Projects / Applied AI
Applied AI, designed as decision systems.
Selected areas of work that connect models to context, constraints, evaluation, and the people responsible for the next action.
- Agentic AI
- Recommendation Systems
- Semantic Knowledge
- Adaptive ML
Selected systems
The model is one part.
The decision is the work.
01 / Agentic AIDependable LLM agents
AI agents designed to make progress without hiding uncertainty from the people using them.
- Intent routing and tool orchestration
- Grounding with semantic and operational context
- Behavioral evaluation and observability
- Confidence, reversibility, and human review
02 / RecommendationsConstraint-aware ranking
Recommendation systems that treat eligibility and operating conditions as first-class parts of relevance.
- Candidate quality and contextual fit
- Hard constraints and availability
- Legible ranking tradeoffs
- Outcome feedback for continual improvement
03 / Adaptive MLLearning under change
Machine learning systems designed for shifting data, evolving conditions, and evaluation across time.
- Drift-aware monitoring
- Selective model updates
- Longitudinal evaluation
- Feedback loops connected to real outcomes
04 / Knowledge systemsSemantic context for action
Knowledge and communication layers that put the right operational context inside an AI-assisted workflow.
- Semantic retrieval and structured context
- Policy-aware communication
- Traceable evidence
- Interfaces built around a useful next action
Method
How I move from ambiguity
to a working system.
01 / FrameDefine the decision
Clarify who acts, what outcome matters, and which constraints cannot be negotiated.
02 / EvaluateTest useful behavior
Measure quality, risk, consistency, and failure modes—not only model fluency.
03 / LearnShip the feedback loop
Make outcomes observable so the product, data, and model can improve together.