Production AI systems engineering
Engineering disciplines
AI systems designed as production infrastructure — with security, evaluation, observability, and ownership built in from the start.
Agent Orchestration Systems
Production-grade agent systems with explicit tool boundaries, approval flows, and failure handling — designed to operate under real inputs and real constraints.
- Tool boundaries + permissioning
- Approval flows and escalation paths
- Structured evaluation harnesses
- Cost telemetry and controls
Retrieval Infrastructure
Knowledge access systems built for accuracy, permission enforcement, and measurable retrieval quality in production environments.
- Ingestion pipelines and chunking strategy
- Permission-aware retrieval
- Evaluation sets and regression checks
- Operational monitoring
Platform & Reliability Engineering
Platform foundations that make AI systems deployable, observable, and maintainable — not just functional.
- Cloud & hybrid architectures
- DevSecOps + CI/CD automation
- Observability + incident readiness
- Performance + scalability engineering
How we measure success
Operational integrity
- Auditability and access control
- Deterministic fallbacks
- Clear runbooks and ownership
Quality and stability
- Offline evaluation against test sets
- Regression checks and guardrail validation
- Monitored drift and error budgets
Business impact
- Reduced cycle time
- Lower operational load
- Improved release readiness / MTTR
Cost control
- Token/cost telemetry
- Rate limiting and quotas
- Right-sized model selection
Discuss an architecture
We begin every engagement with an Architecture Review — a focused assessment of your system's constraints, integration patterns, and evaluation strategy. You'll leave with a clear picture of where your system will fail in production — and how to fix it.
No commitment required. You'll leave with a clear architectural assessment — whether we work together or not.