Turn data
into instinct.
I build machine-learning systems that learn from messy signals, become useful in production, and create decisions people can trust, from retrieval and forecasting to evaluation and inference.
Applied AI, LLM evaluation, retrieval systems, forecasting and ML platforms.
Four ways I make
models useful.
Context Engine
Retrieval, structured signals and evaluation gates for support intelligence, designed to make answers more grounded and measurable.
-31% resolution timeFrom hypothesis
to healthy inference.
Frame
Define the decision, failure modes, data boundaries and success metric.
Learn
Prototype representations, baselines and model behavior against real examples.
Stress
Probe edge cases, drift, hallucination, latency and regression risk.
Ship
Deploy with observability, evaluation gates and a feedback loop for iteration.
Small systems,
large signal.
Visual QA Copilot
Combined image embeddings, retrieval and structured validation to triage visual quality issues across a large content workflow.
Demand Pulse
Probabilistic forecasting pipeline with rolling backtests and confidence-aware alerts.
Intent Atlas
Semantic intent layer that clusters support language into actionable operational themes.
Have a signal
worth exploring?
For applied AI projects, ML platform work, research collaborations or thoughtful product conversations, send a note.