01 / PIPELINES
Machine learning pipeline orchestration
Design repeatable training and inference workflows with observable handoffs, controlled data movement, and production-grade release paths.
AI engineering services / Raleigh, NC
Production architecture for agentic systems.
ZTSµ designs the orchestration, model operations, and secure deployment paths that let product teams ship AI capabilities with confidence.
02 / AGENT RUNTIME
Agent framework deployment
Deploy Plan-Execute and Reflexion patterns across Azure Foundry Agent, LangChain, LangGraph, and CrewAI with accuracy and operational controls.
03 / MODEL BOUNDARY
Private LLM operations
Build private LLM deployments and offline inference paths for teams that need model capability without compromising data boundaries.
04 / TRUST LAYER
Secure data pipelines
Containerize sensitive data flows and establish agentic TLS certificate authentication that verifies workload identity at every boundary.
05 / EVALUATION
Model quality engineering
Create learning acceptance tests, fine-tuning workflows, prompt engineering practices, and vector embedding strategies that can be measured.
06 / CONTROL ASSURANCE
Governance & security engineering
Establish centralized and distributed policy management, Policy Enforcement Point design, TLS certificate authentication, evaluation gates, and learning acceptance testing for governed AI operations.
Engagement model
Start with the constraint that is blocking deployment.
01
System review
Map the current model, agent, data, and policy surfaces.
02
Architecture plan
Define the target controls, interfaces, and operating boundaries.
03
Implementation
Engineer the deployment path with validation built in.
Bring the hard technical question.
Whether the constraint is agent accuracy, private inference, or secure data movement, ZTSµ can help turn it into a production architecture.
Request a technical consultation