites and operating responsibilities are confirmed for the selected scope.
Evaluation responsibility mapExact boundaries are agreed for each scope
Customer owns
- Operating outcome and accountable owner
- Business meaning, policy, and decision rights
- Source and environment access
- Adoption and accountable acceptance
Shared
- Value case and measures
- Scope and prerequisites
- Evidence and constraints
- Production and expansion decision
Aevah drives
- Operating-pattern implementation
- Context, workflow, and evidence connection
- Governed change and handoff
- Success Capacity and knowledge transfer
A practical next step
Choose one consequential area. Start there.
In the first conversation, we’ll compare the operating problem, the people who own it, and the evidence that would make a next step worthwhile.
Review architecture boundaries