Multi-cloud architecture
AWS, Google Cloud, and Microsoft Azure patterns covering private networking, managed services, regional placement, and KMS integration.
ENTERPRISE ENGINEERING
Deep technical architecture
& enterprise capabilities.
Cloud platforms, data systems, controlled AI, and reliability engineering—assembled around explicit security, operational, and procurement requirements.
Schedule Architecture ReviewARCHITECTURE PRINCIPLE
Specific enough to operate. Flexible enough to fit.
This page describes capability coverage, not a promise that every technology or control appears in every solution. The final architecture, service levels, evidence, and responsibilities are documented during discovery and contracting.
CLOUD & DEVOPS INFRASTRUCTURE
Cloud foundations built for controlled change.
Architecture choices follow workload, data-boundary, recovery, and operating requirements. A technology is included only when it improves the system in scope.
Containers & orchestration
Docker packaging with Kubernetes, managed container platforms, or simpler runtimes selected according to scale and operational complexity.
Infrastructure as Code
Terraform-based infrastructure definitions, environment separation, reviewable changes, and repeatable provisioning where appropriate.
Release engineering
GitHub Actions or GitLab CI pipelines with automated checks, gated promotion, rollback planning, and blue/green, canary, or rolling deployment patterns.
DATA PIPELINES & AI STACK
Authoritative data around bounded AI.
Models interpret within a larger system of source data, retrieval policy, validation, human review, and controlled actions. Framework choice never replaces explicit system boundaries.
Operational data
PostgreSQL and Supabase for relational workloads, with Redis for caching, rate coordination, queues, and short-lived operational state.
Retrieval & vector search
Pinecone, Qdrant, pgvector, or Weaviate selected around tenancy, locality, filtering, scale, and deployment constraints.
Pipelines & event processing
Apache Kafka and Airflow patterns for streaming, scheduled orchestration, replay, lineage, and recoverable data movement.
LLM orchestration & HITL
LangChain, LlamaIndex, or focused typed services with evaluation, prompt boundaries, tool permissions, confidence routing, and human review queues.
MONITORING, RELIABILITY & SERVICE LEVELS
Reliability is designed, measured, and contracted.
Observability, failure handling, recovery objectives, and support responsibilities are defined against the service's business importance and deployment model.
Observability integrations
Datadog, Sentry, Prometheus, Grafana, cloud-native monitoring, and structured logs can be integrated into the agreed operational model.
Failure containment
Rate limits, timeouts, circuit breakers, idempotency, bounded retries, queues, dead-letter handling, and graceful degradation reduce cascading failure.
Recovery architecture
Health checks, backup and restore procedures, provider fallback, replication, and automated failover are selected and tested where the topology supports them.
Service-level commitments
Availability objectives—including targets up to 99.9%—require defined scope, dependencies, exclusions, measurement, support coverage, and a signed SLA. No universal SLA is implied.
PROCUREMENT NOTE
Capabilities become commitments only when they are in scope.
Deployment topology, data residency, security controls, recovery objectives, availability measurement, support coverage, and third-party dependencies are confirmed in the proposal and contract.