AI Product Manager

Most teams build
AI features.
I build AI systems that
behave predictably
in production.

Focused on designing decision-driven systems across classification, orchestration, and response layers — with an emphasis on reliability under real-world conditions.

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Selected impact

97%

System accuracy
up from 88%

<1%

Hallucination rate
via guardrails + RAG

8wk

MVP to production
+ iterative redesign

+8%

User retention from
system-level behavior

These are system-level outcomes — not model benchmarks. The difference matters in production.

01

What I focus on

I work on AI systems where correctness alone is not enough.

Most teams optimize for model performance. I design systems that make consistent decisions under uncertainty — systems that still behave correctly when the input is unclear, incomplete, or wrong.

The gap between a model that performs and a system that behaves only becomes visible in production. That gap is where I work.

— When inputs are unclear or incomplete

— When signals are ambiguous

— When the "right" response is not obvious

— When failure modes need to be designed for, not discovered

Point of view

Most AI teams are not building systems.
They are building model wrappers.

That works in demos.
It breaks in production.

Explicit decision logic Controlled behavior under uncertainty Clear handling of ambiguity Failure modes by design System reliability over model accuracy

02

Selected work

Featured Case Study · AI Safety System

Kyrah AI — Designing a Reliable AI Safety System

Model correctness did not equal user trust. Redesigned the system architecture — not the model — to make consistent decisions under uncertainty. Introduced an explicit decision layer between classification and response generation.

Case Study · Consumer AI

Food Spy AI — Turning AI Output into Usable Insight

Users didn't struggle with getting data — they struggled with understanding what it meant. Conducted 20+ user interviews and translated AI output into structured, readable decision flows that improved clarity and usability.

Case Study · Enterprise · NETGEAR

NETGEAR — Designing a Structured Onboarding System

The issue wasn't lack of effort — it was lack of structure. Mapped end-to-end onboarding workflows, defined key states and transitions, and introduced a structured system that improved clarity, ownership, and visibility across teams.

team visibility

Case Study · Operations · Arlo

Arlo — Clarifying Internal Workflow Structure Across Teams

Internal workflows existed but lacked consistent structure across functions. Defined workflow requirements, aligned stakeholders across product and operations, and improved how teams coordinated within shared systems.

03

About

I'm an AI Product Manager focused on turning ambiguous human problems into systems that can make consistent decisions.

My background spans internal platforms and operational systems at NETGEAR and Arlo, where I translated complex workflows into more structured, scalable user experiences.

I now focus on building AI systems that behave reliably in real-world conditions — not just controlled environments.

University of Pennsylvania

MS — in progress

Cornell University

BS

NETGEAR — Internal onboarding workflows and platform design

Arlo — Internal people systems, reporting workflows, and program operations

Kyrah AI — AI safety systems, classification, decision logic, and guardrails