Private by default
Prefer customer-tenant deployment and data boundaries that match regulated reality.
Architecture Principles
Opinionated defaults for enterprise AI systems.
Principles InheritX applies when designing private, governed, IP-transferrable AI, useful for CTOs comparing builders vs. integrators.
Capabilities
Seven principles
Evaluate before you scale
Define quality and risk tests early; do not confuse fluency with readiness.
Human gates on consequential actions
Autonomy is earned inside policy, not granted globally because a demo impressed.
Attribution over opacity
Design for reconstructable decisions: tools used, context retrieved, approvals given.
Observability is a product requirement
Tracing, cost, and failure modes are visible to operators, not buried in vendor dashboards you cannot export.
IP-transferrable by construction
Prefer designs your team can own: code, configs, evals, and runbooks that survive handover.
Commodity where it helps; ownership where it matters
Use foundation models and cloud primitives for speed; own orchestration, fine-tunes, retrieval, and workflow glue.
Perspective
LLMOps in practice
Delivery & observability layers include regression checks for prompts/agents, cost controls, and guardrails appropriate to the workflow. Exact tooling varies by estate, AWS, Azure, GCP, and your existing CI/CD.
We document trade-offs in blueprint so security and platform teams can accept the path before build velocity increases.
Next step
Map this capability to your mandate.
A focused strategy conversation, constraints, systems, and what production readiness looks like for your organization.