Production AI systems engineering
Engineering disciplines
We design and build AI systems as software infrastructure—architected for security, evaluation, observability, and ownership.
Agent Orchestration Systems
Production-grade agent architectures that integrate with your stack and remain operable under real inputs, real users, 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 and security: ingestion, indexing, permission-aware retrieval, and measurable quality.
- Ingestion pipelines and chunking strategy
- Permission-aware retrieval
- Evaluation sets and regression checks
- Operational monitoring
Platform & Reliability Engineering
The platform foundations that make AI shippable: CI/CD, observability, reliability patterns, and secure integration.
- 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
If you have a production use case—or a pilot that needs hardening—we can review constraints and propose a concrete build plan.
Talk to an engineer →