Senior Product Manager · Boston, MA
I build generative AI products that work in production — not just in demos. 15 years in B2B SaaS, fintech, and marketing technology. Currently at HubSpot.
About
Most AI product failures aren't model failures — they're context failures. The model gets thin inputs, produces thin outputs, and the team blames the technology. I focus on the part before the prompt: what context does the user need to give the system, how do you make that easy, and how do you know the output is actually useful.
That's been the core of my work at HubSpot: building the context infrastructure that makes AI-generated campaign assets accurate and on-brand, not just fast. It's what I brought to Orum before that, where getting the context architecture right turned meeting summaries from "kind of useful" into something AEs relied on every day.
I've built products across retirement fintech, wealth management, sales intelligence, and marketing technology. The common thread: reducing friction between user intent and system output, at scale.
Experience
Owning campaign creation and AI output quality for Marketing Studio. Leading discovery for Campaign Planning Hub — a structured workspace that lives upstream of creation, turning unstructured planning artifacts into the context that makes AI-generated assets actually good. The problem isn't the model; it's the brief.
Running continuous discovery with marketers, building out the Opportunity Solutions Tree, and driving cross-functional alignment across design, engineering, and GTM on what gets built first and why.
Discovery in progress — OST complete, early user research signals strongBuilt Orum's first generative AI features: meeting summaries and coaching insights from conversation transcripts. The efficiency gains came from getting the context architecture right — transcript quality, speaker attribution, structured prompt engineering around specific sales workflow outputs.
Also shipped the enterprise design system that reduced design-to-engineering friction across the platform.
30% end-user efficiency gain · $8K saved per seat · $2M design system impact annuallyRebuilt enterprise onboarding from a one-at-a-time workflow to a bulk transaction system that scaled 10x. Delivered the iOS advisor experience and built the measurement infrastructure — success metrics, tracking, and iteration cadence — that gave the team a real feedback loop for the first time.
10x onboarding scale · 30% feature adoption lift · 4x API performanceEleven years building the retirement plan mobile app from the ground up. The headline number is 1.3M monthly active users — but the work was obsessing over enrollment friction. Every reduction in friction tied to a measurable enrollment rate, and five years of consistent compounding is what produced the growth.
1.3M MAU · +25K users/month for 5 years · 25% enrollment increaseCase Studies
Orum's meeting summaries generated from raw transcripts with no structure — speaker order jumbled, filler included, no signal about what mattered. Users opened them once and stopped.
Ran discovery with AEs and SDRs to understand what they actually needed post-call. The answer wasn't a shorter transcript — it was structured extraction: talk time ratio, key objections, committed next steps, and a coaching signal. Rebuilt the prompt architecture around those outputs.
Strong download numbers, low enrollment rates. Users downloaded the retirement app, started the flow, and dropped. Nobody knew where — or why — because the funnel wasn't instrumented.
Instrumented the enrollment funnel for the first time and ran structured usability testing. Found three friction points — a confusing beneficiary step, an SSN entry that felt insecure, and a flow that buried contribution options. Prioritized fixes by enrollment impact, not engineering effort.
Marketers using HubSpot's AI campaign creation weren't getting on-brand output. Not because the models were wrong — because the inputs were thin. No campaign context, no tone guidance, no audience specifics.
Leading discovery for Campaign Planning Hub: a structured workspace upstream of creation that converts unstructured planning artifacts into context that makes AI generation accurate. Built the OST, running continuous discovery with marketers to validate problem shape and solution space.
Work Samples
The take-home that led to the role — real SDR research, a before/during/after framework, and a prioritized roadmap that seeded the AI features built after joining.
Persona, prioritization matrix, phased roadmap, and revenue model for bridging a hardware product with a modern digital experience — built in 2 hours.
End-to-end product launch: research, OST, prompt engineering, shadow testing, and GTM — resulting in a 30% SDR efficiency gain and 90% note coverage.
70% of users abandoned enrollment at the prospectus wall. The fix came from a 30-year-old analog solution — and it cleared legal review in weeks, not months.
A tiered scheduling system for clinical reachouts — rubric informed by nurses and PTs, CRM + call automation, and an honest case for why the MVP isn't good enough but start there anyway.
A structured discovery and influence framework for guiding an enterprise client toward a generic API — without losing the relationship.
Side Projects
Most homeowners have no system for their home. Manuals are in a junk drawer or gone. Paint colors are a mystery. Repair history lives in email threads. When something breaks, they start from zero.
TheHomeApp.io is an AI-native home CRM. It gives homeowners one place to manage everything: DIY guidance, repair tracking, appliance manuals, paint colors, and a full record of the home's history — structured and searchable.
Building it as a solo PM applies a different kind of discipline. No team to delegate discovery to, no engineering resources to burn, no room for scope creep. Every feature decision is a direct trade-off against time and traction. It's the clearest signal I know for whether a product idea is real: will someone use it when you can't make them?
Most AI tutors just give the answer. That's the problem.
Built a local LLM running on a Raspberry Pi — Ollama with a quantized model, running entirely offline. No subscriptions, no API costs, no data leaving the house. My son uses it for school and homework questions.
The key product decision: the model is system-prompted to never give the complete answer. It asks questions back, surfaces the relevant concept, and makes him think through the last step himself. The constraint is the feature.
Running a quantized model on edge hardware also meant working through real AI tradeoffs firsthand — accuracy vs. speed, model size vs. available memory, response quality vs. latency. Those tradeoffs get concrete fast when you're debugging on a Pi.
Education
Skills & Expertise
Contact
Open to AI-focused PM roles — remote or Boston hybrid.
Boston, MA