APPLIED AI
AI Integration & LLM Applications
Moving an LLM feature beyond a demo requires thoughtful integration: data exposure, reliability, costs, latency, and evaluation all matter. I help teams build practical AI-enabled capabilities within their software.
Discuss this challenge ↗When this helps
Common challenges.
- Connecting LLM APIs to existing .NET services and workflows
- Handling untrusted model output safely
- Managing prompt inputs, data privacy, and provider failure modes
- Measuring usefulness, latency, and operating cost
What I can deliver
Practical outcomes.
- LLM API integration patterns for .NET
- Provider abstraction and error-handling guidance
- Evaluation, logging, and safety considerations
- Architecture for maintainable AI-enabled application features
My approach
Context before complexity.
Treat AI as an external, probabilistic dependency: define measurable outcomes, validate outputs, and keep critical actions under application control.