Architecture Rehearsal
Know the blast radius before you deploy. Architecture Rehearsal builds a causal dependency graph, runs failure scenarios, and blocks unsafe changes before they reach production.
Pre-deploy break risk → verified after change
Engineering cases
Systems with evidence.
Engineering systems for production — from AI infrastructure and Kubernetes platforms to reliability, recovery, and control planes.
01
Primary story
Three cases that set the architecture narrative.
TwinOps Control Plane
TwinOps treats industrial digital twins as operable infrastructure: OpenUSD composition, GitOps ownership, drift detection, reconciliation, telemetry, and incident replay on Kubernetes.
Compose → detect drift → reconcile
BlastGuard
Supply Chain Blast-Radius Control Plane: don't count CVEs — predict where they can reach, decide ALLOW/WARN/QUARANTINE/BLOCK, and prove remediation.
Blast radius predicted before CVE count
02
More flagship
6 more flagship systems.
AI Infra Control Plane
Private AI platforms need shared governance across identity, policy, audit, cost, and SLO signals — not scattered service configs
Private AI with explicit platform contracts
AI Runtime Platform
AI workloads often start as API calls but become production systems that need identity, policy, scaling, telemetry, and clear ownership
Every model call: policy + trace + budget
Cloud-Native AI Gateway
AI usage needs routing, policy, budget awareness, and provider resilience
One boundary: rate, budget, fallback, OTel
Kanister Backup & Restore
A green backup job is not proof you can recover — artifacts, secrets, schema, and RTO still fail quietly
Restore proved before the incident
GitOps: Argo CD & Flux
Teams need a clear delivery model before GitOps becomes another layer of operational confusion
Delivery as explicit platform contracts
RAG Knowledge Platform
Engineering knowledge is spread across repositories, runbooks, tickets, architecture notes, and project history
Cited answers from project evidence
03
Supporting
More architecture cases.
Breadth across recovery, observability, GitOps delivery, policy, and platform foundations.
Automatic SaaS Restore System
Restores are high-pressure, manual, and easy to execute inconsistently
Recovery as a platform capability
SBOM Integration
Software supply-chain data is often generated late, stored separately, and disconnected from deployment decisions
SBOM on the delivery path, not quarterly
LLM Infrastructure Runtime
LLM workloads move faster than traditional platform controls and can quickly become expensive, opaque, and hard to operate
LLM serving with operability signals
EKS Platform Foundation
Kubernetes clusters become inconsistent when networking, identity, ingress, storage, and observability are assembled per project
Clusters as a repeatable product
OpenTelemetry Observability Mesh
Metrics, logs, and traces often exist separately, making incidents slower and ownership unclear
Request path explainable end-to-end
SLO-Driven Monitoring
Dashboards can look healthy while users experience latency, errors, or degraded workflows
Alerts from SLO burn, not chart noise
Multi-Region GitOps
Multi-region systems need repeatable promotion and rollback without turning every deployment into manual coordination
Regional drift stays auditable
Terraform Platform Modules
Cloud platforms drift when teams copy infrastructure snippets and adjust them under delivery pressure
Infra changes as reviewable product PRs
Argo CD App of Apps
As platforms grow, application onboarding, add-ons, and environment drift become hard to reason about
Platform bootstrapable from Git
Policy as Code Guardrails
Security and platform rules are often discovered only after deployment or during reviews
Standards enforced in CI, not slides
Secrets and Certificate Automation
Manual secret rotation and certificate handling create outage risk and hidden operational debt
Secrets + TLS with a real lifecycle
Incident Runbook Automation
Incidents are slower when context, dashboards, logs, and recovery steps live in different places
Repeatable response → lower MTTR
Self-Healing Infrastructure
Transient infrastructure failures can become user-facing incidents when recovery depends on manual detection
Common failures recover with guardrails
Developer Platform Interface
Developers lose time when every deployment, environment, and infrastructure request requires platform team translation
Golden paths without losing control
Cost and Token Observability
AI and cloud costs can grow quietly when usage is disconnected from teams, services, and deployment changes
Token spend visible before finance week
FleetDM Endpoint Visibility
Endpoint visibility is often separate from cloud and Kubernetes operations, leaving security context incomplete
Endpoints in the infra picture
Zero Trust Service Mesh
Internal traffic is often trusted by default, making lateral movement and policy gaps hard to see
East-west mTLS by default
Available for meaningful infrastructure conversations