Research Focus
AI engineering questions I keep returning to.
My research interests sit in the applied zone: I like questions that can be explored experimentally and then shipped as product or platform capability. The throughline is simple — make AI systems more useful, more measurable, and more trustworthy.
Grounded AI systems
How can retrieval, reranking, and structured context improve answer quality while keeping costs, latency, and hallucinations under control?
Agent reliability
Which orchestration patterns make tool-using agents more observable, recoverable, and safe for production workflows?
Eval-driven development
How do we combine qualitative review, automated scoring, and workflow metrics to decide whether an AI system is actually improving?
Inference architecture
What serving, caching, and routing strategies create the best balance of performance, spend, and user experience?
How I approach research
- •Start with a business or workflow bottleneck rather than a model-first demo.
- •Test small hypotheses quickly with measurable success criteria and fallback paths.
- •Instrument quality, latency, usage, and failure modes before scaling adoption.
- •Turn successful experiments into maintainable product and platform primitives.
Proof of practice
This site highlights project case studies, stack decisions, and writing topics that reflect how I think about real AI systems: grounded answers, tool use, feedback loops, and operational excellence.