AI product & platform strategy · systems reliability · inventorship
AI product and platform strategy grounded in large-scale systems reliability, applied research, and working technical artifacts. I focus on choosing the right problem, designing the workflow around the model, and validating the system before it earns broader authority.
The through-line
In broadband systems, an explanation has to line up with telemetry, reproduce on real devices, and lead to a sensible release or triage action. Weak assumptions eventually become customer impact.
I bring the same discipline to AI products: define what the system may do, design the fallback path, and make the result easy for engineering and leadership to challenge.
How that shaped my work →Selected work
Issued patent · working evaluator
US 12,670,085 B1
A control point for a hard automation question: when should generated remediation be allowed to move forward?
ReplayGuard runs a bounded remediation twice, compares the resulting bytes, records cryptographic evidence, and routes the outcome through an explicit gate. The public implementation is inspectable and the synthetic evaluator runs entirely in the browser.
Applied research · under review
Treat uncertainty as routing behavior, not a score that disappears into a dashboard.
PCS-R studies image-text provenance and human-review routing for generative-AI workflows. The practical question is which cases can advance automatically and which need a person because the available signals are weak or conflicting.
See the research record →Platform reliability · field triage
Narrow what is happening, distinguish observation from explanation, and make the next move obvious.
My current work includes broadband and Wi-Fi field triage, telemetry analysis, release validation, and customer-impact investigation. The useful output is a narrower problem statement, a defensible interpretation of the signal, and a clear next test or owning team.
See operating work →How I work
01
Start with the user, business consequence, and workflow friction. Model choice comes later.
02
Define inputs, controls, review points, observability, and what happens when the system is uncertain or wrong.
03
Use tests, telemetry, and explicit launch criteria so the product can survive both technical and executive scrutiny.
Proof, not promises
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